Persistent memory is the ability of an AI system to **store, recall, and build on information across time and interactions**, instead of forgetting everything once a session ends. It means the AI can: - **Remember context**: carry knowledge of past conversations, documents, or tasks into the future. - **Maintain continuity**: recall decisions, preferences, and history over days, weeks, or years. - **Preserve identity**: keep a consistent “personality” or behavior, so interacting with it feels stable and reliable. ### Why It’s Hard Most AI today runs statelessly: each query is handled in isolation, with memory simulated by tools like: - **Vector databases (Vector DBs):** store embeddings of text to retrieve relevant snippets. - **RAG (Retrieval-Augmented Generation):** fetch past info when needed. - **Longer context windows:** let models read more text at once. These help, but they don’t equal true persistent memory. They provide retrieval, not real understanding or continuity. ### Everyday Analogy Persistent memory for AI would be like having a friend who actually remembers your past conversations, preferences, and inside jokes—rather than someone with amnesia who needs to be reminded every time you talk.