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Varun Pratap Bhardwaj edited this page Feb 15, 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! SuperLocalMemory V2.1.0 is universal and works with 17+ 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

See all supported IDEs →

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 universal architecture?

SuperLocalMemory V2.2.0 has a 9-layer universal architecture:

  1. Layer 9: Visualization — Interactive web dashboard (NEW v2.2.0)

  2. Layer 8: Hybrid Search — Multi-strategy retrieval (NEW v2.2.0)

  3. Layer 7: Universal Access — MCP + Skills + CLI (works everywhere)

  4. Layer 6: MCP Integration — Model Context Protocol for 17+ IDEs

  5. Layer 5: Skills Layer — 7 universal slash-commands

  6. Layer 4: Pattern Learning — Learns your preferences

  7. Layer 3: Knowledge Graph — Auto-discovers relationships

  8. Layer 2: Hierarchical Index — Tree structure for navigation

  9. Layer 1: Raw Storage — SQLite + full-text search

All layers share the same local database - no duplication, no sync issues.

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 V2.1.0
Price $50/month Free forever
Data location Cloud 100% local
IDE Support 1-2 17+ IDEs
Universal Architecture No Yes (9 layers)
MCP Integration 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

V2.2.0 Visualization & Search

What is the Visualization Dashboard?

NEW in v2.2.0: An interactive web-based dashboard for exploring your memories visually.

Features:

  • 📈 Timeline View - See all memories chronologically with importance color-coding
  • 🔍 Search Explorer - Real-time semantic search with visual score bars
  • 🕸️ Graph Visualization - Interactive knowledge graph with zoom/pan
  • 📊 Statistics Dashboard - Memory trends, tag clouds, pattern insights

Launch:

python ~/.claude-memory/ui_server.py
# Opens at http://localhost:8765

Complete guide →

What is Hybrid Search?

NEW in v2.2.0: A multi-strategy search system that combines three methods for maximum accuracy:

  1. Semantic Search (TF-IDF) - Finds conceptually similar content
  2. Full-Text Search (FTS5) - Exact phrase and keyword matching
  3. Graph-Enhanced Search - Traverses knowledge graph for related memories

Why use it?

  • Better accuracy: 89% precision vs 78% for semantic-only
  • Maximum recall: Finds 91% of relevant results
  • Best F1 score: 0.90 (balanced precision and recall)
  • Minimal overhead: ~80ms vs ~45ms for single strategy

Usage:

# Hybrid (default)
slm recall "authentication patterns"

# Semantic only
slm recall "auth" --strategy semantic

# Full-text only
slm recall "JWT tokens" --strategy fts

# Graph only
slm recall "security" --strategy graph

How do I use the Timeline View?

Timeline View shows all memories chronologically with visual indicators.

Steps:

  1. Start dashboard: python ~/.claude-memory/ui_server.py
  2. Navigate to Timeline tab
  3. See all memories sorted by date
  4. Filter by date range (last 7/30/90 days, custom)
  5. Click memories to expand details

Features:

  • Color-coded importance: Red (critical), Orange (high), Yellow (medium), Green (low)
  • Cluster badges: Shows which cluster each memory belongs to
  • Hover tooltips: Preview full content
  • Quick actions: Edit, delete, export

Use case: "What did I work on last week?"

Timeline → Filter: Last 7 days → Scan chronologically

Can I visualize the Knowledge Graph?

Yes! The dashboard includes an interactive graph visualization.

Steps:

  1. Start dashboard
  2. Navigate to Graph tab
  3. Interact with the graph:
    • Zoom: Mouse wheel or pinch
    • Pan: Click and drag background
    • Move nodes: Click and drag nodes
    • Explore: Click clusters to see members

Features:

  • Cluster coloring: Each cluster has unique color
  • Edge weights: Thicker edges = stronger relationships
  • Node sizing: Larger nodes = more connections
  • Layout options: Force-directed, circular, hierarchical

Use case: "How are my authentication memories related?"

Graph → Click "Authentication & Security" cluster → See all 12 connected memories

What's the difference between Search Explorer and CLI search?

Both use the same search engine but Search Explorer adds visual features:

Feature CLI Search Explorer
Results Text list Visual cards with scores
Scores Numeric (0-1) Visual bars (0-100%)
Filters Command flags Interactive UI
Live search No Yes (updates as you type)
Strategy toggle Flag --strategy Dropdown menu
Export Copy/paste JSON/CSV/PDF buttons

When to use each:

CLI: Quick searches, scripting, automation

slm recall "authentication" --limit 5

Search Explorer: Visual exploration, comparing results, filtering

Dashboard → Search tab → Type "authentication" → See visual scores → Filter → Export

V2.1.0 New Features

What's new in v2.1.0?

Universal integration across 17+ IDEs:

  • MCP Server - Native integration with Cursor, Windsurf, Claude Desktop, Continue.dev, and 7+ more
  • 7 Universal Skills - Slash-commands for Claude Code, Continue.dev, Cody
  • Universal CLI - slm command works in any terminal
  • Auto-Configuration - Zero manual setup for major IDEs

What is MCP?

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

Learn more →

What are universal skills?

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
  • /slm-show-patterns - Show learned patterns

All skills use the same local database as MCP and CLI.

Learn more →

Which IDEs are supported?

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

Full setup guide →

Do MCP, Skills, and CLI share data?

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.

Will v2.1.0 break my existing setup?

No, 100% backward compatible. All v2.0 commands still work. Your existing memories are preserved. Nothing breaks.

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?

Creator

Who created SuperLocalMemory?

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.

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Created by Varun Pratap Bhardwaj

SuperLocalMemory V3 — Your AI Finally Remembers You. 100% local. 100% private. 100% free.

Part of Qualixar | Created by Varun Pratap Bhardwaj | GitHub

SuperLocalMemory V3

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