When developers build AI coding assistants, they often hit a wall: the model forgets project quirks after a session ends. Traditional workarounds shove entire chat histories into large context windows, but token limits and cost make this brittle. The real bottleneck is the lack of a lightweight, auditable memory layer.

Enter memweave, an open‑source tool that stores memories as plain Markdown files on disk and indexes them with a local SQLite database. The index blends BM25 full‑text scoring with a lightweight vector search, delivering relevance without a remote vector store. Because the files are the source of truth, every change is simply a git commit.

Unlike vector databases that demand a separate service, memweave keeps everything local, eliminating credentials, server maintenance, and stale‑data drift. When a memory needs correction, a developer edits the Markdown line, commits, and the system rebuilds the index on demand. Auditing a session becomes as easy as running `git diff memory/`.

The result is a minimal‑infra, fully traceable agent memory that scales from a single developer’s notes to a team’s shared knowledge base. By treating memory as code, developers gain version control, reproducibility, and instant visibility into what the AI knows. For projects that value auditability over raw speed, memweave offers a practical alternative.