AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
- URL: http://arxiv.org/abs/2308.08155v2
- Date: Tue, 3 Oct 2023 20:47:10 GMT
- Title: AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
- Authors: Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang
Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan
Awadallah, Ryen W White, Doug Burger, and Chi Wang
- Abstract summary: AutoGen is 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.
- Score: 61.455159391215915
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: AutoGen is 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. Empirical studies demonstrate the effectiveness of the framework in
many example applications, with domains ranging from mathematics, coding,
question answering, operations research, online decision-making, entertainment,
etc.
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