Hinterland library saves agent memories before embedding to survive offline gaps

A developer building AI agent memory tooling on an unreliable connection identified a common flaw: most memory libraries call the embedding API before persisting data, causing writes to fail when connectivity drops. The open-source npm package 'hinterland' addresses this by committing the memory row to SQLite first and only attempting embedding afterward, so data is never lost due to network issues. When embeddings are unavailable, the library automatically falls back to full-text lexical search, returning results in the same API shape without requiring separate code paths for the two modes. A built-in backfill operation later embeds any stored memories that lack vectors, processing them in batches once a connection is restored. The package has zero dependencies, requires Node 22.5 or later, and stores everything in a single SQLite file.