Every AI agent project I start ends up with the same boilerplate: chunk docs, pick an embedding model, set up a vector store, write retrieval logic, wire it into a custom tool.

It works, but it's plumbing — and it needs to be rebuilt for every new agent or runtime.

The idea I'm exploring: mount a drive at /drive/ with two directories:

- /drive/files/ — actual documents (PDF, code, markdown, etc.)

- /drive/search/ — virtual directory where the filename IS the semantic query

So instead of a custom RAG tool, the agent just does: cat "/drive/search/refund policy enterprise customers"

Any runtime that reads files works immediately. No integration code. Context cost drops ~10-20x since you get a relevant chunk, not the full document.

Under the hood: markitdown for conversion, sqlite-vss for vector search, and a virtual filesystem layer to wire it all together.

Before I build this: is this a solved problem I'm not aware of? Does the filesystem interface make sense, or am I overcomplicating something simpler?

GitHub / implementation details coming if there's interest.

If there's enough interest, I'll build this in public and share updates. Follow along: @r_klosowski on X