Most people who add "memory" to an AI agent do the same thing: embed every message, throw the vector into Pinecone or pgvector, and call similarity_search at query time. It works for a demo. It falls apart in production, because nothing ever updates or deletes anything — the store only grows, and it fills with contradictions.
Say a user tells your agent "I live in Austin" in March and "I just moved to Denver" in July. A naive vector store keeps both. At retrieval time, both come back as top-k matches for "where do I live," and now your LLM is holding two contradictory facts with no signal about which one is current. This is the actual problem memory layers like Mem0 are built to solve, and the mechanism is more interesting than "vector DB with extra steps." Here's how it actually works.
The pipeline has four stages, not one
A naive RAG setup has one stage: embed and store. A real memory layer has four:
Extraction — turn a raw conversation turn into candidate facts
