# Advanced Features & Integration Guide

## Table of Contents
1. [mem0AI Integration](#mem0ai-integration)
2. [Security & Authentication](#security--authentication)
3. [CI/CD Integration](#cicd-integration)
4. [Performance Optimization](#performance-optimization)
5. [Enterprise Features](#enterprise-features)
6. [External Service Integration](#external-service-integration)
7. [Advanced Orchestration](#advanced-orchestration)
8. [Production Operations](#production-operations)
9. [Troubleshooting & Monitoring](#troubleshooting--monitoring)

---

## mem0AI Integration

### Overview

The Claude Multi-Agent PM Framework provides seamless integration with mem0AI for intelligent memory management, enabling agents to learn from previous interactions and maintain context across sessions.

### Memory Architecture

#### Four Memory Categories

The framework implements four distinct memory categories for optimal organization:

```python
from claude_pm.services.memory_service import MemoryService, MemoryCategory

class MemoryCategory(str, Enum):
    PROJECT = "project"    # Project-specific decisions and patterns
    PATTERN = "pattern"    # Reusable code patterns and solutions
    TEAM = "team"         # Team preferences and standards
    ERROR = "error"       # Error patterns and solutions
```

#### Memory Service Configuration

**Basic Configuration:**
```python
# config/memory_config.py
from claude_pm.core.memory_config import MemoryConfig

memory_config = MemoryConfig(
    host="localhost",
    port=8002,
    timeout=30,
    max_retries=3,
    retry_delay=1.0,
    connection_pool_size=10,
    enable_logging=True
)
```

**Advanced Configuration:**
```python
# Advanced memory configuration with custom categories
from claude_pm.services.memory_service import MemoryService

memory_service = MemoryService(config=memory_config)

# Custom memory categories for specific use cases
CUSTOM_CATEGORIES = {
    "architecture": "Architectural decisions and patterns",
    "security": "Security-related memories and patterns",
    "deployment": "Deployment and infrastructure patterns",
    "performance": "Performance optimization patterns"
}

# Register custom categories
for category, description in CUSTOM_CATEGORIES.items():
    memory_service.register_category(category, description)
```

### Memory Operations

#### Adding Project Memory

```python
# Add project-specific memory
await memory_service.add_project_memory(
    content="Implemented FastAPI authentication using JWT tokens",
    category=MemoryCategory.PROJECT,
    metadata={
        "project_id": "my-project",
        "technology": "FastAPI",
        "component": "authentication",
        "decision_rationale": "JWT chosen for stateless authentication"
    }
)

# Add pattern memory
await memory_service.add_pattern_memory(
    pattern_type="authentication",
    implementation="JWT with refresh tokens",
    use_case="API authentication",
    code_example="""
def create_access_token(data: dict, expires_delta: Optional[timedelta] = None):
    to_encode = data.copy()
    if expires_delta:
        expire = datetime.utcnow() + expires_delta
    else:
        expire = datetime.utcnow() + timedelta(minutes=15)
    to_encode.update({"exp": expire})
    return jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)
    """
)
```

#### Querying Memory

```python
# Query project memories
project_memories = await memory_service.get_project_memories(
    project_id="my-project",
    category=MemoryCategory.PROJECT,
    limit=10
)

# Search for patterns
auth_patterns = await memory_service.search_patterns(
    query="authentication JWT",
    technology="FastAPI",
    confidence_threshold=0.7
)

# Get error solutions
error_solutions = await memory_service.get_error_solutions(
    error_type="ImportError",
    context="FastAPI application startup"
)
```

### Context Management

#### Context Persistence

```python
from claude_pm.services.mem0_context_manager import Mem0ContextManager

context_manager = Mem0ContextManager()

# Store session context
await context_manager.store_session_context(
    session_id="session-123",
    context={
        "current_task": "Implementing user authentication",
        "technologies": ["FastAPI", "SQLAlchemy", "JWT"],
        "progress": "70%",
        "next_steps": ["Add password reset", "Implement 2FA"]
    }
)

# Retrieve context
session_context = await context_manager.get_session_context("session-123")
```

#### Intelligent Context Switching

```python
# Switch context based on task type
await context_manager.switch_context(
    from_context="authentication",
    to_context="database_migration",
    preserve_patterns=True,
    transfer_relevant_memories=True
)
```

