simple-memory-mcp

mcp
Guvenlik Denetimi
Basarisiz
Health Uyari
  • License Ò€” License: MIT
  • Description Ò€” Repository has a description
  • Active repo Ò€” Last push 0 days ago
  • Low visibility Ò€” Only 8 GitHub stars
Code Basarisiz
  • child_process Ò€” Shell command execution capability in package.json
  • execSync Ò€” Synchronous shell command execution in package.json
  • process.env Ò€” Environment variable access in scripts/setup-vscode.js
  • process.env Ò€” Environment variable access in src/index.ts
  • exec() Ò€” Shell command execution in src/services/database-optimizer.ts
  • process.env Ò€” Environment variable access in src/services/database-optimizer.ts
Permissions Gecti
  • Permissions Ò€” No dangerous permissions requested
Purpose
This tool provides an AI assistant with persistent, long-term memory across chat sessions. It uses a local SQLite database to store and retrieve information, entirely avoiding cloud services.

Security Assessment
Overall risk: High. The audit flagged multiple instances of shell command execution capabilities, including the use of synchronous executions and direct `exec()` calls within the database optimizer. While the tool does not request explicit dangerous permissions, it reads environment variables across several files. There is no indication of hardcoded secrets or active network requests. However, the significant shell execution capabilities introduce a substantial attack surface that could potentially be exploited.

Quality Assessment
The project is licensed under the standard MIT license and was actively updated very recently. However, it suffers from extremely low community visibility, having only 8 GitHub stars. The developer explicitly notes in the documentation that this tool is no longer their main focus and points to a newer, alternative project. This indicates that while the code is currently functional, it may not receive ongoing maintenance or security patches.

Verdict
Use with cautionβ€”the underlying code executes shell commands, and the project is effectively unmaintained in favor of a newer tool.
SUMMARY

🧠 Persistent local memory for AI assistants across conversations. SQLite, no cloud, no config, works with any MCP client.

README.md

🧠 Simple Memory MCP Server

npm version
License: MIT
TypeScript

Give your AI assistant persistent memory across sessions.

πŸš€ Looking for the next evolution?

git-memory is now my daily driver β€” it builds on the ideas from simple-memory and takes them further. If you're starting fresh, check it out! Simple-memory is still functional but is no longer my main focus.

The problem: AI assistants forget everything when you start a new conversation. Your preferences, project context, decisions - all gone.

The solution: Simple Memory lets AI store and retrieve information that persists forever. Local SQLite database, zero cloud, sub-millisecond fast.

Why not RAG? RAG systems need vector databases, embeddings, chunking strategies, and ongoing maintenance. Simple Memory is just keyword search in SQLite - no ML infrastructure, no API costs, works offline. Perfect for personal knowledge that doesn't need semantic similarity.


How It Works

You: "Remember that I prefer TypeScript over JavaScript and use 4-space indentation"

AI: stores this automatically

...days later, new session...

You: "Help me set up a new project"

AI: searches memory, finds your preferences β†’ Sets up TypeScript with 4-space indentation

The AI handles storage and retrieval automatically. You just chat naturally.

Simple by design: Keyword search, not semantic. Local SQLite, not cloud. Zero setup, not configuration hell. If you need vector embeddings or team collaboration, see alternatives.

πŸ“– End-to-End Example

Session 1 (January):

You: "I'm using PostgreSQL with TypeScript. Decided on Prisma ORM after 
      evaluating TypeORM - better type inference and migrations."

AI: β†’ stores automatically with tags [tech-stack, database, decision]

Session 2 (3 weeks later):

You: "Setting up a new microservice, what database setup should I use?"

AI: β†’ searches memories, finds your PostgreSQL + Prisma decision
   "Based on your previous work, you standardized on PostgreSQL with Prisma 
    ORM. You chose it over TypeORM for the better type inference..."

What got stored:

{
  "content": "Tech stack decision: PostgreSQL + Prisma ORM. Chose over TypeORM for better type inference and cleaner migrations.",
  "tags": ["tech-stack", "database", "decision"],
  "relevance": 0.92  // BM25 score when searching "database setup"
}

πŸ€” Why I built this

I got tired of every new conversation starting from zero. I use this daily - saving project status, gotchas, ideas that pop up while I’m deep in work. Instead of breaking flow to write things down somewhere, I just tell the assistant to save it and keep going.
Having it all central means I can reference old solutions, things that failed, things that worked - across projects, across time. It’s basically an external memory for my dev brain.


