The capabilities of a single large language model (LLM) agent for solving a complex task are limited. Connecting multiple LLM agents to a network can effectively improve overall performance. However, building an LLM agent network (LAN) requires a substantial amount of time and effort. In this paper, we introduce EasyLAN, a human-computer collaborative tool that helps developers construct LANs. EasyLAN initially generates a LAN containing only one agent based on the description of the desired task. Subsequently, EasyLAN leverages a few training examples to update the LAN. For each example, EasyLAN models the gap between the output and the ground truth and identifies the causes of the errors. These errors are addressed through carefully designed strategies. Users can intervene in EasyLAN's workflow or directly modify the LAN. Eventually, the LAN evolves from a single agent to a network of LLM agents. The experimental results indicate that developers can rapidly construct LANs with good performance.

翻译：单个大语言模型（LLM）智能体在解决复杂任务时能力有限。将多个LLM智能体连接成网络可有效提升整体性能。然而，构建LLM智能体网络（LAN）需要耗费大量时间和精力。本文介绍EasyLAN——一种协助开发者构建LAN的人机协作工具。EasyLAN首先根据目标任务描述生成仅含一个智能体的LAN，随后利用少量训练示例更新该LAN。针对每个示例，EasyLAN通过建模输出与真实值之间的差距来识别错误成因，并采用精心设计的策略处理这些错误。用户可干预EasyLAN的工作流程或直接修改LAN。最终，LAN将从单个智能体演化为LLM智能体网络。实验结果表明，开发者能够快速构建具有良好性能的LAN。