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Save up to 90% on every LLM API call. Cache. Compress. Remember. The only local-first memory system that SKIPS repeat calls (100% saved), SHRINKS prompts 60-95%, and REMEMBERS everything — locally, for free.
SuperLocalMemory gives AI assistants persistent memory across sessions. v3.6 "Optimize" adds Cache + Compress + Align — a local-first LLM cost-reduction layer alongside SLM's existing memory engine. One install. One daemon. One UI.
Skip repeat LLM calls (100% saved), shrink prompts 60-95%, and stabilize prefixes for native provider KV-cache discounts. Local-first. Fail-open. AES-256-GCM encrypted. One command: slm wrap claude. Read more →
Tiered storage auto-classifies every memory as active, warm, cold, or archived. Graph pruning removes redundant connections. Optional acceleration backends (CozoDB, LanceDB) for graph + vector operations. Tested on 1.18 million real graph edges with under 2-second recall. Migration is automatic: pip install -U superlocalmemory && slm restart. View details →
One npm install and your AI memory is fully automatic:
- Auto-recall at session start — your context is there before you ask
- Auto-observe during coding — decisions and changes captured silently
- Auto-save at session end — full summary with git context
- Zero setup — hooks install themselves, no config needed
- Zero risk — every hook fails silently, never blocks your workflow
SLM learns from your usage patterns and gets smarter over time — at zero token cost. Every recall generates learning signals. After 20+ signals, the system starts optimizing retrieval for YOUR specific patterns. After 200+, a full ML model trains on your data. No other memory system learns without spending LLM tokens. Read more →
pip install -U superlocalmemory # Install or upgrade
slm restart # Restart daemon (auto-migrates)
slm wrap claude # Start saving on LLM costs
slm optimize savings --since 1 # See your savingsThat's it. Your AI now remembers you — and saves you money.
| Paper | Title | Link |
|---|---|---|
| Paper 3 (2026) | The Living Brain — Forgetting, Quantization, 7-Channel Retrieval | arXiv:2604.04514 |
| Paper 2 (2026) | Information-Geometric Foundations for Zero-LLM Agent Memory | arXiv:2603.14588 |
| Paper 1 (2026) | Trust & Behavioral Foundations for Multi-Agent Memory | arXiv:2603.02240 |
Part of Qualixar | Created by Varun Pratap Bhardwaj | AI Reliability Engineering
SuperLocalMemory V3 — Your AI Finally Remembers You. 100% local. 100% private. 100% free.
Part of Qualixar | Created by Varun Pratap Bhardwaj | GitHub
SuperLocalMemory V3
Getting Started
Reference
Architecture
Enterprise
Release Notes
V2 Documentation