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SMFS: Making Agentic Retrieval 55% Cheaper and More Accurate
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SMFS: Making Agentic Retrieval 55% Cheaper and More Accurate
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# 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.
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