LLMs are stateless by design: everything they "know" about you lives in the context window and dies with the session. A memory layer is the piece of infrastructure that fixes this — a store outside the model where facts, decisions, preferences, and documents persist, and from which any model can recall them later. It's becoming as standard a dependency as a database.
What a memory layer actually does
- Stores durable records — "we deploy on Fridays", "the client prefers French" — written by the agent itself mid-conversation.
- Retrieves by meaning, not keywords: "what did we decide about auth?" finds the JWT decision even though no word matches.
- Deduplicates and versions, so ten similar saves don't become ten noisy memories.
- Serves every client from one place: your IDE agent, your chatbot, your scripts, your teammates' agents.
Memory infrastructure vs a memory feature
ChatGPT Memory and Claude Projects are memory features — useful, but locked inside one vendor's app. Infrastructure means an API: your product, your agents, and your tools all read and write the same store, whatever model is behind them. When you switch models, your memory comes with you.
How Kit for AI implements it
Kit for AI is a memory layer with the content pipeline attached: remember/recall/search as native MCP tools and REST endpoints, backed by knowledge bases with hybrid retrieval. The same account converts documents and ingests URLs into the same store — so the agent's memory and its reference library share one retrieval system, and answers cite their sources.
Add a memory layer to your agent in a couple of minutes — Start free and connect over MCP or REST.