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。