How to Build Your Own Custom LLM Memory Layer from Scratch

Large language models lack memory between sessions, treating users as strangers each time, which limits personalized chat experiences. This tutorial demonstrates building a custom memory system using four key components: extracting atomic facts from conversations with DSPy, embedding them in a vector database like QDrant, retrieving relevant memories through agent tool-calling, and maintaining the memory store by adding, updating, or deleting information based on new interactions.