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SuperLocalMemory V3.3

The first local-only AI memory to break 74% retrieval on LoCoMo. No cloud. No APIs. No data leaves your machine. Backed by 3 peer-reviewed research papers.

SuperLocalMemory gives AI assistants persistent memory across sessions. Install once, and your AI remembers your projects, preferences, decisions, and debugging history — forever.

Research Papers

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

V3.3.26: The Living Brain Evolves ✨ NEW

Memories now strengthen when used, fade when neglected, compress when idle, and consolidate into reusable patterns — all automatically, all locally. Trust-weighted forgetting, FRQAD mixed-precision metric, 7-channel retrieval, 3000+ tests. Read more →

V3.2: Associative Memory — The Living Brain

100x faster recall, automatic memory surfacing, associative retrieval (5th channel), temporal intelligence, sleep-time consolidation. Read more →

V3.1: Active Memory — Memory That Learns

SLM now 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 →

Quick Start

npm install -g superlocalmemory    # or: pip install superlocalmemory
slm setup                          # Choose mode A/B/C
slm warmup                        # Pre-download embedding model (optional)

That's it. Your AI now remembers you.

Three Operating Modes

Mode	What It Does	Cloud Required
A: Local Guardian	Zero cloud. Your data never leaves your machine. EU AI Act compliant. 74.8% on LoCoMo.	No
B: Smart Local	Local LLM via Ollama for answer synthesis. Still fully private.	No
C: Full Power	Cloud LLM for maximum accuracy (87.7% on LoCoMo).	Yes

Dashboard

Dashboard Demo — v3.4.4 "Neural Glass"

Your browser does not support the video tag. Watch the demo.

21-tab sidebar dashboard with light + dark theme. Knowledge Graph, Health Monitor, Entity Explorer, Mesh Peers, Ingestion Status, and more.

Key Features

• Works in 17+ IDEs — Claude Code, Cursor, VS Code, Windsurf, Gemini CLI, JetBrains, and more
• Dual Interface: MCP + CLI — MCP for IDEs, agent-native CLI (--json) for scripts, CI/CD, agent frameworks
• 4-channel retrieval — Semantic + keyword + entity graph + temporal for maximum accuracy
• Mathematical foundations — Fisher-Rao similarity, sheaf consistency, Langevin lifecycle
• Trust scoring — Bayesian trust per agent and per fact
• EU AI Act compliant — Mode A satisfies data sovereignty by architecture
• 85% open-domain — highest of any system evaluated, including cloud-powered ones
• 1400+ tests — production-grade reliability
• Multi-profile — isolated memory contexts for work, personal, clients

V3.2 Features

Page	What You'll Learn
V3.2 Overview	All v3.2 features, feature matrix by mode, migration guide
Auto-Invoke	Multi-signal scoring, FOK gating, ACT-R mode, contextual descriptions
Association Graph	SYNAPSE spreading activation, auto-linking, Hebbian strengthening
Temporal Intelligence	Bi-temporal validity, contradiction detection, historical queries
Consolidation	Core Memory blocks, 6-step consolidation cycle, triggers

Documentation

Page	What You'll Learn
Installation	Full install guide — npm, pip, git clone
Quick Start Tutorial	Step-by-step for new users and V2 upgraders
Getting Started	Install + first memory in 5 minutes
Modes Explained	A vs B vs C — which is right for you
CLI Reference	All 18 slm commands with --json docs
MCP Tools	All 24 MCP tools for IDE integration
IDE Setup	Per-IDE configuration guide
Migration from V2	Upgrade guide for existing users
Auto-Memory	How auto-capture and auto-recall work
Architecture Overview	How the system works
Mathematical Foundations	The math behind the memory
Compliance	EU AI Act, GDPR, retention policies
FAQ	Common questions answered

Research Papers

• V3 Paper: Information-Geometric Foundations for Agent Memory (arXiv) | Zenodo
• V2 Paper: Privacy-Preserving Multi-Agent Memory (arXiv)

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Part of Qualixar | Created by Varun Pratap Bhardwaj