How I gave my $2/month AI agent a memory that persists across sessions

How I gave my $2/month AI agent a memory that persists across sessions Every AI agent demo looks magical. Then you close the tab, reopen it, and the agent has forgotten everything. This is the memory problem. And it's solvable without a vector database, without RAG pipelines, without spending $50/month on infrastructure. Here's exactly how I did it. The problem with stateless agents Most AI APIs are stateless by design. Each request is a clean slate. You send messages, you get a response, the context is gone. For a simple chatbot, that's fine. For an agent that's supposed to know you , it's a fatal flaw. My agent — Louie — runs 24/7. It checks in every hour. It tracks goals, decisions, and lessons learned over time. Without persistent memory, it would be useless. The architecture I built (and why it's simpler than you think) Here's the core insight: memory is just a JSON file that gets read at the start of every session and written at the end . No vector database. No embeddings. No RAG