# AutoAgents: A Framework for Automatic Agent Generation

**Authors:** Guangyao Chen, Siwei Dong, Yu Shu, Ge Zhang, Jaward Sesay, Börje F. Karlsson, Jie Fu, Yemin Shi  
**arXiv:** [2309.17288](https://arxiv.org/abs/2309.17288)  
**Date:** 2023-09-29  
**Summary page:** https://www.summarizepaper.com/en/paper/2309.17288v3/

## Key points

- AutoAgents framework developed by Guangyao Chen, Siwei Dong, Yu Shu, Ge Zhang, Jaward Sesay, Börje F. Karlsson, Jie Fu, and Yemin Shi revolutionizes automated task-solving with multi-agent systems.
- Traditional approaches limited by predefined agents handling simple tasks; AutoAgents dynamically generates and coordinates specialized agents for specific tasks.
- Establishes link between tasks and roles by generating necessary agents based on task content; plans solutions using expert agents.
- Collaboration of multiple specialized agents enables efficient task completion; observer role evaluates plans and agent responses for continuous improvement.
- Outperforms existing multi-agent methods in producing coherent and accurate solutions based on experiments conducted on diverse benchmarks.
- Assigning distinct roles to different tasks and promoting team cooperation are essential components for tackling complex challenges effectively.
- Recognized at IJCAI 2024 for advancing AI capabilities; repository publicly available at https://github.com/Link-AGI/AutoAgents for further research and development.

## Summary

AutoAgents: A Revolutionary Framework for Multi-Agent Systems

AutoAgents is a groundbreaking framework developed by Guangyao Chen, Siwei Dong, Yu Shu, Ge Zhang, Jaward Sesay, Börje F. Karlsson, Jie Fu, and Yemin Shi that revolutionizes the field of automated task-solving with multi-agent systems. Traditional approaches in this domain have been limited by predefined agents handling simple tasks, which restricts adaptability to various scenarios. In response to this challenge, AutoAgents dynamically generates and coordinates multiple specialized agents to form an AI team tailored to specific tasks. This innovative framework establishes a crucial link between tasks and roles by generating the necessary agents based on task content and planning solutions using expert agents. The collaboration of multiple specialized agents enables efficient task completion while an observer role evaluates plans and agent responses for continuous improvement. Experiments conducted on diverse benchmarks have demonstrated that AutoAgents outperforms existing multi-agent methods by producing more coherent and accurate solutions. This success underscores the importance of assigning distinct roles to different tasks and promoting team cooperation as essential components for tackling complex challenges effectively. The authors' work on AutoAgents has been recognized at IJCAI 2024, highlighting its significance in advancing the capabilities of AI systems. The repository for this project is publicly available at https://github.com/Link-AGI/AutoAgents, providing a valuable resource for further research and development in this area.