# @codai/memorai-mcp v9.0.0

**World-Class AI Memory Management with Relationship Intelligence**

A production-ready MCP server that provides advanced memory capabilities with AI-powered relationship detection, knowledge graph construction, and intelligent search. Built on CBD (Codai Better Database) architecture for enterprise-grade performance and reliability.

## Features

🚀 **High-Performance Architecture**

- CBD-based vector memory storage with HPKV architecture
- OpenAI embeddings integration for semantic search
- Production-ready error handling and recovery
- Zero data loss and corruption protection

🧠 **Advanced Memory Operations**

- Store memories with rich metadata and context
- Semantic search across stored memories
- Agent-isolated memory management
- Structured key-based memory organization
- Vector similarity search for memory keys

🔗 **NEW: Relationship Intelligence (v9.0.0)**

- AI-powered automatic relationship detection between memories
- Knowledge graph construction with nodes, edges, and clusters
- Multi-dimensional relationship analysis (semantic, temporal, contextual)
- Graph traversal and exploration with configurable depth
- Community detection and centrality analysis

🎯 **NEW: Advanced Search Intelligence (v9.0.0)**

- Multi-dimensional relevance scoring (semantic, temporal, usage-based)
- Intelligent query expansion and fuzzy matching
- Context-aware search result clustering
- Relationship-aware search ranking
- Advanced filtering and personalization

🔧 **Easy Integration**

- Compatible with VS Code MCP configuration
- Environment-based configuration
- Supports custom .env file paths
- Graceful shutdown and error handling
- Comprehensive logging and monitoring

## Quick Start

### Using with VS Code MCP

Add to your VS Code MCP configuration (`mcp.json`):

```json
{
  "mcpServers": {
    "memorai": {
      "command": "npx",
      "args": ["-y", "@codai/memorai-mcp@latest"],
      "env": {
        "DOTENV_CONFIG_PATH": "E:\\GitHub\\workspace-ai\\.env"
      }
    }
  }
}
```

### Environment Configuration

Create a `.env` file with:

```env
# Required
OPENAI_API_KEY=your-openai-api-key

# Optional
MEMORAI_CBD_PATH=./memorai-cbd-data
MEMORAI_LOG_LEVEL=info
MEMORAI_CACHE_SIZE=10000
MEMORAI_DIMENSIONS=1536
MEMORAI_EMBEDDING_MODEL=text-embedding-ada-002
MEMORAI_MAX_MEMORIES=100000
```

### Direct Usage

```bash
# Install and run
npx -y @codai/memorai-mcp@latest

# With custom environment
DOTENV_CONFIG_PATH="/path/to/.env" npx @codai/memorai-mcp@latest

# With custom CBD path
MEMORAI_CBD_PATH="/path/to/data" npx @codai/memorai-mcp@latest
```

## MCP Tools

The server provides the following MCP tools:

### Core Memory Tools

### `mcp_memoraimcp_remember`

Store a new memory with metadata and automatic relationship detection.

**Parameters:**

- `agentId` (string, required): Agent identifier for memory isolation
- `content` (string, required): Memory content to store
- `metadata` (object, optional): Additional metadata
  - `entityType`: Type of entity (e.g., 'plan', 'task', 'prompt')
  - `priority`: Priority level ('low', 'medium', 'high', 'critical')
  - `project`: Project name for organization
  - `session`: Session identifier
  - `tags`: Array of tags for categorization

**Example:**

```json
{
  "name": "mcp_memoraimcp_remember",
  "arguments": {
    "agentId": "copilot-agent",
    "content": "React Hooks provide a way to use state in functional components",
    "metadata": {
      "entityType": "concept",
      "priority": "high",
      "project": "react-learning",
      "tags": ["react", "hooks", "frontend"]
    }
  }
}
```

### `mcp_memoraimcp_recall`

Search and retrieve stored memories with advanced search intelligence.

**Parameters:**

- `agentId` (string, required): Agent identifier
- `query` (string, required): Search query
- `limit` (number, optional): Maximum results (default: 10)
- `minImportance` (number, optional): Minimum importance score (default: 0)
- `project` (string, optional): Filter by project
- `session` (string, optional): Filter by session

**Example:**

```json
{
  "name": "mcp_memoraimcp_recall",
  "arguments": {
    "agentId": "copilot-agent",
    "query": "react state management",
    "limit": 5,
    "project": "react-learning"
  }
}
```

### `mcp_memoraimcp_forget`

Delete a memory by structured key.

