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.

翻译：智能代理被视为通往通用人工智能（AGI）的潜在路径。因此，研究者们为其实施多样化的实现方式投入了大量精力。得益于大语言模型（LLM）的最新进展，以通用自然语言为接口的基于LLM的代理，在从充当自主通用任务助手到编程、社交和经济领域的各类应用中，展现出强大的泛化能力。基于LLM的代理提供了广泛的探索机会。本文对现有研究进行调研，对单代理和多代理系统中基于LLM的智能代理进行了深入概述。内容涵盖其定义、研究框架以及基本组件，如构成、认知与规划方法、工具利用及对环境反馈的响应。我们还深入探讨了在多代理系统中部署基于LLM的代理的机制，包括多角色协作、消息传递以及缓解代理间通信问题的策略。讨论还阐明了流行的数据集与应用场景。最后，结合人工智能和自然语言处理发展的演变趋势，对基于LLM的代理的前景进行了展望。