SuperLocalMemory V2

Your AI Finally Remembers You

⚡ Created & Architected by Varun Pratap Bhardwaj ⚡
Solution Architect • Original Creator • 2026

Stop re-explaining your codebase every session. 100% local. Zero setup. Completely free.

superlocalmemory.com • Quick Start • Why This? • Features • Docs • Issues

Created by Varun Pratap Bhardwaj • 💖 Sponsor • 📜 Attribution Required

What's New in v2.7 — "Your AI Learns You"

SuperLocalMemory now learns your patterns, adapts to your workflow, and personalizes recall — all 100% locally on your machine. No cloud. No LLM. Your behavioral data never leaves your device.

Adaptive Learning System

Your memory system evolves with you through three learning layers:

Three-Phase Adaptive Ranking

Recall results get smarter over time — automatically:

1. Phase 1 (Baseline): Standard search — same as v2.6
2. Phase 2 (Rule-Based): After ~20 feedback signals — boosts results matching your preferences
3. Phase 3 (ML Ranking): After ~200 signals — LightGBM LambdaRank re-ranks with 9 personalized features

Privacy by Design — GDPR Compliant

Your learning data is stored separately from your memories. Delete learning.db and your memories are untouched. Delete memory.db and your learning patterns are untouched. Full data sovereignty.

Research-Backed Architecture

Every component is grounded in peer-reviewed research, adapted for local-first operation:

New MCP Tools

New CLI Commands

slm useful 42 87           # Mark memories as useful (ranking feedback)
slm patterns list           # See learned tech preferences
slm learning status         # Learning system diagnostics
slm learning reset          # Delete all behavioral data (memories preserved)
slm engagement              # Local engagement health metrics

Upgrade: npm install -g superlocalmemory@latest — Learning dependencies install automatically.

Learning System Guide → | Upgrade Guide → | Full Changelog

The Problem

Every time you start a new Claude session:

You: "Remember that authentication bug we fixed last week?"
Claude: "I don't have access to previous conversations..."
You: *sighs and explains everything again*

AI assistants forget everything between sessions. You waste time re-explaining your:

• Project architecture
• Coding preferences
• Previous decisions
• Debugging history

The Solution

# Install in one command
npm install -g superlocalmemory

# Save a memory
superlocalmemoryv2:remember "Fixed auth bug - JWT tokens were expiring too fast, increased to 24h"

# Later, in a new session...
superlocalmemoryv2:recall "auth bug"
# ✓ Found: "Fixed auth bug - JWT tokens were expiring too fast, increased to 24h"

Your AI now remembers everything. Forever. Locally. For free.

🚀 Quick Start

Install (One Command)

npm install -g superlocalmemory

Or clone manually:

git clone https://github.com/varun369/SuperLocalMemoryV2.git && cd SuperLocalMemoryV2 && ./install.sh

Both methods auto-detect and configure 17+ IDEs and AI tools — Cursor, VS Code/Copilot, Codex, Claude, Windsurf, Gemini CLI, JetBrains, and more.

Verify Installation

superlocalmemoryv2:status
# ✓ Database: OK (0 memories)
# ✓ Graph: Ready
# ✓ Patterns: Ready

That's it. No Docker. No API keys. No cloud accounts. No configuration.

Launch Dashboard

# Start the interactive web UI
python3 ~/.claude-memory/ui_server.py

# Opens at http://localhost:8765
# Features: Timeline, search, interactive graph, statistics

💡 Why SuperLocalMemory?

For Developers Who Use AI Daily

Built on 2026 Research

Not another simple key-value store. SuperLocalMemory implements cutting-edge memory architecture backed by peer-reviewed research:

• PageIndex (Meta AI) → Hierarchical memory organization
• GraphRAG (Microsoft) → Knowledge graph with auto-clustering
• xMemory (Stanford) → Identity pattern learning
• A-RAG → Multi-level retrieval with context awareness
• LambdaRank (Burges 2010, MO-LightGBM SIGIR 2025) → Adaptive re-ranking (v2.7)
• TSW-PrefixSpan (IEEE 2020) → Time-weighted workflow pattern mining (v2.7)
• MACLA (arXiv:2512.18950) → Bayesian temporal confidence scoring (v2.7)
• FCS (LREC 2024) → Hierarchical cold-start mitigation (v2.7)

The only open-source implementation combining all eight approaches — entirely locally.

See research citations →

✨ Features

Multi-Layer Memory Architecture

View Interactive Architecture Diagram — Click any layer for details, research references, and file paths.

