Build an AI Agent That Actually Remembers

LLMs are stateless by design, which means AI agents forget everything between sessions. Most developers patch this with three separate systems: an in-memory cache, a relational database, and a vector store. This tutorial demonstrates a cleaner architecture using Oracle AI Database 23ai, a converged database that handles relational data, JSON, and vector embeddings in a single engine. Using LangChain as the orchestration layer, the tutorial walks through building an agent with three memory functions: save_memory (writes conversation history and embeddings), load_memory (fetches recent session context), and search_memory (runs semantic similarity search). All three memory types are handled with one connection string and one database, eliminating the coordination overhead of managing multiple systems.