The booming success of LLMs initiates rapid development in LLM agents. Though
the foundation of an LLM agent is the generative model, it is critical to
devise the optimal reasoning strategies and agent architectures. Accordingly,
LLM agent research advances from the simple chain-of-thought prompting to more
complex ReAct and Reflection reasoning strategy; agent architecture also
evolves from single agent generation to multi-agent conversation, as well as
multi-LLM multi-agent group chat. However, with the existing intricate
frameworks and libraries, creating and evaluating new reasoning strategies and
agent architectures has become a complex challenge, which hinders research
investigation into LLM agents. Thus, we open-source a new AI agent library,
AgentLite, which simplifies this process by offering a lightweight,
user-friendly platform for innovating LLM agent reasoning, architectures, and
applications with ease. AgentLite is a task-oriented framework designed to
enhance the ability of agents to break down tasks and facilitate the
development of multi-agent systems. Furthermore, we introduce multiple
practical applications developed with AgentLite to demonstrate its convenience
and flexibility. Get started now at:
https://github.com/SalesforceAIResearch/AgentLite.

通过开源的 AgentLite AI 代理库，为 LLMs 代理的推理、架构和应用创新提供了轻量级、用户友好的平台，并通过多个实际应用程序展示了其便利性和灵活性。

AgentLite: 用于构建和推进面向任务的轻量级 LLM 代理系统的库

AgentLite: A Lightweight Library for Building and Advancing  Task-Oriented LLM Agent System

Autonomous agents have long been a prominent research topic in the academic
community. Previous research in this field often focuses on training agents
with limited knowledge within isolated environments, which diverges
significantly from the human learning processes, and thus makes the agents hard
to achieve human-like decisions. Recently, through the acquisition of vast
amounts of web knowledge, large language models (LLMs) have demonstrated
remarkable potential in achieving human-level intelligence. This has sparked an
upsurge in studies investigating autonomous agents based on LLMs. To harness
the full potential of LLMs, researchers have devised diverse agent
architectures tailored to different applications. In this paper, we present a
comprehensive survey of these studies, delivering a systematic review of the
field of autonomous agents from a holistic perspective. More specifically, our
focus lies in the construction of LLM-based agents, for which we propose a
unified framework that encompasses a majority of the previous work.
Additionally, we provide a summary of the various applications of LLM-based AI
agents in the domains of social science, natural science, and engineering.
Lastly, we discuss the commonly employed evaluation strategies for LLM-based AI
agents. Based on the previous studies, we also present several challenges and
future directions in this field. To keep track of this field and continuously
update our survey, we maintain a repository for the related references at
this https URL

通过对基于大语言模型的自主代理的综合调查，本文提出了一个统一框架来概括以往研究，并总结了在社会科学、自然科学和工程领域中应用大语言模型的人工智能代理的各种应用及评估策略。同时，我们还讨论了该领域的挑战和未来方向。

基于大型语言模型的自主代理的调查

A Survey on Large Language Model based Autonomous Agents

The massive successes of large language models (LLMs) encourage the emerging
exploration of LLM-augmented Autonomous Agents (LAAs). An LAA is able to
generate actions with its core LLM and interact with environments, which
facilitates the ability to resolve complex tasks by conditioning on past
interactions such as observations and actions. Since the investigation of LAA
is still very recent, limited explorations are available. Therefore, we provide
a comprehensive comparison of LAA in terms of both agent architectures and LLM
backbones. Additionally, we propose a new strategy to orchestrate multiple LAAs
such that each labor LAA focuses on one type of action, \textit{i.e.} BOLAA,
where a controller manages the communication among multiple agents. We conduct
simulations on both decision-making and multi-step reasoning environments,
which comprehensively justify the capacity of LAAs. Our performance results
provide quantitative suggestions for designing LAA architectures and the
optimal choice of LLMs, as well as the compatibility of both. We release our
implementation code of LAAs to the public at
https://github.com/salesforce/BOLAA.

大型语言模型（LLM）的巨大成功鼓舞了 LLM 增强型自主代理（LAA）的出现。LAA 能够使用其核心 LLM 生成动作并与环境交互，可以通过将过去的交互（包括观察和动作）作为条件来解决复杂任务的能力。我们提供了一个关于代理体系结构和 LLM 骨干的全面比较，同时提出了一种新的策略来编排多个 LAA，使得每个工作代理集中于一种类型的动作，即 BOLAA，其中一个控制器管理多个代理之间的通信。我们在决策制定和多步推理环境中进行了模拟，全面证明了 LAA 的能力。我们的性能结果量化地为设计 LAA 架构和 LLM 的最佳选择以及两者的兼容性提供了建议。我们将 LAA 的实现代码公开发布在 https://github.com/salesforce/BOLAA。