┌─────────────────────────────────────────────────────────────┐
│  Layer 9: VISUALIZATION (v2.2+)                             │
│  Interactive dashboard: timeline, graph explorer, analytics │
├─────────────────────────────────────────────────────────────┤
│  Layer 8: HYBRID SEARCH (v2.2+)                             │
│  Combines: Semantic + FTS5 + Graph traversal                │
├─────────────────────────────────────────────────────────────┤
│  Layer 7: UNIVERSAL ACCESS                                  │
│  MCP + Skills + CLI (works everywhere)                      │
│  17+ IDEs with single database                              │
├─────────────────────────────────────────────────────────────┤
│  Layer 6: MCP INTEGRATION                                   │
│  Model Context Protocol: 12 tools, 6 resources, 2 prompts   │
│  Auto-configured for Cursor, Windsurf, Claude               │
├─────────────────────────────────────────────────────────────┤
│  Layer 5½: ADAPTIVE LEARNING (v2.7 — NEW)                   │
│  Three-layer learning: tech prefs + project context + flow  │
│  LightGBM LambdaRank re-ranking (fully local, no cloud)    │
│  Research: eKNOW 2025, MACLA, TSW-PrefixSpan, LREC 2024    │
├─────────────────────────────────────────────────────────────┤
│  Layer 5: SKILLS LAYER                                      │
│  7 universal slash-commands for AI assistants               │
│  Compatible with Claude Code, Continue, Cody                │
├─────────────────────────────────────────────────────────────┤
│  Layer 4: PATTERN LEARNING + MACLA                          │
│  Bayesian confidence scoring (arXiv:2512.18950)             │
│  "You prefer React over Vue" (73% confidence)               │
├─────────────────────────────────────────────────────────────┤
│  Layer 3: KNOWLEDGE GRAPH + HIERARCHICAL CLUSTERING         │
│  Recursive Leiden algorithm: "Python" → "FastAPI" → "Auth" │
│  Community summaries with TF-IDF structured reports         │
├─────────────────────────────────────────────────────────────┤
│  Layer 2: HIERARCHICAL INDEX                                │
│  Tree structure for fast navigation                         │
│  O(log n) lookups instead of O(n) scans                     │
├─────────────────────────────────────────────────────────────┤
│  Layer 1: RAW STORAGE                                       │
│  SQLite + Full-text search + TF-IDF vectors                 │
│  Compression: 60-96% space savings                          │
└─────────────────────────────────────────────────────────────┘

Key Capabilities

• Adaptive Learning System — Learns your tech preferences, workflow patterns, and project context. Personalizes recall ranking using local ML (LightGBM). Zero cloud dependency. New in v2.7
• Knowledge Graphs — Automatic relationship discovery. Interactive visualization with zoom, pan, click.
• Pattern Learning — Learns your coding preferences and style automatically.
• Multi-Profile Support — Isolated contexts for work, personal, clients. Zero context bleeding.
• Hybrid Search — Semantic + FTS5 + Graph retrieval combined for maximum accuracy.
• Visualization Dashboard — Web UI for timeline, search, graph exploration, analytics.
• Framework Integrations — Use with LangChain and LlamaIndex applications.
• Real-Time Events — Live notifications via SSE/WebSocket/Webhooks when memories change.

🌐 Works Everywhere

SuperLocalMemory V2 is the ONLY memory system that works across ALL your tools:

Supported IDEs & Tools

Three Ways to Access

1. MCP (Model Context Protocol) — Auto-configured for Cursor, Windsurf, Claude Desktop

   • AI assistants get natural access to your memory
   • No manual commands needed
   • "Remember that we use FastAPI" just works

2. Skills & Commands — For Claude Code, Continue.dev, Cody

   • /superlocalmemoryv2:remember in Claude Code
   • /slm-remember in Continue.dev and Cody
   • Familiar slash command interface

3. Universal CLI — Works in any terminal or script

   • slm remember "content" - Simple, clean syntax
   • slm recall "query" - Search from anywhere
   • aider-smart - Aider with auto-context injection

All three methods use the SAME local database. No data duplication, no conflicts.

Complete setup guide for all tools →

🆚 vs Alternatives

The Hard Truth About "Free" Tiers

What Actually Matters

SuperLocalMemory V2 is the ONLY solution that:

• ✅ Learns and adapts locally — no cloud LLM needed for personalization
• ✅ Works across 17+ IDEs and CLI tools
• ✅ Remains 100% local (no cloud dependencies)
• ✅ GDPR Article 17 compliant — one-command data erasure
• ✅ Completely free with unlimited memories

See full competitive analysis →

⚡ Measured Performance

All numbers measured on real hardware (Apple M4 Pro, 24GB RAM). No estimates — real benchmarks.

Search Speed

For typical personal use (under 500 memories), search results return faster than you blink.

Concurrent Writes — Zero Errors

WAL mode + serialized write queue = zero "database is locked" errors, ever.

Storage

10,000 memories = 13.6 MB on disk (~1.9 KB per memory). Your entire AI memory history takes less space than a photo.

Graph Construction

Leiden clustering discovers 6-7 natural topic communities automatically.

Full benchmark details →

🔧 CLI Commands

# Memory Operations
superlocalmemoryv2:remember "content" --tags tag1,tag2  # Save memory
superlocalmemoryv2:recall "search query"                 # Search
superlocalmemoryv2:list                                  # Recent memories
superlocalmemoryv2:status                                # System health

# Profile Management
superlocalmemoryv2:profile list                          # Show all profiles
superlocalmemoryv2:profile create <name>                 # New profile
superlocalmemoryv2:profile switch <name>                 # Switch context

# Knowledge Graph
python ~/.claude-memory/graph_engine.py build            # Build graph
python ~/.claude-memory/graph_engine.py stats            # View clusters

# Pattern Learning
python ~/.claude-memory/pattern_learner.py update        # Learn patterns
python ~/.claude-memory/pattern_learner.py context 0.5   # Get identity

# Visualization Dashboard
python ~/.claude-memory/ui_server.py                     # Launch web UI

Complete CLI reference →

📖 Documentation

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Areas for contribution:

• Additional pattern categories
• Performance optimizations
• Integration with more AI assistants
• Documentation improvements

💖 Support This Project

If SuperLocalMemory saves you time, consider supporting its development:

• ⭐ Star this repo — helps others discover it
• 🐛 Report bugs — open an issue
• 💡 Suggest features — start a discussion
• ☕ Buy me a coffee — buymeacoffee.com/varunpratah
• 💸 PayPal — paypal.me/varunpratapbhardwaj
• 💖 Sponsor — GitHub Sponsors

📜 License

MIT License — use freely, even commercially. Just include the license.

👨‍💻 Author

Varun Pratap Bhardwaj — Solution Architect

Building tools that make AI actually useful for developers.

100% local. 100% private. 100% yours.