AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
- Qingyun Wu ,
- Gagan Bansal ,
- Jieyu Zhang ,
- Yiran Wu ,
- Shaokun Zhang ,
- Erkang (Eric) Zhu ,
- Beibin Li ,
- Li Jiang ,
- Xiaoyun Zhang ,
- Chi Wang
MSR-TR-2023-33 |
Published by Microsoft
Best Paper Award, ICLR 2024
Download BibTexWe present AutoGen, an open-source framework that allows developers to build LLM applications via multiple agents that can 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.
Using AutoGen, developers can also flexibly define agent interaction behaviors. Both natural language and computer code can be used to program flexible conversation patterns for different applications. AutoGen serves as a generic infrastructure to build diverse applications of various complexities and LLM capacities. We provide many examples to build effective applications for domains ranging from mathematics, coding, question answering, operations research, online decision-making, entertainment, etc.
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AutoGen
September 25, 2023
Enable Next-Gen Large Language Model Applications. AutoGen is a framework that enables the 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 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.