Natural Language Information Retrieval from BIM Models: An LLM-Based Multi-Agent System Approach

While Building Information Models (BIM) effectively store building-related information, accessing it requires specialized software and expertise. Natural Language (NL) interfaces for BIM data retrieval can mitigate this challenge, but existing approaches are limited by rigid ontological frameworks or extensive pre-processing requirements. We present a Large Language Model-based agentic workflow that processes NL queries and automatically interacts with IFC-encoded BIM models without ontological or pre-processing constraints. In tests across architectural, structural, and MEP domains, our approach achieves 80% overall accuracy. We provide open access to IFC-Bench-v1, our evaluation dataset containing various queries, answers, and reference BIM models.