For a long time, humanity has pursued artificial intelligence (AI) equivalent
to or surpassing the human level, with AI agents considered a promising vehicle
for this pursuit. AI agents are artificial entities that sense their
environment, make decisions, and take actions. Many efforts have been made to
develop intelligent AI agents since the mid-20th century. However, these
efforts have mainly focused on advancement in algorithms or training strategies
to enhance specific capabilities or performance on particular tasks. Actually,
what the community lacks is a sufficiently general and powerful model to serve
as a starting point for designing AI agents that can adapt to diverse
scenarios. Due to the versatile and remarkable capabilities they demonstrate,
large language models (LLMs) are regarded as potential sparks for Artificial
General Intelligence (AGI), offering hope for building general AI agents. Many
research efforts have leveraged LLMs as the foundation to build AI agents and
have achieved significant progress. We start by tracing the concept of agents
from its philosophical origins to its development in AI, and explain why LLMs
are suitable foundations for AI agents. Building upon this, we present a
conceptual framework for LLM-based agents, comprising three main components:
brain, perception, and action, and the framework can be tailored to suit
different applications. Subsequently, we explore the extensive applications of
LLM-based agents in three aspects: single-agent scenarios, multi-agent
scenarios, and human-agent cooperation. Following this, we delve into agent
societies, exploring the behavior and personality of LLM-based agents, the
social phenomena that emerge when they form societies, and the insights they
offer for human society. Finally, we discuss a range of key topics and open
problems within the field.

人工智能代理是一个前景光明的领域，利用大型语言模型作为基础构建通用人工智能代理带来了显著的进展，其在单一代理场景、多代理场景以及人机合作中的广泛应用展示了卓越的潜力，并为人类社会提供了有价值的见解。