SMFS: making agentic retrieval 55% cheaper AND more accurate

SMFS.ai, or Supermemory Filesystem, is presented as a FUSE-based filesystem designed for AI agents that combines conventional file navigation with semantic retrieval, automatically maintained profile files, OCR-based indexing of multimodal content, and enhanced grep-like commands. Its developers argue that conventional agentic search preserves file structure but requires many exploratory reads, while retrieval-augmented search can find relevant information semantically but often returns context-poor excerpts; SMFS aims to combine semantic discovery with structured file-based exploration. To evaluate this approach, the team created xAFS, an open benchmark containing coherent conversational and document datasets that scale to 10,000 files and test multi-hop, temporal, and other nontrivial queries. Reported results claim that at 10,000 files, SMFS achieved 81% accuracy versus 69% for a standard filesystem agent while reducing overall evaluation costs by 55% and using roughly 54% fewer tokens, with public evaluation runs and a technical report available through the project’s website.

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