We introduce ECNUClaw, an open-source framework for building learner-profiled intelligent study companions in K-12 education. The system constructs and maintains a five-dimension learner profile -- covering cognitive, behavioral, emotional, metacognitive, and contextual dimensions -- by extracting signals from student-companion dialogues at each turn. Profile updates feed directly into an adaptive strategy engine that adjusts the companion's guidance intensity, encouragement frequency, and Bloom's taxonomy scaffolding in real time. The framework design draws on three theoretical strands from the Chinese educational technology literature: Zhang's Digital Portrait Three-Layer Framework for learner assessment, the Education Brain model for educational system architecture, and the Human-AI Collaborative IQ concept for companion design philosophy. ECNUClaw is implemented in Python and supports seven Chinese LLM providers through a unified OpenAI-compatible adapter layer. We describe the system architecture, the profiling and adaptation mechanisms, and discuss limitations and next steps. The source code is available at https://github.com/bushushu2333/ECNUClaw.

翻译：我们提出了ECNUClaw，一个用于构建K-12教育中学习者画像型智能学习伴侣的开源框架。该系统通过从每轮学生-伴侣对话中提取信号，构建并维护一个五维学习者画像——涵盖认知、行为、情感、元认知和情境维度。画像更新直接反馈至自适应策略引擎，该引擎实时调整伴侣的指导强度、鼓励频率和布鲁姆认知分类支架。框架设计借鉴了中国教育技术文献中的三个理论分支：用于学习者评估的张氏数字画像三层框架、用于教育系统架构的教育大脑模型，以及用于伴侣设计哲学的人机协同智能概念。ECNUClaw采用Python实现，并通过统一兼容OpenAI的适配层支持七种中文大语言模型提供商。我们描述了系统架构、画像构建与自适应机制，并讨论了当前局限性与后续工作。源代码已开源至 https://github.com/bushushu2333/ECNUClaw。