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Varun Pratap Bhardwaj edited this page Feb 5, 2026 · 13 revisions

Frequently Asked Questions

Common questions about SuperLocalMemory V2, answered.

General Questions

What is SuperLocalMemory V2?

SuperLocalMemory V2 is an intelligent local memory system for AI assistants. It stores your conversations, code decisions, and project context locally, so AI assistants like Claude can remember everything about you and your projects.

Is it really free?

Yes, 100% free. MIT license. No usage limits. No credit systems. No "free tier" restrictions. Use it commercially if you want.

Does it only work with Claude?

No! While optimized for Claude CLI, SuperLocalMemory works with:

  • Any AI assistant via CLI commands or Python API
  • Standalone via terminal
  • Custom integrations via the Python module

Where is my data stored?

100% on your local machine at ~/.claude-memory/. Nothing is ever sent to any cloud service. No telemetry, no analytics, no data collection.

Privacy & Security

Is my data safe?

Yes. Your data:

  • Never leaves your computer
  • Is stored in a local SQLite database
  • Has no network connectivity
  • Requires no API keys or accounts

Is it GDPR/HIPAA compliant?

Yes, by default. Since no data leaves your machine, there's no third-party data processing to worry about. You have complete control over your data.

Can my employer use this for sensitive projects?

Yes. SuperLocalMemory is ideal for:

  • Enterprise environments
  • Classified projects
  • Healthcare (HIPAA)
  • Financial services
  • Air-gapped systems

What about the pattern learning? Does it phone home?

No. Pattern learning happens entirely locally using basic frequency analysis. No external AI services, no API calls, no internet required.

Technical Questions

What's the 4-layer architecture?

  1. Layer 1: Raw Storage — SQLite + full-text search
  2. Layer 2: Hierarchical Index — Tree structure for navigation
  3. Layer 3: Knowledge Graph — Auto-discovers relationships
  4. Layer 4: Pattern Learning — Learns your preferences

Full architecture explanation →

How does the knowledge graph work?

  1. Extracts key terms from your memories (TF-IDF)
  2. Calculates similarity between memories
  3. Groups similar memories into clusters (Leiden algorithm)
  4. Auto-names clusters based on content

Example: It discovers "JWT", "OAuth", and "session tokens" are all related to "Authentication" — even if you never tagged them.

What's pattern learning?

Pattern learning analyzes your memories to detect:

  • Framework preferences ("React: 73% confidence")
  • Coding style ("Performance over readability: 58%")
  • Testing approaches ("Jest preferred: 65%")

You can feed this to Claude to get personalized suggestions.

Can I use multiple profiles?

Yes! Create isolated contexts:

superlocalmemoryv2:profile create work
superlocalmemoryv2:profile create personal
superlocalmemoryv2:profile create client-acme
superlocalmemoryv2:profile switch work

Each profile has completely separate memories, graphs, and patterns.

What databases does it support?

SQLite only (by design). Benefits:

  • Zero configuration
  • No server to run
  • Portable (single file)
  • Reliable and fast
  • Works everywhere

Can I export my data?

Yes. Your data is in ~/.claude-memory/memory.db. You can:

  • Copy the SQLite file
  • Query it with any SQLite tool
  • Write custom export scripts

Comparison Questions

How is this different from Mem0?

Aspect Mem0 SuperLocalMemory
Price Usage-based Free forever
Data location Cloud 100% local
Pattern learning No Yes
Setup API keys, accounts ./install.sh

How is this different from Zep?

Aspect Zep SuperLocalMemory
Price $50/month Free forever
Data location Cloud 100% local
4-layer architecture No Yes
Credit limits Yes No limits

Why not just use ChatGPT memory?

ChatGPT memory:

  • Is cloud-based (privacy concerns)
  • Has limited capacity
  • Doesn't work with Claude
  • No knowledge graphs
  • No pattern learning
  • No multi-profile

Is this like Obsidian or Notion?

No. Those are note-taking apps. SuperLocalMemory is specifically designed for:

  • AI assistant context
  • Automatic relationship discovery
  • Pattern learning
  • Code/development workflows

Usage Questions

How many memories can it store?

Unlimited. Tested up to 5,000+ memories with no issues. The graph engine has a configurable limit (default 5,000) for performance.

How fast is search?

  • Full-text search: ~45ms (3.3x faster than v1)
  • Graph queries: <100ms
  • Pattern lookup: <50ms

Do I need to manually tag everything?

No! The knowledge graph automatically discovers relationships. Tags are optional for additional organization.

How often should I rebuild the graph?

  • After adding 10+ new memories — rebuild for best results
  • Weekly — if you add memories regularly
  • It's fast — <2 seconds for 100 memories
python ~/.claude-memory/graph_engine.py build

Can I use this offline?

Yes, 100% offline. No internet connection required for any feature.

Troubleshooting

"command not found: superlocalmemoryv2"

Add to PATH:

export PATH="${HOME}/.claude-memory/bin:${PATH}"

Graph build fails with "sklearn not found"

Install optional dependency:

pip install scikit-learn

Memories not showing up in search

  1. Check if memory was added: superlocalmemoryv2:list
  2. Try exact phrase search
  3. Rebuild FTS index (rare): restart the app

More troubleshooting →

Contributing

How can I contribute?

See Contributing for guidelines. Areas we need help:

  • Performance optimizations
  • Graph visualization UI
  • Additional pattern categories
  • Documentation improvements

Is there a roadmap?

Yes! See Roadmap for upcoming features.

Support

Where do I report bugs?

GitHub Issues

Where can I ask questions?

GitHub Discussions

How can I support the project?

← Back to Home

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

V2 Documentation

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