Mnemory prioritizes local-first semantic memory through hierarchical markdown and vector embeddings.

Mnemory couples local filesystem storage with semantic vector indexing. By parsing markdown files, it preserves human-readable hierarchies while generating embeddings for semantic search. This hybrid schema allows direct file manipulation without breaking the index. The architecture relies on local file watchers to trigger re-indexing pipelines. While this ensures data ownership and privacy, performance degrades when scaling to millions of documents. The local CPU and disk I/O bottlenecks limit its application to single-user contexts or small-team knowledge bases.