### Performance Optimization

#### Memory Caching

```python
from claude_pm.services.memory_cache import MemoryCache

# Configure memory cache
memory_cache = MemoryCache(
    cache_size=1000,
    ttl_seconds=300,
    eviction_policy="lru"
)

# Cache frequently accessed patterns
await memory_cache.cache_pattern(
    pattern_id="auth-jwt-pattern",
    pattern_data=auth_pattern,
    priority="high"
)
```

#### Batch Operations

```python
# Batch memory operations for better performance
memories_to_add = [
    {
        "content": "Implemented rate limiting",
        "category": MemoryCategory.PATTERN,
        "metadata": {"component": "middleware"}
    },
    {
        "content": "Added request validation",
        "category": MemoryCategory.PATTERN,
        "metadata": {"component": "validation"}
    }
]

await memory_service.add_memories_batch(memories_to_add)
```

---

## Security & Authentication

### API Key Authentication

#### Quick Setup

The Claude PM Framework includes built-in security tools for generating and managing API keys:

```bash
# Generate a new secure API key
python -m claude_pm.scripts.security_cli generate-key
```

This generates a cryptographically secure API key with usage instructions:

```
==================================================
  API Key Generation
==================================================
✅ Generated secure API key:

API Key: Xy9mN2kL8pQ4vR7sT1uW6eY3rE5tU2iO9pA1sD4fG7hJ0kL3zX8cV5bN6mQ9wE2r

📋 Usage Instructions:
1. Copy the API key above
2. Add to your .env file:
   MEM0AI_API_KEY=Xy9mN2kL8pQ4vR7sT1uW6eY3rE5tU2iO9pA1sD4fG7hJ0kL3zX8cV5bN6mQ9wE2r
3. Restart your Claude PM services
4. Store the key securely (password manager recommended)
```

#### Environment Configuration

For development:
```bash
# Create/edit .env file
cd ~/Projects/claude-multiagent-pm
nano .env

# Add configuration
MEM0AI_API_KEY=your_generated_api_key_here
MEM0AI_HOST=localhost
MEM0AI_PORT=8002
MEM0AI_TIMEOUT=30

# Security Settings (development)
MEM0AI_USE_TLS=false
MEM0AI_VERIFY_SSL=false
```

For production:
```bash
# Production environment configuration
MEM0AI_API_KEY=your_production_api_key_here
MEM0AI_HOST=your-mem0ai-server.com
MEM0AI_PORT=443
MEM0AI_TIMEOUT=60

# Security Settings (production)
MEM0AI_USE_TLS=true
MEM0AI_VERIFY_SSL=true
```

#### Authentication Validation

Test your authentication setup:

```bash
# Validate configuration
python -m claude_pm.scripts.security_cli validate

# Test authentication
python -m claude_pm.scripts.security_cli test-auth
```

### Advanced Security Configuration

#### Custom Security Settings

```python
from claude_pm.integrations.security import SecurityConfig, Mem0AIAuthenticator

# Configure security settings
security_config = SecurityConfig(
    api_key="your-secure-api-key",
    use_tls=True,
    verify_ssl=True,
    auth_retry_attempts=3,
    max_auth_failures=5,
    auth_failure_lockout_minutes=15,
    require_request_signing=True
)

# Setup authentication
authenticator = Mem0AIAuthenticator(security_config)
await authenticator.authenticate()
```

#### Role-Based Access Control

```python
from claude_pm.core.rbac import RoleBasedAccessControl, Role, Permission

# Define roles and permissions
roles = {
    "admin": Role("admin", [
        Permission("agent.create"),
        Permission("agent.delete"),
        Permission("memory.read"),
        Permission("memory.write"),
        Permission("system.configure")
    ]),
    "developer": Role("developer", [
        Permission("agent.create"),
        Permission("memory.read"),
        Permission("memory.write"),
        Permission("task.delegate")
    ]),
    "viewer": Role("viewer", [
        Permission("memory.read"),
        Permission("health.check")
    ])
}

# Initialize RBAC
rbac = RoleBasedAccessControl(roles)

# Check permissions
async def secure_operation(user_id: str, operation: str):
    """Perform operation with security check."""
    user_role = await rbac.get_user_role(user_id)
    if rbac.check_permission(user_role, operation):
        return await perform_operation(operation)
    else:
        raise PermissionError(f"User {user_id} lacks permission for {operation}")
```

