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Due to the powerful capabilities demonstrated by large language model (LLM),there has been a recent surge in efforts to integrate them with AI agents toenhance their performance. In this paper, we have explored the core differencesand characteristics between LLM-based AI agents and traditional AI agents.Specifically, we first compare the fundamental characteristics of these twotypes of agents, clarifying the significant advantages of LLM-based agents inhandling natural language, knowledge storage, and reasoning capabilities.Subsequently, we conducted an in-depth analysis of the key components of AIagents, including planning, memory, and tool use. Particularly, for the crucialcomponent of memory, this paper introduced an innovative classification scheme,not only departing from traditional classification methods but also providing afresh perspective on the design of an AI agent's memory system. We firmlybelieve that in-depth research and understanding of these core components willlay a solid foundation for the future advancement of AI agent technology. Atthe end of the paper, we provide directional suggestions for further researchin this field, with the hope of offering valuable insights to scholars andresearchers in the field.

An In-depth Survey of Large Language Model-based Artificial Intelligence Agents