The Rise and Potential of Large Language Model Based Agents: A Survey

🔥 Must-read papers for LLM-based agents.

🏃 Coming soon: Add one-sentence intro to each paper.

🔔 News

• 🎉 [2025-09-10] Note！You can develop your custom environment to AgentGym and perform RL on it! The tutorial is here.
• 🍺 [2025-09-10] New paper is released on arXiv: AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning.
• 🚀 [2025-09-10] AgentGym-RL Framework released! We introduce the reinforcement learning (RL) version of AgentGym, enabling agents to learn directly from interactive environments: AgentGym-RL.
• 👀 [2025/09/03] AgentGym now provides an interactive frontend for visualization. Researchers can replay and inspect full trajectories, step through agent decision-making, and analyze model behaviors more easily.
• ☄️ [2024/06/07] AgentGym has been released for developing and evolving LLM-based agents across diverse environments!
  • Paper: AgentGym.
  • Project page: https://agentgym.github.io/.
  • Codes: Platform and Implementations.
  • Huggingface resources: AgentTraj-L, AgentEval, AgentEvol-7B.
• 🎉 [2024/05/02] R3 (Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning) was accepted by ICML 2024!
• 💫 [2024/02/08] New paper R3 on RL for LLM agent reasoning has been released! Paper: Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning. Codes: LLM-Reverse-Curriculum-RL.
• 🥳 [2023/09/20] This project has been listed on GitHub Trendings! It is a great honor!
• 💥 [2023/09/15] Our survey is released! See The Rise and Potential of Large Language Model Based Agents: A Survey for the paper!
• ✨ [2023/09/14] We create this repository to maintain a paper list on LLM-based agents. More papers are coming soon!

🌟 Introduction

For a long time, humanity has pursued artificial intelligence (AI) equivalent to or surpassing human level, with AI agents considered as a promising vehicle of this pursuit. AI agents are artificial entities that sense their environment, make decisions, and take actions.

Due to the versatile and remarkable capabilities they demonstrate, large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI), offering hope for building general AI agents. Many research efforts have leveraged LLMs as the foundation to build AI agents and have achieved significant progress.

In this repository, we provide a systematic and comprehensive survey on LLM-based agents, and list some must-read papers.

Specifically, we start by the general conceptual framework for LLM-based agents: comprising three main components: brain, perception, and action, and the framework can be tailored to suit different applications. Subsequently, we explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation. Following this, we delve into agent societies, exploring the behavior and personality of LLM-based agents, the social phenomena that emerge when they form societies, and the insights they offer for human society. Finally, we discuss a range of key topics and open problems within the fiel