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semantic search capabilities using SQLite and embeddings.\n\n## Author\n\nJay Simons - https://yaa.bz\n\n## Features\n\n- 🧠 **Memory Storage**: Store text-based memories with metadata\n- 🔍 **Semantic Search**: Find memories by meaning, not just keywords  \n- ⚡ **Vector Embeddings**: Uses OpenAI's embedding models for semantic understanding\n- 🗄️ **SQLite Backend**: Lightweight, local database with vector search capabilities\n- 🔧 **MCP Compatible**: Works with any MCP-compatible AI assistant\n- 💻 **CLI Tools**: Full command-line interface for memory management\n- 📦 **Easy Installation**: Install via npm and start using immediately\n- ⚙️ **Flexible Config**: Use config files or environment variables\n\n## Installation\n\n### Global Installation (Recommended)\n```bash\nnpm install -g mcpmem@latest\n```\n\n## Quick Start\n\n### Option 1: Using Environment Variables (Simplest)\n```bash\n# Set your API key\nexport OPENAI_API_KEY=sk-your-openai-api-key-here\n\n# Optional: Customize model and database path\nexport OPENAI_MODEL=text-embedding-3-small\nexport MCPMEM_DB_PATH=/path/to/memories.db\n\n# Test the configuration\nmcpmem test\n\n# Start using the CLI or MCP server\nmcpmem stats\n```\n\n### Option 2: Using Configuration File\n1. **Initialize configuration**:\n   ```bash\n   mcpmem init\n   ```\n   \n   This creates `mcpmem.config.json` and updates `.gitignore`.\n\n2. **Edit the configuration file** and add your OpenAI API key:\n   ```json\n   {\n     \"embedding\": {\n       \"provider\": \"openai\", \n       \"apiKey\": \"your-openai-api-key-here\",\n       \"model\": \"text-embedding-3-small\"\n     },\n     \"database\": {\n       \"path\": \"./mcpmem.db\"\n     }\n   }\n   ```\n\n3. **Test the configuration**:\n   ```bash\n   mcpmem test\n   ```\n\n## CLI Usage\n\nMCPMem provides a comprehensive command-line interface for managing memories:\n\n### 📝 Storing Memories\n```bash\n# Store a simple memory\nmcpmem store \"Remember to review the quarterly reports\"\n\n# Store memory with metadata\nmcpmem store \"API endpoint returns 500 errors\" -m '{\"project\":\"web-app\",\"severity\":\"high\"}'\n```\n\n### 🔍 Searching Memories\n```bash\n# Semantic search \nmcpmem search \"database issues\"\n\n# Custom limits and thresholds\nmcpmem search \"bugs\" --limit 5 --threshold 0.8\n```\n\n### 📋 Listing Memories\n```bash\n# Show recent memories\nmcpmem list\n\n# Show more memories\nmcpmem list --limit 50\n```\n\n### 🔍 Getting Specific Memory\n```bash\n# Get memory details by ID\nmcpmem get abc123-def456-789\n```\n\n### 🗑️ Deleting Memories\n```bash\n# Delete with confirmation\nmcpmem delete abc123-def456-789\n\n# Force delete (no confirmation)\nmcpmem delete abc123-def456-789 --force\n\n# Clear all memories (with confirmation)\nmcpmem clear\n\n# Force clear all memories (no confirmation)\nmcpmem clear --force\n```\n\n### 📊 Database Info\n```bash\n# Show database statistics\nmcpmem stats\n\n# Show database file location and details\nmcpmem ls_db\n```\n\n### 📚 Help\n```bash\n# Show all available commands\nmcpmem --help\n\n# Show detailed examples and usage\nmcpmem help-commands\n\n# Get help for a specific command\nmcpmem search --help\n```\n\n## MCP Server Usage\n\n### Using with Cursor/Claude Desktop\n\nAdd to your MCP configuration file:\n\n#### With Environment Variables (Recommended)\n```json\n{\n  \"mcpServers\": {\n    \"mcpmem\": {\n      \"command\": \"mcpmem\",\n      \"env\": {\n        \"OPENAI_API_KEY\": \"your-openai-api-key-here\",\n        \"OPENAI_MODEL\": \"text-embedding-3-small\",\n        \"MCPMEM_DB_PATH\": \"/path/to/memories.db\"\n      }\n    }\n  }\n}\n```\n\n### Available MCP Tools\n\nWhen running as an MCP server, the following tools are available:\n\n- **`store_memory`**: Store a new memory with optional