Unpopular opinion: AI agents don't always need a Vector DB for project memory

A lot of AI coding tutorials seem to follow the same pattern: Want your AI agent to remember project context, coding guidelines, or architectural decisions? → Chunk the documents → Generate embeddings → Store them in a Vector DB → Build a RAG pipeline For massive codebases and large knowledge collections, that absolutely makes sense. But for many small and medium-sized coding projects, I've started wondering whether we're adding too much infrastructure to solve a relatively simple problem. The problem I see with Vector DBs for project memory 1. It's harder to inspect If an agent remembers a wrong architectural decision, where exactly did that memory come from? With embeddings and retrieval pipelines, debugging the memory itself can become another problem. 2. It adds another layer developers have to manage Developers already have Git and text editors. So why not make AI memory something developers can actually open, read, edit, diff, review and commit? 3. Not every project needs semantic retrieval If an agent is working on a 20–50 file project, do we really need to turn every piece of context into embeddings before the model can use it? Sometimes simply giving the model the relevant project context is enough. A different approach: treat AI memory like source code I've been experimenting with a much simpler approach for AI coding agents: Markdown + Git. The idea is straightforward: Flat-file storage: Project memory lives in an .ai-memory/ directory as Markdown. Human-readable: If the AI makes a wrong assumption, I can open the file in VS Code and fix it directly. Git-auditable: Every memory change becomes part of the Git history. git diff shows exactly what the agent learned or changed. No extra infrastructure: No database, embedding pipeline, or separate memory service is required for the basic case. The principle I'm exploring is: This doesn't mean Vector DBs are bad or unnecessary. They clearly have their place when the amount of information or retrieval requirements justify them. I'm more interested in the boundary between the two approaches. At what point does simple Git + Markdown memory stop being enough for an AI agent, and when does a Vector DB/RAG system actually become necessary?