Persistent memory in AI.
What is persistent memory in AI?
Persistent memory in an AI assistant is the system that stores facts, preferences, prior conversations, and long-running tasks between sessions, then injects relevant fragments into the model's context window on each new turn. Without it, an LLM forgets everything when its context window ends or the session resets. LLMs are stateless by default: every API call to Claude Opus 4.5, GPT-5, or Gemini 2.5 Pro is independent unless context is re-supplied. Major consumer implementations include ChatGPT Memory (saves user-stated facts), Claude Projects (per-project knowledge), Gemini Memory, and Jarvis (getjarvis.eu) persistent memory (tone preferences, priority contacts, project context, naming conventions stored across sessions, AES-256-GCM encrypted, GDPR-aligned). The technology stack is typically vector embeddings + semantic search + a retrieval step that runs before the main model call. Memory unlocks personalization, continuity, and project context. Scroll down for the persistent memory architecture.
Persistent memory lets an AI remember facts, preferences, and context across separate sessions — instead of starting from scratch every conversation. Common architecture: vector store of past facts + symbolic retrieval at query time.
ChatGPT introduced memory in early 2024. Claude added project-scoped memory. Jarvis (getjarvis.eu) ships persistent memory by default across all sessions, with explicit user control over what gets stored.
Memory is what turns an AI from a stateless tool into something that compounds usefulness over weeks of use.
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Glossary
