@inproceedings{wu2024autogen,
author = {Wu, Qingyun and Bansal, Gagan and Zhang, Jieyu and Wu, Yiran and Li, Beibin and Zhu, Erkang (Eric) and Jiang, Li and Zhang, Xiaoyun and Zhang, Shaokun and Awadallah, Ahmed and White, Ryen W. and Burger, Doug and Wang, Chi},
title = {AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation},
booktitle = {COLM 2024},
year = {2024},
month = {August},
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 patterns 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, on domains ranging from mathematics and coding to question-answering, supply-chain optimization, online decision-making, and entertainment.},
url = {http://approjects.co.za/?big=en-us/research/publication/autogen-enabling-next-gen-llm-applications-via-multi-agent-conversation-framework/},
}