SuperLocalMemory V3.3

Every other AI forgets. Yours won't.
Infinite memory for Claude Code, Cursor, Windsurf & 17+ AI tools.

v3.3.6 — Install once. Every session remembers the last. Automatically.

+16pp vs Mem0 (zero cloud)  ·  85% Open-Domain (best of any system)  ·  EU AI Act Ready

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Why SuperLocalMemory?

Every major AI memory system — Mem0, Zep, Letta, EverMemOS — sends your data to cloud LLMs for core operations. That means latency on every query, cost on every interaction, and after August 2, 2026, a compliance problem under the EU AI Act.

SuperLocalMemory V3 takes a different approach: mathematics instead of cloud compute. Three techniques from differential geometry, algebraic topology, and stochastic analysis replace the work that other systems need LLMs to do — similarity scoring, contradiction detection, and lifecycle management. The result is an agent memory that runs entirely on your machine, on CPU, with no API keys, and still outperforms funded alternatives.

The numbers (evaluated on LoCoMo, the standard long-conversation memory benchmark):

System	Score	Cloud Required	Open Source	Funding
EverMemOS	92.3%	Yes	No	—
Hindsight	89.6%	Yes	No	—
SLM V3 Mode C	87.7%	Optional	Yes (MIT)	$0
Zep v3	85.2%	Yes	Deprecated	$35M
SLM V3 Mode A	74.8%	No	Yes (MIT)	$0
Mem0	64.2%	Yes	Partial	$24M

Mode A scores 74.8% with zero cloud dependency — outperforming Mem0 by 16 percentage points without a single API call. On open-domain questions, Mode A scores 85.0% — the highest of any system in the evaluation, including cloud-powered ones. Mode C reaches 87.7%, matching enterprise cloud systems.

Mathematical layers contribute +12.7 percentage points on average across 6 conversations (n=832 questions), with up to +19.9pp on the most challenging dialogues. This isn't more compute — it's better math.

Upgrading from V2 (2.8.6)? V3 is a complete architectural reinvention — new mathematical engine, new retrieval pipeline, new storage schema. Your existing data is preserved but requires migration. After installing V3, run slm migrate to upgrade your data. Read the Migration Guide before upgrading. Backup is created automatically.

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What's New in V3.3 — The Living Brain Evolves

V3.3 gives your memory a lifecycle. Memories strengthen when used, fade when neglected, compress when idle, and consolidate into reusable patterns — all automatically, all locally. Your agent gets smarter the longer it runs.

Features at a Glance

• Adaptive Memory Lifecycle — memories naturally strengthen with use and fade when neglected. No manual cleanup, no hardcoded TTLs.
• Smart Compression — embedding precision adapts to memory importance. Low-priority memories compress up to 32x. High-value memories stay full-resolution.
• Cognitive Consolidation — the system automatically extracts patterns from clusters of related memories. One decision referenced 50 times becomes one reusable insight.
• Pattern Learning — auto-learned soft prompts injected into your agent's context at session start. The system teaches itself what matters to you.
• Hopfield Retrieval (6th Channel) — vague or partial queries now complete themselves. Ask half a question, get the whole answer.
• Process Health — orphaned SLM processes detected and cleaned automatically. No more zombie workers eating RAM.

New CLI Commands

# Run a memory lifecycle review — strengthens active memories, archives neglected ones
slm decay

# Run smart compression — adapts embedding precision to memory importance
slm quantize

# Extract reusable patterns from memory clusters
slm consolidate --cognitive

# View auto-learned patterns that get injected into agent context
slm soft-prompts

# Clean up orphaned SLM processes
slm reap

New MCP Tools

Tool	Description
forget	Programmatic memory archival via lifecycle rules
quantize	Trigger smart compression on demand
consolidate_cognitive	Extract and store patterns from memory clusters
get_soft_prompts	Retrieve auto-learned patterns for context injection
reap_processes	Clean orphaned SLM processes
get_retention_stats	Memory lifecycle analytics

Mode A/B Memory Improvements

Metric	V3.2	V3.3	Change
RAM usage (Mode A/B)	~4GB	~40MB	100x reduction
Retrieval channels	5	6	+Hopfield completion
MCP tools	29	35	+6 new
CLI commands	21	26	+5 new
Dashboard tabs	20	23	+3 new
API endpoints	9	16	+7 new

Embedding migration happens automatically when you switch modes — no manual steps needed.

