Every "give your agent memory" tutorial I've read reaches for the same stack: chunk your docs, embed them, throw the vectors in a database, do cosine similarity at query time. So when I needed my coding agent to search through indexed tool output, git logs, and fetched docs without dumping raw text into the model's context window, I assumed I'd be standing up a vector store too.
I didn't. I used SQLite's FTS5 full-text search instead, and for this specific job it's not a compromise — it's the better tool.
What the problem actually was
The tool I built (context-mode, for routing large command output and API responses out of the model's context) needs to answer queries like:
"failing tests"
