A Survey of LLM-based Agents: Theories, Technologies, Applications and Suggestions
Xiaofei Dong, Xue‐Qiang Zhang, Wen Bu, Dan Zhang, Feng Cao
Abstract
AI Agent has presented potential towards Artificial General Intelligence (AGI), which is expected to autonomously perceive the environments, make decisions and take actions. However, most of existing AI agents tend to train in confined environments with limited knowledge, yielding sub-optimal performance. Benefiting from the remarkable progress of large language models (LLMs), diverse LLM-based agents emerge. These agents employ LLM as the central brain to perceive, plan, and memorize, etc, which exhibit human-level intelligence across multifarious applications and obtain satisfactory performance. In this paper, we propose a survey of LLM-based agents from the perspective of theories, technologies, applications and suggestions, respectively. Specifically, we first deliver a recapitulative review of the theory foundation, which includes Large Language Models, Chain of Thought and AI Alignment, Retrieval-Augmented Generation, Embodied AI, etc; With this, we then present the key technologies, comprising four critical components: Perception, Planning, Memory and Action; Subsequently, we briefly explore some domain-related and evaluation applications; Finally, we provide pertinent suggestions based on the observations of significant challenges for LLM-based agents.