AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
- Qingyun Wu ,
- Gagan Bansal ,
- Jieyu Zhang ,
- Yiran Wu ,
- Beibin Li ,
- Erkang (Eric) Zhu ,
- Li Jiang ,
- Xiaoyun Zhang ,
- Shaokun Zhang ,
- Ahmed Awadallah ,
- Ryen W. White ,
- Doug Burger ,
- Chi Wang
Best Paper, LLM Agents Workshop ICLR'24
Télécharger BibTexWe 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.
What’s new in AutoGen?
Microsoft Research Forum | Episode 2 | March 5, 2024 Chi Wang discussed the latest updates on AutoGen – the multi-agent framework for next generation AI applications. This includes milestones achieved, community feedback, new exciting features, and ongoing research and challenges. See more at https://aka.ms/ResearchForum-Mar2024 (opens in new tab)
AutoGen Update: Complex Tasks and Agents
Adam Fourney discusses the effectiveness of using multiple agents, working together, to complete complex multi-step tasks. He will showcase their capability to outperform previous single-agent solutions on benchmarks like GAIA, utilizing customizable arrangements of agents that collaborate, reason, and utilize tools to achieve complex outcomes.