We present SDialog, an MIT-licensed open-source Python toolkit that unifies dialog generation, evaluation and mechanistic interpretability into a single end-to-end framework for building and analyzing LLM-based conversational agents. Built around a standardized Dialog representation, SDialog provides: (1) persona-driven multi-agent simulation with composable orchestration for controlled, synthetic dialog generation, (2) comprehensive evaluation combining linguistic metrics, LLM-as-a-judge and functional correctness validators, (3) mechanistic interpretability tools for activation inspection and steering via feature ablation and induction, and (4) audio generation with full acoustic simulation including 3D room modeling and microphone effects. The toolkit integrates with all major LLM backends, enabling mixed-backend experiments under a unified API. By coupling generation, evaluation, and interpretability in a dialog-centric architecture, SDialog enables researchers to build, benchmark and understand conversational systems more systematically.

翻译：本文介绍SDialog，这是一个采用MIT许可证的开源Python工具包，它将对话生成、评估与机制可解释性统一到一个端到端的框架中，用于构建和分析基于大语言模型（LLM）的对话智能体。SDialog围绕标准化的对话表示构建，提供以下功能：（1）基于角色的多智能体模拟，支持可组合编排以实现受控的合成对话生成；（2）综合评估体系，结合语言指标、LLM-as-a-judge和功能正确性验证器；（3）用于激活检查与特征消融/诱导调控的机制可解释性工具；（4）包含完整声学模拟（含3D房间建模与麦克风效果）的音频生成功能。该工具包兼容所有主流LLM后端，支持通过统一API进行混合后端实验。通过将生成、评估与可解释性在对话中心架构中耦合，SDialog使研究人员能够更系统地构建、评估和理解对话系统。