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https://github.com/Paitesanshi/LLM-Agent-Survey


https://github.com/Paitesanshi/LLM-Agent-Survey

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README

        

# A Survey on LLM-based Autonomous Agents

![Growth Trend](assets/trend.png)

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.

**๐Ÿ“ This is the first released and published survey paper in the field of LLM-based autonomous agents.**

Paper link: [A Survey on Large Language Model based Autonomous Agents](https://journal.hep.com.cn/fcs/EN/10.1007/s11704-024-40231-1)

## Update Records
- ๐Ÿ”ฅ [25/3/2024] Our survey paper has been accepted by Frontiers of Computer Science, which is the first published survey paper in the field of LLM-based agents.

- ๐Ÿ”ฅ [9/28/2023] We have compiled and summarized papers related to LLM-based Agents that have been accepted by Neurips 2023 in the repository [LLM-Agent-Paper-Digest](https://github.com/XueyangFeng/LLM-Agent-Paper-Digest). This repository will continue to be updated with accepted agent-related papers in the future.

- ๐Ÿ”ฅ [9/8/2023] The second version of our survey has been released on arXiv.

Updated contents

- **๐Ÿ“š Additional References**
- We have added 31 new works until 9/1/2023 to make the survey more comprehensive and up-to-date.

- **๐Ÿ“Š 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.
![single-path and multi-path reasoning](assets/planning.png)
- **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.
![Capabilities Acquisition](assets/capability.png)

- **๐Ÿ” Optimized Classification System**
- We have slightly modified the classification system in our survey to make it more logical and organized.

- ๐Ÿ”ฅ [8/23/2023] The first version of our survey has been released on arXiv.

## Table of Content

- [๐Ÿค– Construction of LLM-based Autonomous Agent](#-construction-of-llm-based-autonomous-agent)
- [๐Ÿ“ Applications of LLM-based Autonomous Agent](#-applications-of-llm-based-autonomous-agent)
- [๐Ÿ“Š Evaluation on LLM-based Autonomous Agent](#-evaluation-on-llm-based-autonomous-agent)
- [๐ŸŒ More Comprehensive Summarization](#-more-comprehensive-summarization)
- [๐Ÿ‘จโ€๐Ÿ‘จโ€๐Ÿ‘งโ€๐Ÿ‘ฆ Maintainers](#-maintainers)
- [๐Ÿ“š Citation](#-citation)
- [๐Ÿ’ช How to Contribute](#-how-to-contribute)
- [๐Ÿซก Acknowledgement](#-acknowledgement)
- [๐Ÿ“ง Contact Us](#-contact-us)

## ๐Ÿค– Construction of LLM-based Autonomous Agent
![Architecture Design](assets/architecture-1.png)


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


LLM Planner
-
-
-
w/ feedback
w/o tools
Environment feedback
Paper
Code


MALLM
-
Read/Write
Hybrid
-
w/o tools
-
Paper
-


aiflows
-
Read/Write/
Reflection
Hybrid
w/ feedback
w/ tools
-
Paper
Code


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
-



Chameleon
-
-
-
w/o feedback
w/ tools
-
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


L2MAC
Handcrafting
Read/Write/
Reflection
Hybrid
w/ feedback
w/ tools
-
Paper
Code


LEO
-
-
-
w/ feedback
w/o tools
w/ fine-tuning
Paper
Code


JARVIS-1
-
Read/Write/
Reflection
Hybrid
w/ feedback
w/ tools
w/o fine-tuning
Paper
Code


CLOVA
-
Read/Write/
Reflection
Hybrid
w/ feedback
w/ tools
w/ fine-tuning
Paper
Code


LearnAct
-
-
-
w/ feedback
w/ tools
w/ fine-tuning
Paper
Code

* More papers can be found at [More comprehensive Summarization](#-more-comprehensive-summarization).
* CA means the strategy of model capability acquisition.

## ๐Ÿ“ 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


aiflows
-
-
CS & SE
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


L2MAC
-
-
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
-


MindAgent
-
-
Robotics & Embodied AI
Paper
-


LEO
-
-
Robotics & Embodied AI
Paper
-


JARVIS-1
-
-
Robotics & Embodied AI
Paper
-


CLOVA
-
-
CS&SE
Paper
-


AgentTrust
-
Social Simulation
✓
Paper
Code
-

* More papers can be found at [More comprehensive Summarization](#-more-comprehensive-summarization).

## ๐Ÿ“Š 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


Mind2Web
-
Environment Simulation;
Multi-task Evaluation
✓
06/2023
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
-


MintBench
-
Multi-task Evaluation
✓
Paper
Code


MindAgent
-
Environment Simulation;
Multi-task Evaluation
✓
Paper
-


JARVIS-1
-
Environment Simulation
-
Paper
-

* More papers can be found at [More comprehensive Summarization](#-more-comprehensive-summarization).


## ๐ŸŒ More Comprehensive Summarization

We are maintaining an [interactive table](https://abyssinian-molybdenum-f76.notion.site/237e9f7515d543c0922c74f4c3012a77?v=0a309e53d6454afcbe7a5a7e169be0f9&pvs=4) 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.
![Complete Table](assets/table.png)

## ๐Ÿ‘จโ€๐Ÿ‘จโ€๐Ÿ‘งโ€๐Ÿ‘ฆ Maintainers
- Lei Wang@[Paitesanshi](https://github.com/Paitesanshi)
- Chen Ma@[Uily](https://github.com/Yilu114)
- Xueyang Feng@[XueyangFeng](https://github.com/XueyangFeng)

## ๐Ÿ“š 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}
}
```

## ๐Ÿ’ช 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.

## ๐Ÿซก Acknowledgement
We thank the following people for their valuable suggestions and contributions to this survey:
- Yifan Song[@Yifan-Song793](https://github.com/Yifan-Song793)
- Qichen Zhao[@Andrewzh112](https://github.com/Andrewzh112)
- Ikko E. Ashimine[@eltociear](https://github.com/eltociear)

## ๐Ÿ“ง Contact Us
If you have any questions or suggestions, please contact us via:
- Email: [email protected], [email protected]