✨ Features

  • 🧠 Persistent Memory - Survives across sessions, restarts, forever
  • πŸ” Full-Text Search - Find memories by keyword, tags, or content
  • 🏷️ Smart Tagging - Organize memories with tags
  • πŸ’Ύ Automatic Backups - Optional sync to OneDrive/Dropbox
  • πŸ“¦ Zero Config - Works out of the box
  • πŸš€ Fast - Sub-millisecond queries, 2,000-10,000 ops/sec

πŸš€ Quick Start

Configure MCP Client

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "simple-memory": {
      "command": "npx",
      "args": ["-y", "simple-memory-mcp"]
    }
  }
}

VS Code (.vscode/mcp.json):

{
  "mcpServers": {
    "simple-memory": {
      "command": "npx",
      "args": ["-y", "simple-memory-mcp"]
    }
  }
}

Restart your MCP client - that's it! No install needed.

πŸ’‘ Best Experience: Works best with Claude Sonnet in Agent Mode for optimal auto-capture.

Auto-Configure VS Code & Claude Desktop

npx -y simple-memory-mcp setup

This automatically configures all detected installations.

Optional: Install CLI

For command-line usage (search, stats, export/import):

npm install -g simple-memory-mcp
simple-memory setup  # auto-configure VS Code & Claude

For Contributors

git clone https://github.com/chrisribe/simple-memory-mcp.git
cd simple-memory-mcp
npm run setup  # installs, builds, links, and configures

That's it! The AI assistant can now remember information across conversations.


πŸ’» CLI Commands

# Setup - auto-configure VS Code and Claude Desktop
simple-memory setup

# Search memories
simple-memory search --query "typescript" --limit 5
simple-memory search --tags "project,work"

# Store a memory
simple-memory store --content "Remember this" --tags "note"

# Other operations
simple-memory get --hash "abc123..."
simple-memory delete --hash "abc123..."
simple-memory stats

# Export/import
simple-memory export-memory --output backup.json
simple-memory import-memory --input backup.json

Run simple-memory --help for all options.


πŸ› οΈ MCP Tools

The AI uses these tools automatically - you don't need to call them directly.

Tool Purpose
memory-graphql Store, search, update, delete memories
export-memory Backup memories to JSON file
import-memory Restore memories from JSON file
GraphQL Schema (for developers)
type Memory { hash, content, title, preview, tags, createdAt, relevance }
type StoreResult { success, hash, error }
type UpdateResult { success, hash, error }
type DeleteResult { success, hash, deletedCount, error }

type Query {
  memories(query: String, tags: [String], limit: Int): [Memory!]!
  memory(hash: String!): Memory
  related(hash: String!, limit: Int): [Memory!]!
  stats: Stats!
}
type Mutation {
  store(content: String!, tags: [String]): StoreResult!
  update(hash: String!, content: String!, tags: [String]): UpdateResult!
  delete(hash: String, tag: String): DeleteResult!
}

Note: Mutation results return minimal data (hash/success). To get full memory fields (title, tags, createdAt), query: { memory(hash: "...") { ... } }


βš™οΈ Configuration

Zero config default: ~/.simple-memory/memory.db

Custom database location:

{
  "mcpServers": {
    "simple-memory": {
      "command": "npx",
      "args": ["-y", "simple-memory-mcp"],
      "env": {
        "MEMORY_DB": "/path/to/memory.db"
      }
    }
  }
}

With automatic backups:

{
  "env": {
    "MEMORY_BACKUP_PATH": "/path/to/OneDrive/backups",
    "MEMORY_BACKUP_INTERVAL": "180"
  }
}

πŸ“– Full configuration guide: docs/configuration.md

  • Environment variables reference
  • Backup strategies
  • Cloud storage best practices
  • HTTP transport setup
  • Multiple database instances

πŸ”§ Development

npm install          # Install dependencies
npm run build        # Build TypeScript
npm test             # Run tests
npm run benchmark    # Performance benchmarks

Testing:

npm test              # GraphQL tests
npm run test:perf     # Performance tests  
npm run test:migration # Migration tests

πŸ“š Documentation

Guide Description
Configuration Full config reference, backups, cloud storage, HTTP transport
Examples Real-world scenarios, namespace patterns
Design Philosophy Trade-offs, BM25 relevance scoring
Performance Benchmarks and optimization details
Web Server Visual memory browser interface
Changelog Version history

Developer Docs: docs/dev/ - Manual testing, publishing guide, optimization history


🀝 Contributing

  1. Fork the repository
  2. Create feature branch (git checkout -b feature/amazing-feature)
  3. Commit changes (git commit -m 'Add amazing feature')
  4. Push to branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

πŸ“„ License

MIT License - see LICENSE for details.


πŸ™ Acknowledgments


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Made with ❀️ by chrisribe

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