摘要

BIM 设计流程需与专业软件开展复杂多步交互，限制了大语言模型（LLM）的直接应用。本文提出基于大语言模型智能体的 BIM 设计助手，通过可靠的工具调用实现 BIM 设计与可视化任务的自然语言辅助。首先构建双层 Revit 接口函数库，打通大模型智能体与 BIM 软件，提供安全、可扩展、细粒度的模型操作能力。基于 ReAct 范式搭建智能体框架，融合对话历史管理、结构化提示工程与分步推理，支持长周期、多轮 BIM 任务。为评估该系统，构建面向智能体的 BIM 任务基准数据集，涵盖信息查询、模型可视化、构件修改与删除，并按难度划分任务等级。基于三类主流大语言模型开展实验，结果表明高性能通用大模型可在本框架下胜任 BIM 设计助手，任务准确率最高可达 90%，验证了大模型智能体驱动 BIM 设计辅助在实际场景中的可行性与可靠性。（AI自动翻译）

BIM design workflows involve complex, multi-step interactions with professional software, which limit the direct applicability of large language models (LLMs). This paper presents a BIM design assistant driven by LLM agents, enabling natural-language-based assistance for BIM design and visualization tasks through reliable tool invocation. First, a two-layer Revit interface function library is designed to bridge LLM agents and BIM software, providing secure, extensible, and fine-grained model manipulation capabilities. Based on the ReAct paradigm, an agent framework is developed that integrates dialogue history management, structured prompt engineering, and step-by-step reasoning to support long-horizon and multi-turn BIM tasks. To evaluate the proposed system, an agent-oriented BIM task benchmark is constructed, covering information querying, model visualization, component modification, and deletion, with tasks further categorized by difficulty. Experimental results with three representative LLMs show that high-performance general-purpose models can reliably act as BIM design assistants within the proposed framework, achieving up to 90% task accuracy. These results demonstrate the feasibility and reliability of LLM-agent-driven BIM design assistance in practical scenarios.

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The authors are grateful for the financial support received from the National Natural Science Foundation of China (No. 52378306).

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