  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).
