A Coding Implementation to Design a Stateful Tutor Agent with Long-Term Memory, Semantic Recall, and Adaptive Practice Generation

This tutorial demonstrates building a stateful tutoring agent that learns continuously over time beyond brief conversations. The system stores user preferences, tracks weak learning areas, and selectively recalls relevant context using semantic search. By combining persistent storage, semantic retrieval, and adaptive prompting, developers can create personalized educational AI agents that improve through sustained interactions and adapt exercises to individual needs.