  Intelligent agents stand out as a potential path toward artificial general
intelligence (AGI). Thus, researchers have dedicated significant effort to
diverse implementations for them. Benefiting from recent progress in large
language models (LLMs), LLM-based agents that use universal natural language as
an interface exhibit robust generalization capabilities across various
applications -- from serving as autonomous general-purpose task assistants to
applications in coding, social, and economic domains, LLM-based agents offer
extensive exploration opportunities. This paper surveys current research to
provide an in-depth overview of LLM-based intelligent agents within
single-agent and multi-agent systems. It covers their definitions, research
frameworks, and foundational components such as their composition, cognitive
and planning methods, tool utilization, and responses to environmental
feedback. We also delve into the mechanisms of deploying LLM-based agents in
multi-agent systems, including multi-role collaboration, message passing, and
strategies to alleviate communication issues between agents. The discussions
also shed light on popular datasets and application scenarios. We conclude by
envisioning prospects for LLM-based agents, considering the evolving landscape
of AI and natural language processing.
