dogank01 / llm-agent-survey

A Survey on LLM-based Autonomous Agents

Autonomous agents are designed to achieve specific objectives through self-guided instructions. With the emergence and growth of large language models (LLMs), there is a growing trend in utilizing LLMs as fundamental controllers for these autonomous agents. While previous studies in this field have achieved remarkable successes, they remain independent proposals with little effort devoted to a systematic analysis. To bridge this gap, we conduct a comprehensive survey study, focusing on the construction, application, and evaluation of LLM-based autonomous agents. In particular, we first explore the essential components of an AI agent, including a profile module, a memory module, a planning module, and an action module. We further investigate the application of LLM-based autonomous agents in the domains of natural sciences, social sciences, and engineering. Subsequently, we delve into a discussion of the evaluation strategies employed in this field, encompassing both subjective and objective methods. Our survey aims to serve as a resource for researchers and practitioners, providing insights, related references, and continuous updates on this exciting and rapidly evolving field.

Paper link: A Survey on Large Language Model based Autonomous Agents

Update Records

• 🔥 [9/8/2023] The second version of our survey has been released on arXiv.

  Updated contents

  • 📚 Additional References

    • We have added 31 new works until 9/1/2023 to make the survey more comprehensive and up-to-date.

  • 📊 New Figures

    • Figure 3: This is a new figure illustrating the differences and similarities between various planning approaches. This helps in gaining a clearer understanding of the comparisons between different planning methods.
    • Figure 4: This is a new figure that describes the evolutionary path of model capability acquisition from the "Machine Learning era" to the "Large Language Model era" and then to the "Agent era." Specifically, a new concept, "mechanism engineering," has been introduced, which, along with "parameter learning" and "prompt engineering," forms part of this evolutionary path.

  • 🔍 Optimized Classification System

    • We have slightly modified the classification system in our survey to make it more logical and organized.

• 🔥 [8/23/2023] The first version of our survey has been released on arXiv.
  

Table of Content

• 🤖 Construction of LLM-based Autonomous Agent
• 📍 Applications of LLM-based Autonomous Agent
• 📊 Evaluation on LLM-based Autonomous Agent
• 🌐 More Comprehensive Summarization
• 👨‍👨‍👧‍👦 Maintainers
• 📚 Citation
• 💪 How to Contribute
• 🫡 Acknowledgement
• 📧 Contact Us

🤖 Construction of LLM-based Autonomous Agent

Model	Profile	Memory		Planning	Action	CA	Paper	Code
		Operation	Structure
WebGPT	-	-	-	-	w/ tools	w/ fine-tuning	Paper	-
SayCan	-	-	-	w/o feedback	w/o tools	w/o fine-tuning	Paper	Code
MRKL	-	-	-	w/o feedback	w/ tools	-	Paper	-
Inner Monologue	-	-	-	w/ feedback	w/o tools	w/o fine-tuning	Paper	Code
Social Simulacra	GPT-Generated	-	-	-	w/o tools	-	Paper	-
ReAct	-	-	-	w/ feedback	w/ tools	w/ fine-tuning	Paper	Code
MALLM	-	Read/Write	Hybrid	-	w/o tools	-	Paper	-
DEPS	-	-	-	w/ feedback	w/o tools	w/o fine-tuning	Paper	Code
Toolformer	-	-	-	w/o feedback	w/ tools	w/ fine-tuning	Paper	Code
Reflexion	-	Read/Write/ Reflection	Hybrid	w/ feedback	w/o tools	w/o fine-tuning	Paper	Code
CAMEL	Handcrafting & GPT-Generated	-	-	w/ feedback	w/o tools	-	Paper	Code
API-Bank	-	-	-	w/ feedback	w/ tools	w/o fine-tuning	Paper	Code
ViperGPT	-	-	-	-	w/ tools	-	Paper	Code
HuggingGPT	-	-	Unified	w/o feedback	w/ tools	-	Paper	Code
Generative Agents	Handcrafting	Read/Write/ Reflection	Hybrid	w/ feedback	w/o tools	-	Paper	Code
LLM+P	-	-	-	w/o feedback	w/o tools	-	Paper	-
ChemCrow	-	-	-	w/ feedback	w/ tools	-	Paper	Code
OpenAGI	-	-	-	w/ feedback	w/ tools	w/ fine-tuning	Paper	Code
AutoGPT	-	Read/Write	Hybrid	w/ feedback	w/ tools	w/o fine-tuning	-	Code
SCM	-	Read/Write	Hybrid	-	w/o tools	-	Paper	Code
Socially Alignment	-	Read/Write	Hybrid	-	w/o tools	Example	Paper	Code
GITM	-	Read/Write/ Reflection	Hybrid	w/ feedback	w/o tools	w/ fine-tuning	Paper	Code
Voyager	-	Read/Write/ Reflection	Hybrid	w/ feedback	w/o tools	w/o fine-tuning	Paper	Code
Introspective Tips	-	-	-	w/ feedback	w/o tools	w/o fine-tuning	Paper	-
RET-LLM	-	Read/Write	Hybrid	-	w/o tools	w/ fine-tuning	Paper	-
ChatDB	-	Read/Write	Hybrid	w/ feedback	w/ tools	-	Paper	-
S3	Dataset alignment	Read/Write/ Reflection	Hybrid	-	w/o tools	w/ fine-tuning	Paper	-
ChatDev	Handcrafting	Read/Write/ Reflection	Hybrid	w/ feedback	w/o tools	w/o fine-tuning	Paper	Code
ToolLLM	-	-	-	w/ feedback	w/ tools	w/ fine-tuning	Paper	Code
MemoryBank	-	Read/Write/ Reflection	Hybrid	-	w/o tools	-	Paper	Code
MetaGPT	Handcrafting	Read/Write/ Reflection	Hybrid	w/ feedback	w/ tools	-	Paper	Code