Dashboard

Three new tabs: Memory Lifecycle (retention curves, decay stats), Compression (storage savings, precision distribution), and Patterns (auto-learned soft prompts, consolidation history). Seven new API endpoints power the new views.

Enable V3.3 Features

All new features default OFF. Zero breaking changes. Opt in when ready:

# Turn on adaptive memory lifecycle
slm config set lifecycle.enabled true

# Turn on smart compression
slm config set quantization.enabled true

# Turn on cognitive consolidation
slm config set consolidation.cognitive.enabled true

# Turn on pattern learning (soft prompts)
slm config set soft_prompts.enabled true

# Turn on Hopfield retrieval (6th channel)
slm config set retrieval.hopfield.enabled true

# Or enable everything at once
slm config set v33_features.all true

Fully backward compatible. All existing MCP tools, CLI commands, and configs work unchanged. New tables are created automatically on first run. No migration needed.

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What's New in V3.2 — The Living Brain (click to expand)

100x faster recall (<10ms at 10K facts), automatic memory surfacing, associative retrieval (5th channel), temporal intelligence with bi-temporal validity, sleep-time consolidation, and core memory blocks. All features default OFF, zero breaking changes.

Metric	V3.0	V3.2	Change
Recall latency (10K facts)	~500ms	<10ms	100x faster
Retrieval channels	4	5	+spreading activation
MCP tools	24	29	+5 new
DB tables	9	18	+9 new

Enable with slm config set v32_features.all true. See the V3.2 Overview wiki page for details.

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Quick Start

Install via npm (recommended)

npm install -g superlocalmemory
slm setup     # Choose mode (A/B/C)
slm doctor    # Verify everything is working
slm warmup    # Pre-download embedding model (~500MB, optional)

Install via pip

pip install superlocalmemory

First Use

slm remember "Alice works at Google as a Staff Engineer"
slm recall "What does Alice do?"
slm status

MCP Integration (Claude, Cursor, Windsurf, VS Code, etc.)

{
  "mcpServers": {
    "superlocalmemory": {
      "command": "slm",
      "args": ["mcp"]
    }
  }
}

35 MCP tools + 7 resources available. Works with Claude Code, Cursor, Windsurf, VS Code Copilot, Continue, Cody, ChatGPT Desktop, Gemini CLI, JetBrains, Zed, and 17+ AI tools. V3.3: Adaptive lifecycle, smart compression, and pattern learning.

Dual Interface: MCP + CLI

SLM works everywhere -- from IDEs to CI pipelines to Docker containers. The only AI memory system with both MCP and agent-native CLI.

Need	Use	Example
IDE integration	MCP	Auto-configured for 17+ IDEs via slm connect
Shell scripts	CLI + --json	slm recall "auth" --json | jq '.data.results[0]'
CI/CD pipelines	CLI + --json	slm remember "deployed v2.1" --json in GitHub Actions
Agent frameworks	CLI + --json	OpenClaw, Codex, Goose, nanobot
Human use	CLI	slm recall "auth" (readable text output)

Agent-native JSON output on every command:

# Human-readable (default)
slm recall "database schema"
#   1. [0.87] Database uses PostgreSQL 16 on port 5432...

# Agent-native JSON
slm recall "database schema" --json
# {"success": true, "command": "recall", "version": "3.0.22", "data": {"results": [...]}}

All --json responses follow a consistent envelope with success, command, version, data, and next_actions for agent guidance.

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Three Operating Modes

Mode	What	Cloud?	EU AI Act	Best For
A	Local Guardian	None	Compliant	Privacy-first, air-gapped, enterprise
B	Smart Local	Local only (Ollama)	Compliant	Better answers, data stays local
C	Full Power	Cloud LLM	Partial	Maximum accuracy, research

slm mode a   # Zero-cloud (default)
slm mode b   # Local Ollama
slm mode c   # Cloud LLM

Mode A is the only agent memory that operates with zero cloud dependency while achieving competitive retrieval accuracy on a standard benchmark. All data stays on your device. No API keys. No GPU. Runs on 2 vCPUs + 4GB RAM.

