Analog circuit design remains a knowledge- and experience-intensive process that relies heavily on human intuition for topology generation and device parameter tuning. Existing LLM-based approaches typically depend on prompt-driven netlist generation or predefined topology templates, limiting their ability to satisfy complex specification requirements. We propose AnalogSAGE, an open-source self-evolving multi-agent framework that coordinates three-stage agent explorations through four stratified memory layers, enabling iterative refinement with simulation-grounded feedback. To support reproducibility and generality, we release the source code. Our benchmark spans ten specification-driven operational amplifier design problems of varying difficulty, enabling quantitative and cross-task comparison under identical conditions. Evaluated under the open-source SKY130 PDK with ngspice, AnalogSAGE achieves a 10$\times$ overall pass rate, a 48$\times$ Pass@1, and a 4$\times$ reduction in parameter search space compared with existing frameworks, demonstrating that stratified memory and grounded reasoning substantially enhance the reliability and autonomy of analog design automation in practice.

翻译：模拟电路设计仍然是一个知识和经验密集型的过程，高度依赖人类直觉进行拓扑生成和器件参数调优。现有基于大语言模型的方法通常依赖于提示驱动的网表生成或预定义的拓扑模板，限制了其满足复杂规格要求的能力。我们提出了AnalogSAGE，一个开源的自演化多智能体框架，它通过四个分层记忆层协调三阶段智能体探索，实现了基于仿真验证反馈的迭代优化。为支持可复现性和通用性，我们公开了源代码。我们的基准测试涵盖了十个不同难度的规格驱动运算放大器设计问题，可在相同条件下进行定量和跨任务比较。在开源SKY130 PDK和ngspice环境下评估，AnalogSAGE相比现有框架实现了10倍的整体通过率、48倍的首次通过率以及4倍的参数搜索空间缩减，证明分层记忆与基于事实的推理在实践中显著提升了模拟设计自动化的可靠性和自主性。