This technical report presents AutoGen, a new framework that enables development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools. AutoGen's design offers multiple advantages: a) it gracefully navigates the strong but imperfect generation and reasoning abilities of these LLMs; b) it leverages human understanding and intelligence, while providing valuable automation through conversations between agents; c) it simplifies and unifies the implementation of complex LLM workflows as automated agent chats. We provide many diverse examples of how developers can easily use AutoGen to effectively solve tasks or build applications, ranging from coding, mathematics, operations research, entertainment, online decision-making, question answering, etc.

翻译：本技术报告介绍了AutoGen，一种基于多智能体相互对话协作解决任务的新型大语言模型（LLM）应用开发框架。AutoGen智能体具有可定制、可对话的特性，并能无缝融入人类参与。这些智能体可运行于结合LLM、人类输入及工具等不同模式。AutoGen的设计具备多重优势：a）它能妥善驾驭LLM强大但不完美的生成与推理能力；b）在通过智能体对话实现高效自动化的同时，充分发挥人类的理解与智能；c）将复杂LLM工作流简化为统一的自动化智能体对话实现。我们提供了丰富的应用实例，展示开发者如何便捷地使用AutoGen有效解决编程、数学、运筹学、娱乐、在线决策、问答等领域的任务或构建应用。