AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
Abstract
We present AutoGen, an open-source framework that allows developers to build LLM applications by composing multiple agents to converse with each other to accomplish tasks. AutoGen agents are customizable, conversable, and can operate in various modes that employ combinations of LLMs, human inputs, and tools. It also enables developers to create flexible agent behaviors and conversation pat- terns for different applications using both natural language and code. AutoGen serves as a generic infrastructure and is widely used by AI practitioners and researchers to build diverse applications of various complexities and LLM capacities. We demonstrate the framework’s effectiveness with several pilot applications, with domains ranging from mathematics and coding to question-answering, supply-chain optimization, online decision-making, and entertainment
Cite
Text
Wu et al. "AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation." ICLR 2024 Workshops: LLMAgents, 2024.Markdown
[Wu et al. "AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation." ICLR 2024 Workshops: LLMAgents, 2024.](https://mlanthology.org/iclrw/2024/wu2024iclrw-autogen/)BibTeX
@inproceedings{wu2024iclrw-autogen,
title = {{AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation}},
author = {Wu, Qingyun and Bansal, Gagan and Zhang, Jieyu and Wu, Yiran and Li, Beibin and Zhu, Erkang and Jiang, Li and Zhang, Xiaoyun and Zhang, Shaokun and Liu, Jiale and Awadallah, Ahmed Hassan and White, Ryen W and Burger, Doug and Wang, Chi},
booktitle = {ICLR 2024 Workshops: LLMAgents},
year = {2024},
url = {https://mlanthology.org/iclrw/2024/wu2024iclrw-autogen/}
}