From text to design: a framework to leverage LLM agents for automated CAD generation

Abstract

Design generation using traditional Computer-Aided Design (CAD) tools remains a labor-intensive and manual task. This paper introduces a framework for automating CAD geometry generation using Large Language Models (LLMs) with function calling and agent workflows. The framework enables both expert and novice designers to use textual prompts to automatically generate CAD code. We evaluate it with five LLMs and four agent workflows. The agent workflow incorporating automated visual feedback outperforms the others, especially with multimodal LLMs like ChatGPT-4o. A case study shows its use in topology optimization and additive manufacturing with minimal human input. Remaining challenges include limitations in spatial reasoning, prompt dependency, and workflow adaptability. Future work should focus on improving design-for-manufacturing capabilities, visual tools, and evaluation benchmarking.