AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
| Autor: | Wu, Qingyun, Bansal, Gagan, Zhang, Jieyu, Wu, Yiran, Li, Beibin, Zhu, Erkang, Jiang, Li, Zhang, Xiaoyun, Zhang, Shaokun, Liu, Jiale, Awadallah, Ahmed Hassan, White, Ryen W, Burger, Doug, Wang, Chi |
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| Rok vydání: | 2023 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 43 pages (10 pages for the main text, 3 pages for references, and 30 pages for appendices) |
| Databáze: | arXiv |
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