**Parameters:**

- `agentId` (string, required): Agent identifier
- `structuredKey` (string, required): Structured key of memory to delete

### `mcp_memoraimcp_context`

Get recent context for an agent.

**Parameters:**

- `agentId` (string, required): Agent identifier
- `contextSize` (number, optional): Number of recent memories (default: 5)

### `mcp_memoraimcp_get_memory`

Get memory by exact structured key.

**Parameters:**

- `structuredKey` (string, required): Exact structured key

### `mcp_memoraimcp_search_keys`

Vector similarity search for memory keys.

**Parameters:**

- `query` (string, required): Query for finding similar keys
- `limit` (number, optional): Maximum keys to return (default: 10)
- `minScore` (number, optional): Minimum similarity score (default: 0.7)

### NEW: Relationship Intelligence Tools (v9.0.0)

### `mcp_memoraimcp_link_memories`

Create explicit relationships between memories.

**Parameters:**

- `sourceMemoryKey` (string, required): Source memory structured key
- `targetMemoryKey` (string, required): Target memory structured key
- `relationshipType` (string, required): Type of relationship
  - `"related"`: General relationship
  - `"explains"`: Source explains target
  - `"references"`: Source references target
  - `"contradicts"`: Source contradicts target
  - `"builds_on"`: Source builds on target
  - `"implements"`: Source implements target
  - `"exemplifies"`: Source exemplifies target
  - `"depends_on"`: Source depends on target
- `strength` (number, optional): Relationship strength (0-1, default: 0.5)
- `context` (string, optional): Additional context about the relationship

**Example:**

```json
{
  "name": "mcp_memoraimcp_link_memories",
  "arguments": {
    "sourceMemoryKey": "react-learning_20250131_001",
    "targetMemoryKey": "react-learning_20250131_002",
    "relationshipType": "explains",
    "strength": 0.9,
    "context": "Hooks enable state management in functional components"
  }
}
```

### `mcp_memoraimcp_get_relationships`

Retrieve relationships for a specific memory.

**Parameters:**

- `memoryKey` (string, required): Memory structured key
- `maxDepth` (number, optional): Maximum relationship depth (default: 1)
- `relationshipTypes` (array, optional): Filter by relationship types
- `minStrength` (number, optional): Minimum relationship strength (default: 0.0)

**Example:**

```json
{
  "name": "mcp_memoraimcp_get_relationships",
  "arguments": {
    "memoryKey": "react-learning_20250131_001",
    "maxDepth": 2,
    "relationshipTypes": ["explains", "references"],
    "minStrength": 0.5
  }
}
```

### `mcp_memoraimcp_explore_graph`

Explore the knowledge graph from a starting memory.

**Parameters:**

- `startingMemoryKey` (string, required): Starting point for exploration
- `explorationRadius` (number, optional): Exploration radius (default: 2)
- `includeWeakLinks` (boolean, optional): Include weak relationships (default: false)
- `maxNodes` (number, optional): Maximum nodes to return (default: 50)

**Example:**

```json
{
  "name": "mcp_memoraimcp_explore_graph",
  "arguments": {
    "startingMemoryKey": "react-learning_20250131_001",
    "explorationRadius": 3,
    "includeWeakLinks": true,
    "maxNodes": 100
  }
}
```

## Configuration

### Environment Variables

| Variable                  | Description               | Default                  |
| ------------------------- | ------------------------- | ------------------------ |
| `OPENAI_API_KEY`          | OpenAI API key (required) | -                        |
| `DOTENV_CONFIG_PATH`      | Path to .env file         | `.env`                   |
| `MEMORAI_CBD_PATH`        | CBD data directory        | `./memorai-cbd-data`     |
| `MEMORAI_LOG_LEVEL`       | Log level                 | `info`                   |
| `MEMORAI_CACHE_SIZE`      | Memory cache size         | `10000`                  |
| `MEMORAI_DIMENSIONS`      | Embedding dimensions      | `1536`                   |
| `MEMORAI_EMBEDDING_MODEL` | OpenAI embedding model    | `text-embedding-ada-002` |
| `MEMORAI_MAX_MEMORIES`    | Maximum memories stored   | `100000`                 |

### VS Code MCP Configuration

The server is designed to work seamlessly with VS Code MCP configurations:

```json
{
  "mcpServers": {
    "memorai": {
      "command": "npx",
      "args": ["-y", "@codai/memorai-mcp@latest"],
      "env": {
        "DOTENV_CONFIG_PATH": "/absolute/path/to/.env"
      }
    }
  }
}
```