metadata\n- **`search_memories`**: Search memories using semantic similarity  \n- **`get_memory`**: Retrieve a specific memory by ID\n- **`get_all_memories`**: Get all memories (most recent first)\n- **`update_memory`**: Update an existing memory\n- **`delete_memory`**: Delete a memory by ID\n- **`get_memory_stats`**: Get statistics about the memory database\n- **`get_version`**: Get the version of mcpmem\n- **`ls_db`**: Show database file location and details\n- **`clear_all_memories`**: Delete all memories from the database\n\n## Examples\n\n### CLI Examples\n```bash\n# Store project-related memories\nmcpmem store \"Fixed the authentication bug in user login\" -m '{\"project\":\"web-app\",\"type\":\"bug-fix\"}'\nmcpmem store \"Meeting notes: Discussed Q4 roadmap priorities\" -m '{\"type\":\"meeting\",\"quarter\":\"Q4\"}'\n\n# Search for memories\nmcpmem search \"authentication issues\"\nmcpmem search \"meeting\" --limit 3\n\n# Manage memories\nmcpmem list --limit 10\nmcpmem get memory-id-here\nmcpmem delete old-memory-id --force\nmcpmem clear --force\n```\n\n### MCP Usage Examples\n\nWhen connected to an MCP-compatible assistant:\n\n```\nAssistant: I'll help you store that memory about the bug fix.\n\n*Uses store_memory tool*\n- Content: \"Fixed authentication timeout issue in production\"\n- Metadata: {\"severity\": \"high\", \"environment\": \"production\"}\n\nMemory stored successfully with ID: abc123-def456\n```\n\n```\nAssistant: Let me search for previous issues related to authentication.\n\n*Uses search_memories tool with query \"authentication problems\"*\n\nFound 3 related memories:\n1. Fixed authentication timeout issue (similarity: 85%)\n2. Updated auth middleware configuration (similarity: 78%)\n3. Resolved login redirect bug (similarity: 72%)\n```\n\n## Development\n\n### Building\n```bash\n# Install dependencies\npnpm install\n\n# Build the project\npnpm build\n\n# Type checking\npnpm tc\n```\n\n### Testing\n```bash\n# Run tests\npnpm test\n\n# Test configuration\nmcpmem test\n```\n\n## Database\n\nMCPMem uses SQLite with the `sqlite-vec` extension for vector similarity search. The database schema includes:\n\n- **memories**: Stores memory content, metadata, and timestamps\n- **embeddings**: Stores vector embeddings for semantic search\n\nThe database file is created automatically and includes proper indexing for fast retrieval.\n\n## Supported Embedding Models\n\nCurrently supports OpenAI embedding models:\n- `text-embedding-3-small` (1536 dimensions, default)\n- `text-embedding-3-large` (3072 dimensions)\n- `text-embedding-ada-002` (1536 dimensions, legacy)\n\n## Troubleshooting\n\n### Common Issues\n\n1. **\"OPENAI_API_KEY environment variable is required\"**\n   - Set the environment variable: `export OPENAI_API_KEY=sk-...`\n   - Or add it to your `mcpmem.config.json` file\n\n2. **\"Could not determine executable to run\"** (with npx)\n   - The package might not be published yet\n   - Use local installation instead: `npm install -g /path/to/mcpmem`\n\n3. **Database permission errors**\n   - Ensure the directory for the database path exists and is writable\n   - MCPMem automatically creates parent directories\n\n4. **Vector search not working**\n   - Ensure you have a valid OpenAI API key\n   - Check that embeddings are being generated: `mcpmem stats`\n\n### Debug Commands\n```bash\n# Check configuration and connectivity\nmcpmem test\n\n# View database statistics\nmcpmem stats\n\n# List recent memories to verify storage\nmcpmem list --limit 5\n```\n\n## License\n\nMIT\n\n## Contributing\n\n1. Fork the repository\n2. Create a feature branch\n3. Make your changes\n4. Add tests if applicable\n5. Submit a pull request\n\n---\n\nFor more information and updates, visit the [GitHub repository](https://github.com/designly1/mcpmem).","readmeFilename":"README.md"}