Towards Data Science is a community-powered publication that showcases work in data science, machine learning and artificial intelligence. Every day newcomers, seasoned researchers and industry practitioners publish tutorials, research notes and real-world case studies that help the field move forward.

Towards Data Science

Building a custom LLM memory layer involves four key components: extraction (using DSPy to pull atomic facts from conversations), embedding (storing factoids in QDrant vector database with text-embedding-3-small), retrieval (using ReAct agents with tool-calling to fetch relevant memories), and maintenance (add/update/delete operations managed by an agent). The system treats memory as a context engineering problem, allowing chatbots to maintain persistent, per-user knowledge across sessions. Complete implementation available on GitHub with step-by-step code examples.

How to Build Your Own Custom LLM Memory Layer from Scratch

2) Memory Extraction with DSPy: From Transcript to Factoids