AutoGen: Enabling Next Gen LLM Applications via Multi Agent Conversation Framework

AutoGen is 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. The repository also provides 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.

In this video, I will talk about the following: What is AutoGen? How do agents work in AutoGen? How do conversations in AutoGen look like? What are some application scenarios and workflow patterns in AutoGen? Applications of AutoGen for Math Problem Solving, Multi-Agent Coding, Online Decision Making, and Dynamic Group Chat; Benefits and Guidelines for using AutoGen.

For more details, please look at https://arxiv.org/pdf/2308.08155.pdf and https://www.microsoft.com/en-us/resea...

Wu, Qingyun, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. "AutoGen: Enabling next-gen LLM applications via multi-agent conversation framework." arXiv preprint arXiv:2308.08155 (2023).