SMFS: Making Agentic Retrieval 55% Cheaper and More Accurate

Source: https://blog.supermemory.ai/smfs-making-agentic-retrieval-55-cheaper-and-more-accurate/

Overview

SMFS.ai (Supermemory Filesystem) is a purpose-built filesystem designed for AI agents. Combines agentic search with semantic retrieval to optimize cost and accuracy.

Core Features

• FUSE-powered filesystem with instant loading
• Auto-generated profiles (/profile.md) that update dynamically
• Multi-modal support via OCR (images to searchable text)
• Enhanced grep: semantic search alongside traditional string matching

The Problem

**Agentic search** provides control and structure but struggles at scale -- agents must manually traverse directories and maintain context across operations.

**Semantic RAG retrieval** efficiently finds content but strips context -- returns isolated chunks without surrounding information or file relationships.

Developers were forced to choose between control (agentic) or reach (semantic).

The Solution: xAFS Benchmark

Created a realistic evaluation framework featuring:

• Mixed conversational and document data
• Scalable file counts up to 10,000
• Multi-hop and temporal reasoning queries
• Files exceeding 10,000 tokens each

Performance Results

• **Accuracy**: At 10,000 files, SMFS maintained 81% accuracy vs 69% for baseline filesystems
• **Cost reduction**: 55% cheaper overall ($946 vs $2,103 across evaluations)
• **Token efficiency**: 53.8% fewer tokens used; 53.1% fewer per correct answer
• **Per-query savings**: One corpus showed $4.71 cost vs $20.95 for baseline
• **Claude specifically**: -66% tokens, -60% tool calls with improved accuracy

Technical Approach

Hybrid methodology: 1. Semantic search lands on specific file paths 2. Agent-controlled navigation through surrounding context 3. Targeted grep operations within identified subtrees

Agents trust their starting points while maintaining control over exploration.