Observable AI Memory: mem0, LangGraph, and Qdrant with Enterprise-Grade Telemetry
Most "AI memory" demos stop at memory.add() and memory.search(). That works on a laptop. It does not survive contact with production. The real questions are: When this recall is slow, which store is to blame? When a graph's spend triples overnight, which feature caused it? When a customer asks "what did your agent remember about me, and when?", can you answer from an audit log instead of a shrug?
TL;DR — This field report shows how to build an agent memory layer where every operation honors a contract: fail-open, PII-safe, and fully instrumented. Three stores (mem0, Qdrant, LangGraph) are funneled through single chokepoints, and each chokepoint fans out to five telemetry sinks. The result is a stack that answers the hard production questions without guesswork.