I got woken up by an alert call at 2 a.m. – the production chatbot had suddenly developed “amnesia.” It was completely unable to recall order information we had just discussed that morning. I scrambled to investigate and discovered the root cause: we had migrated the memory storage from Redis to PostgreSQL + pgvector. The migration script looked fine, but one historical memory entry’s vector index hadn’t been properly built, causing the similarity recall to return empty results. Before going live, I had manually spot-checked 20 conversations as a regression check – and you guessed it, the one broken entry was the 21st I missed. From that moment on, I resolved that regression testing for LLM memory storage must be automated, and every change must replay historical memories. This post shares how I built that automated regression suite with Pytest + Docker, along with two near-fatal pitfalls along the way.
Problem Breakdown: Why Memory Storage Breaks Silently
“LLM memory storage” is the mechanism that stores chat histories, user preferences, summary snippets, etc., so the bot can retrieve them later via vector similarity or keyword search and inject them into prompts as context. Common implementations include Redis (with RediSearch/Vector modules), Milvus, PostgreSQL + pgvector, or even local FAISS files.