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Architecture

Query  ──►  Strategy Classifier  ──►  6 Parallel Channels:
                                       ├── Semantic (Fisher-Rao geodesic distance)
                                       ├── BM25 (keyword matching)
                                       ├── Entity Graph (spreading activation, 3 hops)
                                       ├── Temporal (date-aware retrieval)
                                       ├── Associative (multi-hop spreading activation)
                                       └── Hopfield (partial query completion)
                                                    │
                                       RRF Fusion (k=60)
                                                    │
                                       Scene Expansion + Bridge Discovery
                                                    │
                                       Cross-Encoder Reranking
                                                    │
                                       ◄── Top-K Results with channel scores

Mathematical Foundations

Three novel contributions replace cloud LLM dependency with mathematical guarantees:

1. Fisher-Rao Retrieval Metric — Similarity scoring derived from the Fisher information structure of diagonal Gaussian families. Graduated ramp from cosine to geodesic distance over the first 10 accesses. The first application of information geometry to agent memory retrieval.

2. Sheaf Cohomology for Consistency — Algebraic topology detects contradictions by computing coboundary norms on the knowledge graph. The first algebraic guarantee for contradiction detection in agent memory.

3. Riemannian Langevin Lifecycle — Memory positions evolve on the Poincare ball via discretized Langevin SDE. Frequently accessed memories stay active; neglected memories self-archive. No hardcoded thresholds.

These three layers collectively yield +12.7pp average improvement over the engineering-only baseline, with the Fisher metric alone contributing +10.8pp on the hardest conversations.

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Benchmarks

Evaluated on LoCoMo — 10 multi-session conversations, 1,986 total questions, 4 scored categories.

Mode A (Zero-Cloud, 10 Conversations, 1,276 Questions)

Category	Score	vs. Mem0 (64.2%)
Single-Hop	72.0%	+3.0pp
Multi-Hop	70.3%	+8.6pp
Temporal	80.0%	+21.7pp
Open-Domain	85.0%	+35.0pp
Aggregate	74.8%	+10.6pp

Mode A achieves 85.0% on open-domain questions — the highest of any system in the evaluation, including cloud-powered ones.

Math Layer Impact (6 Conversations, n=832)

Conversation	With Math	Without	Delta
Easiest	78.5%	71.2%	+7.3pp
Hardest	64.2%	44.3%	+19.9pp
Average	71.7%	58.9%	+12.7pp

Mathematical layers help most where heuristic methods struggle — the harder the conversation, the bigger the improvement.

Ablation (What Each Component Contributes)

Removed	Impact
Cross-encoder reranking	-30.7pp
Fisher-Rao metric	-10.8pp
All math layers	-7.6pp
BM25 channel	-6.5pp
Sheaf consistency	-1.7pp
Entity graph	-1.0pp

Full ablation details in the Wiki.

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EU AI Act Compliance

The EU AI Act (Regulation 2024/1689) takes full effect August 2, 2026. Every AI memory system that sends personal data to cloud LLMs for core operations has a compliance question to answer.

Requirement	Mode A	Mode B	Mode C
Data sovereignty (Art. 10)	Pass	Pass	Requires DPA
Right to erasure (GDPR Art. 17)	Pass	Pass	Pass
Transparency (Art. 13)	Pass	Pass	Pass
No network calls during memory ops	Yes	Yes	No

To the best of our knowledge, no existing agent memory system addresses EU AI Act compliance. Modes A and B pass all checks by architectural design — no personal data leaves the device during any memory operation.

Built-in compliance tools: GDPR Article 15/17 export + complete erasure, tamper-proof SHA-256 audit chain, data provenance tracking, ABAC policy enforcement.

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Web Dashboard

slm dashboard    # Opens at http://localhost:8765

Dashboard Screenshots (click to collapse)

23 tabs: Dashboard, Recall Lab, Knowledge Graph, Memories, Trust Scores, Math Health, Compliance, Learning, IDE Connections, Settings, Memory Lifecycle, Compression, Patterns, and more. Runs locally — no data leaves your machine.

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Active Memory (V3.1) — Memory That Learns (click to expand)

Every recall generates learning signals. Over time, the system adapts to your patterns — from baseline (0-19 signals) → rule-based (20+) → ML model (200+, LightGBM trained on YOUR usage). Zero LLM tokens spent. Four mathematical signals computed locally: co-retrieval, confidence lifecycle, channel performance, and entropy gap.

Auto-capture hooks: slm hooks install + slm observe + slm session-context. MCP tools: session_init, observe, report_feedback.

No competitor learns at zero token cost.

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Features

Retrieval

• 6-channel hybrid: Semantic (Fisher-Rao) + BM25 + Entity Graph + Temporal + Associative + Hopfield
• RRF fusion + cross-encoder reranking
• Agentic sufficiency verification (auto-retry on weak results)
• Adaptive ranking with LightGBM (learns from usage)
• Hopfield completion for vague/partial queries

Intelligence

• 11-step ingestion pipeline (entity resolution, fact extraction, emotional tagging, scene building)
• Automatic contradiction detection via sheaf cohomology
• Adaptive memory lifecycle — memories strengthen with use, fade when neglected
• Smart compression — embedding precision adapts to memory importance (up to 32x savings)
• Cognitive consolidation — automatic pattern extraction from related memories
• Auto-learned soft prompts injected into agent context
• Behavioral pattern detection and outcome tracking

Trust & Security

• Bayesian Beta-distribution trust scoring (per-agent, per-fact)
• Trust gates (block low-trust agents from writing/deleting)
• ABAC (Attribute-Based Access Control) with DB-persisted policies
• Tamper-proof hash-chain audit trail (SHA-256 linked entries)

Infrastructure

• 23-tab web dashboard with real-time visualization
• 17+ IDE integrations (Claude, Cursor, Windsurf, VS Code, JetBrains, Zed, etc.)
• 35 MCP tools + 7 MCP resources
• Profile isolation (independent memory spaces)
• 1400+ tests, MIT license, cross-platform (Mac/Linux/Windows)
• CPU-only — no GPU required
• Automatic orphaned process cleanup

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CLI Reference

Command	What It Does
slm remember "..."	Store a memory
slm recall "..."	Search memories
slm forget "..."	Delete matching memories
slm trace "..."	Recall with per-channel score breakdown
slm status	System status
slm health	Math layer health (Fisher, Sheaf, Langevin)
slm doctor	Pre-flight check (deps, worker, Ollama, database)
slm mode a/b/c	Switch operating mode
slm setup	Interactive first-time wizard
slm warmup	Pre-download embedding model
slm migrate	V2 to V3 migration
slm dashboard	Launch 17-tab web dashboard
slm mcp	Start MCP server (for IDE integration)
slm connect	Configure IDE integrations
slm hooks install	Wire auto-memory into Claude Code hooks
slm profile list/create/switch	Profile management
slm decay	Run memory lifecycle review
slm quantize	Run smart compression cycle
slm consolidate --cognitive	Extract patterns from memory clusters
slm soft-prompts	View auto-learned patterns
slm reap	Clean orphaned SLM processes

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Research Papers

V3: Information-Geometric Foundations

SuperLocalMemory V3: Information-Geometric Foundations for Zero-LLM Enterprise Agent Memory Varun Pratap Bhardwaj (2026) arXiv:2603.14588 · Zenodo DOI: 10.5281/zenodo.19038659

V2: Architecture & Engineering

SuperLocalMemory: A Structured Local Memory Architecture for Persistent AI Agent Context Varun Pratap Bhardwaj (2026) arXiv:2603.02240 · Zenodo DOI: 10.5281/zenodo.18709670

Cite This Work

@article{bhardwaj2026slmv3,
  title={Information-Geometric Foundations for Zero-LLM Enterprise Agent Memory},
  author={Bhardwaj, Varun Pratap},
  journal={arXiv preprint arXiv:2603.14588},
  year={2026},
  url={https://arxiv.org/abs/2603.14588}
}

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Prerequisites

Requirement	Version	Why
Node.js	14+	npm package manager
Python	3.11+	V3 engine runtime

All Python dependencies install automatically during npm install — core math, dashboard server, learning engine, and performance optimizations. If anything fails, the installer shows exact fix commands. Run slm doctor after install to verify everything works. BM25 keyword search works even without embeddings — you're never fully blocked.

Component	Size	When
Core libraries (numpy, scipy, networkx)	~50MB	During install
Dashboard & MCP server (fastapi, uvicorn)	~20MB	During install
Learning engine (lightgbm)	~10MB	During install
Search engine (sentence-transformers, torch)	~200MB	During install
Embedding model (nomic-embed-text-v1.5, 768d)	~500MB	First use or slm warmup
Mode B requires Ollama + a model (ollama pull llama3.2)	~2GB	Manual

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Contributing

See CONTRIBUTING.md for guidelines. Wiki for detailed documentation.

License

MIT License. See LICENSE.

Attribution

Part of Qualixar · Author: Varun Pratap Bhardwaj

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_{Built with mathematical rigor. Not in the race — here to help everyone build better AI memory systems.}