This paper introduces Atelier, a large language model (LLM)-based framework for analog circuit design to address the issues of data scarcity and the substantial domain-specific knowledge required in this field. Atelier integrates general-purpose LLMs with a high-quality, compact knowledge base to fulfill the considerable knowledge requirements of analog circuit design, obviating the need for extensive domain-specific training or fine-tuning. The knowledge base is meticulously curated to be task-oriented and encapsulates critical information from pertinent literature within user-defined templates, leveraging the LLMs’ capabilities in text comprehension and summarization. The framework comprises several LLM agents, structured in a graph-of-thoughts architecture, with each agent specialized in a distinct task in analog circuit design, including circuit analysis, topology selection, topology modification, parameter tuning, and design decision. This collaborative multi-agent system, enriched with access to the compact knowledge base and advanced mechanisms such as self-reflection, backtracking, and tool integration, automates the analog circuit design process. It significantly enhances design quality and efficiency while ensuring interpretability. Experimental results highlight Atelier’s superiority over state-of-the-art black-box methods, general-purpose LLMs, and LLM-based methods, demonstrating notable improvements in success rates, design quality, and runtime.