2023 AutogenEnablingNextGenLlmApplic
- (Wu, Bansal et al., 2023) ⇒ Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. (2023). “Autogen: Enabling Next-gen Llm Applications via Multi-agent Conversation Framework.” In: arXiv preprint arXiv:2308.08155. doi:10.48550/arXiv.2308.08155
Subject Headings: AutoGen, [[]].
Notes
Cited By
2023
- (Wang, Ma et al., 2023) ⇒ Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, and Ji-Rong Wen. (2023). “A Survey on Large Language Model based Autonomous Agents.” In: arXiv preprint arXiv:2308.11432. doi:10.48550/arXiv.2308.11432
- QUOTE: ... To promote the application of LLM-based autonomous agents, researchers have also introduced many open-source libraries, based on which the developers can quickly implement and evaluate agents according to their customized requirements (49, 47, 42, 44, 39, 40, 46, 16, 36, 43, 38, 125, 52, 45, 41, 50, 158). ...
Quotes
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.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023 AutogenEnablingNextGenLlmApplic | Chi Wang Qingyun Wu Gagan Bansal Jieyu Zhang Yiran Wu Shaokun Zhang Erkang Zhu Beibin Li Li Jiang Xiaoyun Zhang | Autogen: Enabling Next-gen Llm Applications via Multi-agent Conversation Framework | 10.48550/arXiv.2308.08155 | 2023 |