• More papers can be found at More comprehensive Summarization.
• CA means the strategy of model capability acquisition.

📍 Applications of LLM-based Autonomous Agent

Title	Social Science	Natural Science	Engineering	Paper	Code
Drori et al.	-	Science Education	-	Paper	-
SayCan	-	-	Robotics & Embodied AI	Paper	Code
Inner monologue	-	-	Robotics & Embodied AI	Paper	Code
Language-Planners	-	-	Robotics & Embodied AI	Paper	Code
Social Simulacra	Social Simulation	-	-	Paper	-
TE	Psychology	-	-	Paper	Code
Out of One	Political Science and Economy	-	-	Paper	-
LIBRO	CS&SE	-	-	Paper	-
Blind Judgement	Jurisprudence	-	-	Paper	-
Horton	Political Science and Economy	-	-	Paper	-
DECKARD	-	-	Robotics & Embodied AI	Paper	Code
Planner-Actor-Reporter	-	-	Robotics & Embodied AI	Paper	-
DEPS	-	-	Robotics & Embodied AI	Paper	-
RCI	-	-	CS&SE	Paper	Code
Generative Agents	Social Simulation	-	-	Paper	Code
SCG	-	-	CS&SE	Paper	-
IGLU	-	-	Civil Engineering	Paper	-
IELLM	-	-	Industrial Automation	Paper	-
ChemCrow	-	Document and Data Management; Documentation, Data Managent; Science Education	-	Paper	-
Boiko et al.	-	Document and Data Management; Documentation, Data Managent; Science Education	-	Paper	-
GPT4IA	-	-	Industrial Automation	Paper	Code
Self-collaboration	-	-	CS&SE	Paper	-
E2WM	-	-	Robotics & Embodied AI	Paper	Code
Akata et al.	Psychology	-	-	Paper	-
Ziems et al.	Psychology; Political Science and Economy; Research Assistant	-	-	Paper	-
AgentVerse	Social Simulation	-	-	Paper	Code
SmolModels	-	-	CS&SE	-	Code
TidyBot	-	-	Robotics & Embodied AI	Paper	Code
PET	-	-	Robotics & Embodied AI	Paper	-
Voyager	-	-	Robotics & Embodied AI	Paper	Code
GITM	-	-	Robotics & Embodied AI	Paper	Code
NLSOM	-	Science Education	-	Paper	-
LLM4RL	-	-	Robotics & Embodied AI	Paper	-
GPT Engineer	-	-	CS&SE	-	Code
Grossman et al.	-	Experiment Assistant; Science Education	-	Paper	-
SQL-PALM	-	-	CS&SE	Paper	-
REMEMBER	-	-	Robotics & Embodied AI	Paper	-
DemoGPT	-	-	CS&SE	-	Code
Chatlaw	Jurisprudence	-	-	Paper	Code
RestGPT	-	-	CS&SE	Paper	Code
Dialogue shaping	-	-	Robotics & Embodied AI	Paper	-
TaPA	-	-	Robotics & Embodied AI	Paper	-
Ma et al.	Psychology	-	-	Paper	-
Math Agents	-	Science Education	-	Paper	-
SocialAI School	Social Simulation	-	-	Paper	-
Unified Agent	-	-	Robotics & Embodied AI	Paper	-
Wiliams et al.	Social Simulation	-	-	Paper	-
Li et al.	Social Simulation	-	-	Paper	-
S3	Social Simulation	-	-	Paper	-
Dialogue Shaping	-	-	Robotics & Embodied AI	Paper	-
RoCo	-	-	Robotics & Embodied AI	Paper	Code
Sayplan	-	-	Robotics & Embodied AI	Paper	Code
ToolLLM	-	-	CS&SE	Paper	Code
ChatDEV	-	-	CS&SE	Paper	-
Chao et al.	Social Simulation	-	-	Paper	-
AgentSims	Social Simulation	-	-	Paper	Code
ChatMOF	-	Document and Data Management; Science Education	-	Paper	-
MetaGPT	-	-	CS&SE	Paper	Code
Codehelp	-	Science Education	CS&SE	Paper	-
AutoGen	-	Science Education	-	Paper	-
RAH	-	-	CS&SE	Paper	-
DB-GPT	-	-	CS&SE	Paper	Code
RecMind	-	-	CS&SE	Paper	-
ChatEDA	-	-	CS&SE	Paper	-
InteRecAgent	-	-	CS&SE	Paper	-
PentestGPT	-	-	CS&SE	Paper	-
Codehelp	-	-	CS&SE	Paper	-
ProAgent	-	-	Robotics & Embodied AI	Paper	-

