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

**Authors:** 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  
**arXiv:** [2503.21460](https://arxiv.org/abs/2503.21460)  
**Date:** 2025-03-27  
**Summary page:** https://www.summarizepaper.com/en/paper/2503.21460v1/

## Key points

- Large Language Model (LLM) agents have revolutionized the field of artificial intelligence with advanced reasoning capabilities and continuous learning.
- LLM agents like DeepResearch, DeepSearch, and Manus blur the line between assistants and collaborators by autonomously performing complex tasks and adapting to user needs.
- The transformation in AI is driven by three key developments: enhanced reasoning abilities of LLMs, advancements in tool manipulation and environmental interaction, and sophisticated memory architectures for experience accumulation.
- A methodology-centered taxonomy systematically deconstructs LLM agent systems by linking architectural foundations, collaboration mechanisms, and evolutionary pathways.
- The survey addresses evaluation methodologies, tool applications, practical challenges, and diverse application domains within the realm of LLM agents.
- Researchers can access a structured taxonomy for understanding LLM agents and identifying future research directions in this evolving field at https://github.com/luo-junyu/Awesome-Agent-Papers.

## Summary

The emergence of Large Language Model (LLM) agents has marked a pivotal era in the rapidly evolving field of artificial intelligence. These intelligent entities possess advanced reasoning capabilities and are capable of actively engaging with their environments through continuous learning and adaptation. Unlike traditional AI systems, LLM agents demonstrate generational advancements across multiple dimensions and have blurred the boundary between assistants and collaborators. Commercial LLM agent systems such as DeepResearch, DeepSearch, and Manus exemplify this paradigm shift by autonomously executing complex tasks that previously required human expertise while adapting to specific user needs. This transformation is driven by the convergence of three key developments: unprecedented reasoning capabilities of LLMs, advancements in tool manipulation and environmental interaction, and sophisticated memory architectures supporting longitudinal experience accumulation. To provide a comprehensive understanding of LLM agent systems, this survey systematically deconstructs them through a methodology-centered taxonomy that links architectural foundations, collaboration mechanisms, and evolutionary pathways. By revealing fundamental connections between agent design principles and their emergent behaviors in complex environments, this work offers a unified architectural perspective on how agents are constructed, collaborate, and evolve over time. Furthermore, the survey addresses evaluation methodologies, tool applications, practical challenges, and diverse application domains within the realm of LLM agents. By offering researchers a structured taxonomy for understanding LLM agents and identifying promising directions for future research in this rapidly evolving field. The collection is available at https://github.com/luo-junyu/Awesome-Agent-Papers.