## Architecture

### CBD (Codai Better Database) Integration

The server uses CBD architecture for:

- High-performance vector storage
- FAISS-based similarity search
- Efficient memory indexing
- Production-ready reliability

### Memory Structure

Memories are stored with the following enhanced structure:

```typescript
interface AdvancedMemory {
  id: string; // Unique identifier
  content: string; // Memory content
  metadata: {
    agentId: string; // Agent identifier
    timestamp: string; // ISO timestamp
    importance: number; // Importance score (0-1)
    project?: string; // Project name
    session?: string; // Session identifier
    tags?: string[]; // Tags array
    entityType?: string; // Entity type
    priority?: string; // Priority level
  };
  structuredKey: string; // Structured key for organization
  embedding?: number[]; // Vector embedding
  relationships: MemoryRelationship[]; // NEW: Detected relationships
}

interface MemoryRelationship {
  targetKey: string; // Target memory key
  type: RelationshipType; // Relationship type
  strength: number; // Relationship strength (0-1)
  context?: string; // Optional context
  metadata: {
    detectionMethod: 'automatic' | 'explicit';
    confidence: number; // AI confidence score
    timestamp: string; // When relationship was created
  };
}
```

### Structured Keys

Memories use structured keys for organization:

```
{project}_{date}_{session}_{sequence}
```

Example: `codai-ecosystem_20241220_copilot-agent_001`

## Error Handling

The server includes comprehensive error handling:

- **Environment Validation**: Checks for required environment variables
- **Graceful Shutdown**: Handles SIGINT/SIGTERM signals
- **Data Persistence**: Automatic memory saving on shutdown
- **Recovery**: Loads existing memories on startup
- **Logging**: Comprehensive logging for debugging

## Troubleshooting

### Common Issues

1. **"OPENAI_API_KEY is required"**
   - Ensure `OPENAI_API_KEY` is set in your environment or .env file

2. **"Environment file not found"**
   - Check that `DOTENV_CONFIG_PATH` points to a valid .env file
   - Ensure the file exists and is readable

3. **"No memories found"**
   - Check that memories are being stored with correct `agentId`
   - Verify search query matches stored content
   - Check memory metadata filters

4. **"Failed to start server"**
   - Verify all dependencies are installed
   - Check CBD data directory permissions
   - Review environment variable configuration

### Debug Mode

Enable debug logging:

```bash
MEMORAI_LOG_LEVEL=debug npx @codai/memorai-mcp@latest
```

## Development

### Building from Source

```bash
# Clone and install dependencies
git clone <repository>
cd packages/@codai/memorai-mcp
npm install

# Build the package
npm run build

# Test the package
npm test

# Run locally
npm start
```

### Package Scripts

- `npm run build`: Build TypeScript to JavaScript
- `npm test`: Run package tests
- `npm start`: Start server locally
- `npm run clean`: Clean build artifacts

## Version History

### 9.0.0 - World-Class Enhancement Release

- **NEW**: AI-powered automatic relationship detection between memories
- **NEW**: Knowledge graph construction with nodes, edges, and clusters
- **NEW**: Three new MCP tools for relationship management:
  - `mcp_memoraimcp_link_memories`: Create explicit relationships
  - `mcp_memoraimcp_get_relationships`: Explore memory relationships
  - `mcp_memoraimcp_explore_graph`: Navigate knowledge graphs
- **NEW**: Advanced search intelligence with multi-dimensional scoring
- **NEW**: Intelligent query expansion and fuzzy matching
- **NEW**: Context-aware search result clustering
- **ENHANCED**: Memory recall with relationship-aware ranking
- **ENHANCED**: Automatic relationship detection during memory creation
- **IMPROVED**: Search relevance through semantic similarity and temporal analysis
- **ADDED**: Graph analytics with centrality and community detection
- **ADDED**: Comprehensive relationship type system (8 types)
- **ADDED**: Configurable relationship strength and context
- **ADDED**: Multi-depth graph traversal and exploration

### 8.0.0-cbd

- Initial CBD-based implementation
- Full MCP tool compatibility
- Production-ready architecture
- VS Code MCP integration
- Comprehensive error handling

## License

MIT License - see LICENSE file for details.

## Support

For issues and questions:

1. Check the troubleshooting section
2. Review environment configuration
3. Enable debug logging
4. Check VS Code MCP server logs

---

**Note**: This package is part of the CODAI ecosystem and uses CBD (Codai Better Database) for high-performance memory operations.