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Intelligent agents stand out as a potential path toward artificial generalintelligence (AGI). Thus, researchers have dedicated significant effort todiverse implementations for them. Benefiting from recent progress in largelanguage models (LLMs), LLM-based agents that use universal natural language asan interface exhibit robust generalization capabilities across variousapplications -- from serving as autonomous general-purpose task assistants toapplications in coding, social, and economic domains, LLM-based agents offerextensive exploration opportunities. This paper surveys current research toprovide an in-depth overview of LLM-based intelligent agents withinsingle-agent and multi-agent systems. It covers their definitions, researchframeworks, and foundational components such as their composition, cognitiveand planning methods, tool utilization, and responses to environmentalfeedback. We also delve into the mechanisms of deploying LLM-based agents inmulti-agent systems, including multi-role collaboration, message passing, andstrategies to alleviate communication issues between agents. The discussionsalso shed light on popular datasets and application scenarios. We conclude byenvisioning prospects for LLM-based agents, considering the evolving landscapeof AI and natural language processing.

Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects