SuperLocalMemory V3

The first local-only AI memory to break 74% retrieval on LoCoMo.
No cloud. No APIs. No data leaves your machine.

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

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):

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.

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"]
    }
  }
}

27 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. V3.1: Active Memory tools auto-learn your patterns.

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.

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.

Three Operating Modes

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.

Architecture

Query  ──►  Strategy Classifier  ──►  4 Parallel Channels:
                                       ├── Semantic (Fisher-Rao geodesic distance)
                                       ├── BM25 (keyword matching)
                                       ├── Entity Graph (spreading activation, 3 hops)
                                       └── Temporal (date-aware retrieval)
                                                    │
                                       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.

Benchmarks

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

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

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)

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

Ablation (What Each Component Contributes)

Full ablation details in the Wiki.

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.

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.

Web Dashboard

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

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

Active Memory (V3.1) — Memory That Learns

Most AI memory systems are passive databases — you store, you search, you get results. SuperLocalMemory learns.

Every recall you make generates learning signals. Over time, the system adapts to your patterns:

Zero-Cost Learning Signals

No LLM tokens spent. Four mathematical signals computed locally:

• Co-Retrieval — memories retrieved together strengthen their connections
• Confidence Lifecycle — accessed facts get boosted, unused facts decay
• Channel Performance — tracks which retrieval channel works best for your queries
• Entropy Gap — surprising content gets prioritized for deeper indexing

Auto-Capture & Auto-Recall

slm hooks install     # Install Claude Code hooks for invisible injection
slm observe "We decided to use PostgreSQL"  # Auto-detects decisions, bugs, preferences
slm session-context   # Get relevant context at session start

MCP Active Memory Tools

Three new tools for AI assistants:

• session_init — call at session start, get relevant project context automatically
• observe — send conversation content, auto-captures decisions/bugs/preferences
• report_feedback — explicit feedback for faster learning

No competitor learns at zero token cost. Mem0, Zep, and Letta all require cloud LLM calls for their learning loops. SLM learns through mathematics.

Features

Retrieval

• 4-channel hybrid: Semantic (Fisher-Rao) + BM25 + Entity Graph + Temporal
• RRF fusion + cross-encoder reranking
• Agentic sufficiency verification (auto-retry on weak results)
• Adaptive ranking with LightGBM (learns from usage)

Intelligence

• 11-step ingestion pipeline (entity resolution, fact extraction, emotional tagging, scene building)
• Automatic contradiction detection via sheaf cohomology
• Self-organizing memory lifecycle (no hardcoded thresholds)
• 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

• 17-tab web dashboard with real-time visualization
• 17+ IDE integrations (Claude, Cursor, Windsurf, VS Code, JetBrains, Zed, etc.)
• 24 MCP tools + 6 MCP resources
• Profile isolation (independent memory spaces)
• 1400+ tests, MIT license, cross-platform (Mac/Linux/Windows)
• CPU-only — no GPU required

CLI Reference

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}
}

Prerequisites

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.

Contributing

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

License

MIT License. See LICENSE.

Attribution

Part of Qualixar · Author: Varun Pratap Bhardwaj

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