Large Language Model Agent: A Survey on Methodology, Applications and Challenges
luojunyu
Abstract
The era of intelligent agents is upon us, driven by revolutionary advancements in large language models. Large Language Model (LLM) agents, with goal-driven behaviors and dynamic adaptation capabilities, potentially represent a critical pathway toward artificial general intelligence. This survey systematically deconstructs LLM agent systems through a methodology-centered taxonomy, linking architectural foundations, collaboration mechanisms, and evolutionary pathways. We unify fragmented research threads by revealing fundamental connections between agent design principles and their emergent behaviors in complex environments. Our work provides a unified architectural perspective, examining how agents are constructed, how they collaborate, and how they evolve over time, while also addressing evaluation methodologies, tool applications, practical challenges, and diverse application domains. By surveying the latest developments in this rapidly evolving field, we offer researchers a structured taxonomy for understanding LLM agents and identify promising directions for future research. The collection is available at https://github.com/luo-junyu/Awesome-Agent-Papers.
Community
Introducing the most up-to-date survey on LLM Agents!
This latest comprehensive survey examines the rapidly evolving field of Large Language Model (LLM) agents, synthesizing insights from recent research papers to provide an up-to-date structured taxonomy. As LLM agents increasingly demonstrate potential for goal-driven behaviors and dynamic adaptation capabilities, understanding their technical foundations becomes essential for researchers and practitioners alike.
Survey Overview:
- Methodological Framework: Analyzes agent systems through the dimensions of construction, collaboration, and evolution
- Application Domains: Reviews the most recent implementations across scientific research, medicine, gaming, social sciences, and productivity tools
- Critical Analysis: Discusses current challenges in security, privacy, and ethical considerations facing LLM agent deployment
- Research Directions: Identifies promising paths for future work in agent development and evaluation
This work aims to connect architectural foundations with emergent behaviors, providing researchers with a systematic approach to understanding the current state of LLM agents and identifying potential areas for advancement.
The collection of papers referenced in this survey is available at the accompanying GitHub repository.
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems (2025)
- AI Agents: Evolution, Architecture, and Real-World Applications (2025)
- CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language Models (2025)
- Position: Towards a Responsible LLM-empowered Multi-Agent Systems (2025)
- D-CIPHER: Dynamic Collaborative Intelligent Agents with Planning and Heterogeneous Execution for Enhanced Reasoning in Offensive Security (2025)
- Robotouille: An Asynchronous Planning Benchmark for LLM Agents (2025)
- Generative Multi-Agent Collaboration in Embodied AI: A Systematic Review (2025)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment:
@librarian-bot
recommend
Models citing this paper 0
No model linking this paper
Datasets citing this paper 0
No dataset linking this paper