mlsanityData to 7 Oct 2026

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

arXiv 2309.07864 ↗First posted 14 Sep 2023cs.AI cs.CLcited by 1,422

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

For a long time, humanity has pursued artificial intelligence (AI) equivalent to or surpassing the human level, with AI agents considered a promising vehicle for this pursuit. AI agents are artificial entities that sense their environment, make decisions, and take actions. Many efforts have been made to develop intelligent agents, but they mainly focus on advancement in algorithms or training strategies to enhance specific capabilities or performance on particular tasks. Actually, what the community lacks is a general and powerful model to serve as a starting point for designing AI agents that can adapt to diverse scenarios. Due to the versatile 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 researchers have leveraged LLMs as the foundation to build AI agents and have achieved significant progress. In this paper, we perform a comprehensive survey on LLM-based agents. We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. Building upon this, we present a general framework for LLM-based agents, comprising three main components: brain, perception, and action, and the framework can be tailored for 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 from an agent society, and the insights they offer for human society. Finally, we discuss several key topics and open problems within the field. A repository for the related papers at https://github.com/WooooDyy/LLM-Agent-Paper-List.

Key facts. Cited by 1,422 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 347 of its 675 references that are in the map. First posted on arXiv 14 Sep 2023.

Builds on

347 of 675 references.

Closest ancestors · 20

Same direction, newest first

  1. 2023Cognitive Architectures for Language Agents5 Sep
  2. 2023A Survey on Large Language Model based Autonomous Agents22 Aug
  3. 2023ProAgent: Building Proactive Cooperative Agents with Large Language Models22 Aug
  4. 2023Is There Any Social Principle for LLM-Based Agents?22 Aug
  5. 2023AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors21 Aug
  6. 2023ExpeL: LLM Agents Are Experiential Learners20 Aug
  7. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate14 Aug
  8. 2023Gentopia: A Collaborative Platform for Tool-Augmented LLMs8 Aug
  9. 2023AgentBench: Evaluating LLMs as Agents7 Aug
  10. 2023TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage7 Aug
  11. 2023InterAct: Exploring the Potentials of ChatGPT as a Cooperative Agent3 Aug
  12. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework1 Aug
8 more
  1. 2023Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration11 Jul
  2. 2023Building Cooperative Embodied Agents Modularly with Large Language Models5 Jul
  3. 2023Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents5 Jun
  4. 2023SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks27 May
  5. 2023Augmenting Autotelic Agents with Large Language Models21 May
  6. 2023Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models26 Apr
  7. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society31 Mar
  8. 2023Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents3 Feb

Other references · 327

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
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  9. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  10. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
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  38. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906

50 most cited of 327 All 327 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 80; 2024: 438; 2025: 438; 2026: 466.

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  3. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 622
  4. 2025UI-TARS: Pioneering Automated GUI Interaction with Native Agentscited by 584
  5. 2025Why Do Multi-Agent LLM Systems Fail?cited by 559
  6. 2024BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructionscited by 527
  7. 2024Large Language Models: A Surveycited by 506
  8. 2024From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judgecited by 480
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  10. 2023AppAgent: Multimodal Agents as Smartphone Userscited by 407
  11. 2024Agentless: Demystifying LLM-based Software Engineering Agentscited by 400
  12. 2023MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoningcited by 311
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  7. 2025Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directionscited by 257
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  10. 2023FireAct: Toward Language Agent Fine-tuningcited by 247
  11. 2023Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology Viewcited by 221
  12. 2024Tool Learning with Large Language Models: A Surveycited by 221
  13. 2024Magentic-One: A Generalist Multi-Agent System for Solving Complex Taskscited by 221
  14. 2024Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalizationcited by 203
  15. 2024A Survey on Efficient Inference for Large Language Modelscited by 197
  16. 2024Trial and Error: Exploration-Based Trajectory Optimization for LLM Agentscited by 194
  17. 2025Large Language Model Agent: A Survey on Methodology, Applications and Challengescited by 194
  18. 2023Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Futurecited by 184
  19. 2025AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Societycited by 180
  20. 2024Security and Privacy Challenges of Large Language Models: A Surveycited by 179
  21. 2024Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AIcited by 175
  22. 2023Can LLM-Generated Misinformation Be Detected?cited by 173
  23. 2024Large Language Model-Based Agents for Software Engineering: A Surveycited by 173
  24. 2024Secrets of RLHF in Large Language Models Part II: Reward Modelingcited by 170
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  31. 2024LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Aheadcited by 155
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50 most cited of 1,422 All 1,422 (JSON)

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