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FAQ
Common questions about SuperLocalMemory V2, answered.
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.
Yes, 100% free. MIT license. No usage limits. No credit systems. No "free tier" restrictions. Use it commercially if you want.
No! SuperLocalMemory V2.1.0 is universal and works with 11+ IDEs:
- MCP Integration: Cursor, Windsurf, Claude Desktop, Continue.dev, ChatGPT Desktop, Perplexity, Zed, OpenCode, Antigravity
- Skills: Claude Code, Continue.dev, Cody
- CLI: Aider, any terminal
- Python API: Custom integrations
100% on your local machine at ~/.claude-memory/. Nothing is ever sent to any cloud service. No telemetry, no analytics, no data collection.
Yes. Your data:
- Never leaves your computer
- Is stored in a local SQLite database
- Has no network connectivity
- Requires no API keys or accounts
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.
Yes. SuperLocalMemory is ideal for:
- Enterprise environments
- Classified projects
- Healthcare (HIPAA)
- Financial services
- Air-gapped systems
No. Pattern learning happens entirely locally using basic frequency analysis. No external AI services, no API calls, no internet required.
SuperLocalMemory V2.1.0 has a 7-layer universal architecture:
- Layer 7: Universal Access — MCP + Skills + CLI (works everywhere)
- Layer 6: MCP Integration — Model Context Protocol for 11+ IDEs
- Layer 5: Skills Layer — 6 universal slash-commands
- Layer 4: Pattern Learning — Learns your preferences
- Layer 3: Knowledge Graph — Auto-discovers relationships
- Layer 2: Hierarchical Index — Tree structure for navigation
- Layer 1: Raw Storage — SQLite + full-text search
All layers share the same local database - no duplication, no sync issues.
Full architecture explanation →
- Extracts key terms from your memories (TF-IDF)
- Calculates similarity between memories
- Groups similar memories into clusters (Leiden algorithm)
- 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.
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.
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.
SQLite only (by design). Benefits:
- Zero configuration
- No server to run
- Portable (single file)
- Reliable and fast
- Works everywhere
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
| Aspect | Mem0 | SuperLocalMemory |
|---|---|---|
| Price | Usage-based | Free forever |
| Data location | Cloud | 100% local |
| Pattern learning | No | Yes |
| Setup | API keys, accounts | ./install.sh |
| Aspect | Zep | SuperLocalMemory V2.1.0 |
|---|---|---|
| Price | 50ドル/month | Free forever |
| Data location | Cloud | 100% local |
| IDE Support | 1-2 | 11+ IDEs |
| Universal Architecture | No | Yes (7 layers) |
| MCP Integration | No | Yes |
| Credit limits | Yes | No limits |
ChatGPT memory:
- Is cloud-based (privacy concerns)
- Has limited capacity
- Doesn't work with Claude
- No knowledge graphs
- No pattern learning
- No multi-profile
No. Those are note-taking apps. SuperLocalMemory is specifically designed for:
- AI assistant context
- Automatic relationship discovery
- Pattern learning
- Code/development workflows
Universal integration across 11+ IDEs:
- MCP Server - Native integration with Cursor, Windsurf, Claude Desktop, Continue.dev, and 7+ more
- 6 Universal Skills - Slash-commands for Claude Code, Continue.dev, Cody
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Universal CLI -
slmcommand works in any terminal - Auto-Configuration - Zero manual setup for major IDEs
MCP (Model Context Protocol) is Anthropic's protocol for connecting AI assistants to external tools. SuperLocalMemory's MCP server lets AI naturally use your memory without slash commands.
Example:
You: "Remember that we use FastAPI for APIs"
Claude: [Uses remember tool automatically] ✓ Saved
Skills are slash-commands that work across multiple IDEs:
-
/slm-remember- Save memory -
/slm-recall- Search -
/slm-status- System health -
/slm-build-graph- Rebuild graph -
/slm-list-recent- Show recent -
/slm-switch-profile- Change profile
All skills use the same local database as MCP and CLI.
Auto-configured (run install.sh):
- ✅ Claude Desktop
- ✅ Cursor
- ✅ Windsurf
- ✅ Continue.dev (VS Code)
Manual setup available:
- ChatGPT Desktop, Perplexity AI, Zed, OpenCode, Antigravity, Cody, Aider
Yes! All three methods use the same SQLite database at ~/.claude-memory/memory.db. No duplication, no sync issues.
Save with MCP → Search with CLI → View with Skills → All work on the same memories.
No, 100% backward compatible. All v2.0 commands still work. Your existing memories are preserved. Nothing breaks.
Unlimited. Tested up to 5,000+ memories with no issues. The graph engine has a configurable limit (default 5,000) for performance.
- Full-text search: ~45ms (3.3x faster than v1)
- Graph queries: <100ms
- Pattern lookup: <50ms
No! The knowledge graph automatically discovers relationships. Tags are optional for additional organization.
- 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 buildYes, 100% offline. No internet connection required for any feature.
Add to PATH:
export PATH="${HOME}/.claude-memory/bin:${PATH}"
Install optional dependency:
pip install scikit-learn
- Check if memory was added:
superlocalmemoryv2:list - Try exact phrase search
- Rebuild FTS index (rare): restart the app
See Contributing for guidelines. Areas we need help:
- Performance optimizations
- Graph visualization UI
- Additional pattern categories
- Documentation improvements
Yes! See Roadmap for upcoming features.
Varun Pratap Bhardwaj - Solution Architect and Original Creator
SuperLocalMemory V2 is built as an open-source alternative to expensive cloud-based memory services like Mem0 and Zep.
- GitHub: github.com/varun369
- License: MIT (free for commercial use)
- Support: Buy me a coffee
Created by Varun Pratap Bhardwaj
SuperLocalMemory V3
Getting Started
Reference
Architecture
Enterprise
V2 Documentation