Multi-agent framework for schema-guided reasoning and tool-augmented interaction with IFC models

Despite recent advances in large language model (LLM)-based agents, their applications to schema-guided reasoning and interaction with Building Information Modelling (BIM) data remain limited. This paper presents IFC-Agent, a tool-augmented multi-agent framework that enables natural language querying, reasoning, and modification of Industry Foundation Classes (IFC) models. The framework integrates schema-guided traversal with LLM-driven dynamic tool composition to achieve interpretable and scalable reasoning workflows. An adaptive dual-mode execution strategy combines asynchronous parallel execution with exploration–aggregation reasoning, while a dual-memory mechanism ensures operational efficiency and semantic consistency. A prototype system was developed using LangChain and validated on benchmark IFC queries. Case studies demonstrate strong performance in field querying, multi-hop reasoning, coordinate transformation, and IFC graph construction. Comparative evaluation with recent graph-based and LLM-assisted BIM frameworks highlights IFC-Agent's advantages in schema-guided reasoning and natural language interaction, establishing a practical foundation for explainable and automated BIM workflows. • Schema-guided multi-agent framework with dynamic tool composition on native IFC data. • Adaptive dual-mode execution addressing interpretability-scalability trade-off. • Dual-memory integrating procedural context and entity caches for cross-agent reuse. • LLMs leverage implicit schema awareness for autonomous multi-hop IFC traversal. • Tools extend execution capabilities but cannot replace LLM's reasoning capacity.