mlsanityData to 7 Oct 2026

A Survey on Large Language Model based Autonomous Agents

arXiv 2308.11432 ↗First posted 22 Aug 2023cs.AI cs.CLcited by 2,203

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

Abstract (arXiv)

Autonomous agents have long been a prominent research focus in both academic and industry communities. Previous research in this field often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and thus makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of web knowledge, large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This has sparked an upsurge in studies investigating LLM-based autonomous agents. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of LLM-based autonomous agents from a holistic perspective. More specifically, we first discuss the construction of LLM-based autonomous agents, for which we propose a unified framework that encompasses a majority of the previous work. Then, we present a comprehensive overview of the diverse applications of LLM-based autonomous agents in the fields of social science, natural science, and engineering. Finally, we delve into the evaluation strategies commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future directions in this field. To keep track of this field and continuously update our survey, we maintain a repository of relevant references at https://github.com/Paitesanshi/LLM-Agent-Survey.

Key facts. Cited by 2,203 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 158 of its 208 references that are in the map. First posted on arXiv 22 Aug 2023.

Builds on

158 of 208 references.

Closest ancestors · 18

Same direction, newest first

  1. 2023AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors21 Aug
  2. 2023ExpeL: LLM Agents Are Experiential Learners20 Aug
  3. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate14 Aug
  4. 2023BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents11 Aug
  5. 2023Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges10 Aug
  6. 2023Gentopia: A Collaborative Platform for Tool-Augmented LLMs8 Aug
  7. 2023AgentBench: Evaluating LLMs as Agents7 Aug
  8. 2023TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage7 Aug
  9. 2023InterAct: Exploring the Potentials of ChatGPT as a Cooperative Agent3 Aug
  10. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework1 Aug
  11. 2023The SocialAI School: Insights from Developmental Psychology Towards Artificial Socio-Cultural Agents15 Jul
  12. 2023Building Cooperative Embodied Agents Modularly with Large Language Models5 Jul
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  1. 2023SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks27 May
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  4. 2023Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models26 Apr
  5. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society31 Mar
  6. 2023Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents3 Feb

Other references · 140

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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  3. 2017Proximal Policy Optimization Algorithmscited by 14,441
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  9. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  10. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
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  4. 2015Continuous control with deep reinforcement learningcited by 3,279
  5. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  6. 2023A Survey of Large Language Modelscited by 2,537
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  28. 2022ProgPrompt: Generating Situated Robot Task Plans using Large Language Modelscited by 598
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  33. 2023MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Actioncited by 511
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  35. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 496
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  37. 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 409
  38. 2023Augmented Language Models: a Surveycited by 398

50 most cited of 140 All 140 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 95; 2024: 495; 2025: 694; 2026: 919.

2,203 papers.

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  10. 2024From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judgecited by 480
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50 most cited of 2,203 All 2,203 (JSON)

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