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Large Language Model Agent: A Survey on Methodology, Applications and Challenges

Paper · 2025 · Junyu Luo, Weizhi Zhang, Ye Yuan et al.

Summary

Work on LLM-based agents has grown quickly but remains scattered. Existing reviews tend to cover one slice, such as games, deployment settings, multimodality or security, or they give broad overviews without a method-level breakdown. Luo et al. address this with a survey built around how agents are engineered. Its core is a "Build–Collaborate–Evolve" framework:

  • Build: how a single agent is assembled.
  • Collaborate: how several agents coordinate.
  • Evolve: how agents improve through self-learning, interaction or outside knowledge.

Around this core the authors also cover benchmarks and tooling, real-world risks (security, privacy, social impact) and application areas from the natural sciences to medicine and gaming. The result is a resource rather than an experiment: a structured map that places hundreds of papers in one taxonomy, plus a curated, maintained paper list on GitHub (Awesome-Agent-Papers). It is useful as an orientation guide for anyone designing or comparing agent systems. It also makes explicit how single-agent design choices connect to multi-agent behavior, which earlier surveys usually treated separately.

Key points

  • Agent construction rests on four interlocking components: profile definition, memory, planning and action execution. Profiles can be human-curated (e.g., CAMEL, AutoGen, MetaGPT, ChatDev) or generated dynamically (e.g., Generative Agents, RecAgent, DSPy).
  • Memory is split into three types:
    • short-term memory (ReAct, Graph of Thoughts);
    • long-term memory (Voyager, Reflexion, MemGPT);
    • retrieval used as memory (RAG, GraphRAG, DeepRAG).
  • Collaboration is classified as centralized (Coscientist, MetaGPT), decentralized (MedAgents, multi-agent debate) or hybrid (KnowAgent, TextGrad).
  • Evolution covers three routes: self-improvement (SELF-REFINE, STaR, Self-Rewarding), multi-agent co-evolution, and learning from external resources (CRITIC).
  • Real-world risks are grouped into:
    • agent-centric and data-centric security;
    • privacy issues such as memorization and IP exploitation;
    • ethics.
  • The survey (arXiv:2503.21460v1, March 2025) cites over 300 references.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

📚 Shelves 2The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 works

Cite

Cite as Junyu Luo, Weizhi Zhang, Ye Yuan, Yusheng Zhao, Junwei Yang, Yiyang Gu, Bohan Wu, Binqi Chen, Ziyue Qiao, Qingqing Long, Rongcheng Tu, Xiao Luo, Wei Ju, Zhiping Xiao, Yifan Wang, Meng Xiao, Chenwu Liu, Jingyang Yuan, Shichang Zhang, Yiqiao Jin, Fan Zhang, Xian Wu, Hanqing Zhao, Dacheng Tao, Philip S. Yu and Ming Zhang. 2025. Large Language Model Agent: A Survey on Methodology, Applications and Challenges. arXiv:2503.21460.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2503.21460 · arXiv 2503.21460

@misc{luo2025large,
  title = {{Large Language Model Agent: A Survey on Methodology, Applications and Challenges}},
  author = {Junyu Luo, Weizhi Zhang, Ye Yuan, Yusheng Zhao, Junwei Yang, Yiyang Gu, Bohan Wu, Binqi Chen, Ziyue Qiao, Qingqing Long, Rongcheng Tu, Xiao Luo, Wei Ju, Zhiping Xiao, Yifan Wang, Meng Xiao, Chenwu Liu, Jingyang Yuan, Shichang Zhang, Yiqiao Jin, Fan Zhang, Xian Wu, Hanqing Zhao, Dacheng Tao, Philip S. Yu and Ming Zhang},
  year = {2025},
  doi = {10.48550/arxiv.2503.21460},
  eprint = {2503.21460},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2503.21460}
}

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