I'm the author. Engram is free and MIT licensed. Everything is plain Markdown on your disk. Search is BM25 over a SQLite FTS5 index that is rebuilt from the Markdown, so the index is disposable. If a local embedding model is already provisioned, cosine results are fused with the lexical ones by reciprocal rank fusion. Recall never downloads a model, so with no model present it simply stays lexical. The test suite enforces recall@5 of at least 90% across 20 seeded queries. That is a small set, so it works as a regression gate, not a benchmark. Walkthrough video above. Repo: https://github.com/utsapoddar/engram   submitted by   /u/utsapoddar [link]   [comments]