Recent advancements in Large Language Models (LLMs) have catalyzed the development of sophisticated frameworks for developing LLM-based agents. However, the complexity of these frameworks r poses a hurdle for nuanced differentiation at a granular level, a critical aspect for enabling efficient implementations across different frameworks and fostering future research. Hence, the primary purpose of this survey is to facilitate a cohesive understanding of diverse recently proposed frameworks by identifying common workflows and reusable LLM-Profiled Components (LMPCs).

翻译：近年来，大型语言模型（LLMs）的进展推动了基于LLM的智能体开发框架的快速发展。然而，这些框架的复杂性使得在细粒度层面进行精确区分变得困难，而这一能力对于实现跨框架的高效部署及促进未来研究至关重要。因此，本综述旨在通过识别通用工作流和可复用的LLM-Profiled组件（LMPCs），为近期提出的各类框架建立统一的理解框架。