Mem0 makes long-term memory a managed agent infrastructure layer

Long-running agents need memory that can survive across sessions without turning every prompt into an ever-growing transcript or forcing each team to operate its own vector store. Mem0's platform overview describes a managed memory layer for AI apps and agents where teams send messages, the system extracts and stores relevant facts, and recall returns the most relevant memories at query time. The documentation also points to user, agent, run, and session memory types, memory operations such as add/search/update, and enterprise concerns like audit logs and workspace governance. For agent builders, the practical signal is to treat memory as an infrastructure boundary: evaluate how facts are extracted, scoped, updated, governed, and retrieved before giving agents durable personalization or operational state.