Why I Replaced My AI Agent's Vector Database With grep

Every AI agent tutorial starts the same way: set up your LLM, configure your vector database, implement RAG. We followed the script. Then we deleted it all. The Promise The standard pitch: embeddings capture semantic meaning, vector search finds relevant context, RAG grounds your agent in reality. For enterprise search across millions of documents, this is genuinely powerful. But we were building a personal AI agent — one that runs 24/7 on a single machine, maintains its own memory, and assists one person. Our entire knowledge base? Under 1,000 documents. What We Actually Needed Here's what our agent does with memory: Saves observations and decisions as Markdown files Searches past experiences when facing similar situations Maintains topic-specific knowledge files Tracks tasks and goals in structured text The key insight: at personal scale, the retrieval problem isn't semantic — it's organizational. You don't need to find documents that are "similar in meaning." You need to find the do