# 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/2309.17288v1/

## Key points

- AutoAgents is a groundbreaking framework that addresses the limitations of existing multi-agent approaches
- It introduces adaptive agent generation and coordination
- Large language models (LLMs) often rely on predefined agents for handling simple tasks, limiting their adaptability
- AutoAgents dynamically generates and coordinates multiple specialized agents tailored to specific tasks
- The framework couples the relationship between tasks and roles, generating expert agents relevant to each task
- Expert agents collaborate with each other to efficiently accomplish complex tasks
- AutoAgents incorporates an observer role to reflect on plans and improve them iteratively
- Experiments show that AutoAgents outperforms existing multi-agent methods in generating coherent and accurate solutions
- Assigning different roles to different tasks and emphasizing team cooperation is crucial for tackling complex problems
- The repository for this project is publicly available on GitHub at https://github.com/LinkSoul-AI/AutoAgents.
- The authors of this innovative framework are Guangyao Chen, Siwei Dong, Yu Shu, Ge Zhang, Jaward Sesay, Börje F. Karlsson, Jie Fu, and Yemin Shi.

## Summary

AutoAgents is a groundbreaking framework that addresses the limitations of existing multi-agent approaches by introducing adaptive agent generation and coordination. While large language models (LLMs) have revolutionized automated task-solving with multi-agent systems, they often rely on predefined agents for handling simple tasks, which restricts their adaptability to different scenarios. In contrast, AutoAgents dynamically generates and coordinates multiple specialized agents to form an AI team tailored to specific tasks. The key innovation of AutoAgents lies in its ability to couple the relationship between tasks and roles. By analyzing the content of each task, the framework generates multiple required agents that possess expertise relevant to the task at hand. These expert agents then collaborate with each other to efficiently accomplish complex tasks. Additionally, AutoAgents incorporates an observer role into the framework, which reflects on designated plans and agents' responses and iteratively improves them. Experiments conducted on various benchmarks demonstrate that AutoAgents outperforms existing multi-agent methods in terms of generating more coherent and accurate solutions. This highlights the significance of assigning different roles to different tasks and emphasizes the importance of team cooperation when tackling complex problems. The repository for this project is publicly available on GitHub at https://github.com/LinkSoul-AI/AutoAgents. The authors of this innovative framework are Guangyao Chen, Siwei Dong, Yu Shu, Ge Zhang, Jaward Sesay, Börje F. Karlsson, Jie Fu, and Yemin Shi. Their work titled "AutoAgents: A Framework for Automatic Agent Generation" showcases how AutoAgents leverages LLMs to enable adaptive agent generation and coordination for enhanced multi-agent collaboration in diverse scenarios.