How I built a local memory layer for AI agents — and why vaults changed everything

Every serious project I've worked on with LLM agents hits the same wall eventually. The agent is smart. It reasons well. It follows instructions. But every new session it starts from zero, with no memory of what happened before, no context from previous runs, no knowledge it built over time. The naive fix is to stuff everything into the system prompt. It works until it doesn't — context windows fill up, costs spike, and you're manually curating what to include every time. The slightly less naive fix is RAG: retrieve relevant chunks before each call. Better, but now you have a retrieval problem on top of your agent problem, and a single shared vector store that every agent reads from indiscriminately. I built CtxVault because I wanted something different. Not just retrieval — infrastructure. The vault abstraction The core idea is simple: a vault is an isolated, self-contained memory unit. Its own directory, its own vector index, its own history. You can have one per agent, one per proje