• More papers can be found at More comprehensive Summarization.

📊 Evaluation on LLM-based Autonomous Agent

Model	Subjective	Objective	Benchmark	Paper	Code
WebShop	-	Environment Simulation; Multi-task Evaluation	✓	Paper	Code
Social Simulacra	Human Annotation	Social Evaluation	-	Paper	-
TE	-	Social Evaluation	-	Paper	Code
LIBRO	-	Software Testing	-	Paper	-
ReAct	-	Environment Simulation	✓	Paper	Code
Out of One, Many	Turing Test	Social Evaluation; Multi-task Evaluation	-	Paper	-
DEPS	-	Environment Simulation	✓	Paper	-
Jalil et al.	-	Software Testing	-	Paper	Code
Reflexion	-	Environment Simulation; Multi-task Evaluation	-	Paper	Code
IGLU	-	Environment Simulation	✓	Paper	-
Generative Agents	Human Annoation; Turing Test	-	-	Paper	Code
ToolBench	Human Annoation	Multi-task Evalution	✓	Paper	Code
GITM	-	Environment Simulation	✓	Paper	Code
Two-Failures	-	Multi-task Evalution	-	Paper	-
Voyager	-	Environment Simulation	✓	Paper	Code
SocKET	-	Social Evaluation; Multi-task Evaluation	✓	Paper	-
Mobile-Env	-	Environment Simulation; Multi-task Evaluation	✓	Paper	Code
Clembench	-	Environment Simulation; Multi-task Evaluation	✓	Paper	Code
Dialop	-	Social Evaluation	✓	Paper	Code
Feldt et al.	-	Software Testing	-	Paper	-
CO-LLM	Human Annoation	Environment Simulation	-	Paper	Code
Tachikuma	Human Annoation	Environment Simulation	✓	Paper	-
WebArena	-	Environment Simulation	✓	Paper	Code
RocoBench	-	Environment Simulation; Social Evaluation; Multi-task Evaluation	✓	Paper	Code
AgentSims	-	Social Evaluation	-	Paper	Code
AgentBench	-	Multi-task Evaluation	✓	Paper	Code
BOLAA	-	Environment Simulation; Multi-task Evaluation; Software Testing	✓	Paper	Code
Gentopia	-	Isolated Reasoning; Multi-task Evaluation	✓	Paper	Code
EmotionBench	Human Annotation	-	✓	Paper	Code
PTB	-	Software Testing	✓	Paper	-

• More papers can be found at More comprehensive Summarization.

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🌐 More Comprehensive Summarization

We are maintaining an interactive table that contains more comprehensive papers related to LLM-based Agents. This table includes details such as tags, authors, publication date, and more, allowing you to sort, filter, and find the papers of interest to you.

👨‍👨‍👧‍👦 Maintainers

• Lei Wang@Paitesanshi
• Chen Ma@Uily
• Xueyang Feng@XueyangFeng

📚 Citation

If you find this survey useful, please cite our paper:

@misc{wang2023survey,
      title={A Survey on Large Language Model based Autonomous Agents}, 
      author={Lei Wang and Chen Ma and Xueyang Feng and Zeyu Zhang and Hao Yang and Jingsen Zhang and Zhiyuan Chen and Jiakai Tang and Xu Chen and Yankai Lin and Wayne Xin Zhao and Zhewei Wei and Ji-Rong Wen},
      year={2023},
      eprint={2308.11432},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}

💪 How to Contribute

If you have a paper or are aware of relevant research that should be incorporated, please contribute via pull requests, issues, email, or other suitable methods.

🫡 Acknowledgement

We thank the following people for their valuable suggestions and contributions to this survey:

• Yifan Song@Yifan-Song793
• Qichen Zhao@Andrewzh112
• Ikko E. Ashimine@eltociear

📧 Contact Us

If you have any questions or suggestions, please contact us via:

• Email: [email protected], [email protected]