### Security Event Monitoring

#### Comprehensive Audit Logging

```python
from claude_pm.core.audit_logger import AuditLogger
from datetime import datetime

class CMPMAuditLogger:
    """Enterprise-grade audit logging for CMPM."""
    
    def __init__(self):
        self.logger = AuditLogger()
    
    async def log_agent_action(self, agent_id: str, action: str, details: Dict):
        """Log agent actions for audit trail."""
        await self.logger.log_event({
            "timestamp": datetime.utcnow(),
            "event_type": "agent_action",
            "agent_id": agent_id,
            "action": action,
            "details": details,
            "severity": "info"
        })
    
    async def log_memory_access(self, user_id: str, operation: str, memory_id: str):
        """Log memory access for compliance."""
        await self.logger.log_event({
            "timestamp": datetime.utcnow(),
            "event_type": "memory_access",
            "user_id": user_id,
            "operation": operation,
            "memory_id": memory_id,
            "severity": "info"
        })
    
    async def log_security_event(self, event_type: str, details: Dict):
        """Log security events."""
        await self.logger.log_event({
            "timestamp": datetime.utcnow(),
            "event_type": "security_event",
            "security_event_type": event_type,
            "details": details,
            "severity": "warning"
        })
```

#### Security Event Types

- `auth_success` - Successful authentication
- `auth_failure` - Authentication failure
- `auth_lockout` - Host locked out due to failures
- `key_rotation` - API key rotation event
- `permission_denied` - Access denied events
- `config_change` - Security configuration changes

### Security Best Practices

#### API Key Security
- ✅ **Store in environment variables only**
- ✅ **Use minimum 32 character keys**
- ✅ **Rotate keys regularly (quarterly)**
- ✅ **Use unique keys per environment**
- ❌ **Never commit keys to version control**
- ❌ **Never log full API keys**

#### Network Security
- ✅ **Use TLS in production environments**
- ✅ **Verify SSL certificates**
- ✅ **Use secure network connections**
- ✅ **Implement network-level access controls**
- ❌ **Never disable SSL verification in production**

#### Security Validation Checklist

##### Development Environment
- [ ] API key configured (minimum 32 chars)
- [ ] Security event logging enabled
- [ ] Basic error handling implemented
- [ ] No hardcoded credentials

##### Production Environment
- [ ] Strong API key (48+ characters)
- [ ] TLS/HTTPS enabled
- [ ] SSL certificate verification enabled
- [ ] Security event monitoring and alerting
- [ ] Regular API key rotation schedule
- [ ] Network access controls in place
- [ ] Security audit completed

---

## CI/CD Integration

### GitHub Actions Integration

#### Basic Workflow Configuration

```yaml
# .github/workflows/cmpm-integration.yml
name: CMPM Integration

on:
  push:
    branches: [ main, develop ]
  pull_request:
    branches: [ main ]

jobs:
  cmpm-health-check:
    runs-on: ubuntu-latest
    
    steps:
    - uses: actions/checkout@v3
    
    - name: Setup Node.js
      uses: actions/setup-node@v3
      with:
        node-version: '18'
        cache: 'npm'
    
    - name: Setup Python
      uses: actions/setup-python@v4
      with:
        python-version: '3.11'
    
    - name: Install CMPM Framework
      run: |
        npm install -g @bobmatnyc/ai-trackdown-tools
        pip install claude-multiagent-pm[all]
    
    - name: Run CMPM Health Check
      run: |
        claude-pm health --format json > health-report.json
        aitrackdown status --stats
    
    - name: Upload Health Report
      uses: actions/upload-artifact@v3
      with:
        name: health-report
        path: health-report.json
```

#### Advanced CI/CD with Memory Integration

```yaml
# .github/workflows/cmpm-memory-integration.yml
name: CMPM Memory Integration

on:
  push:
    branches: [ main ]

jobs:
  deploy-with-memory:
    runs-on: ubuntu-latest
    
    services:
      mem0ai:
        image: mem0ai/mem0ai:latest
        ports:
          - 8002:8002
        env:
          MEM0AI_PORT: 8002
          MEM0AI_HOST: 0.0.0.0
    
    steps:
    - uses: actions/checkout@v3
    
    - name: Setup Environment
      run: |
        pip install claude-multiagent-pm[all]
        export MEM0AI_HOST=localhost
        export MEM0AI_PORT=8002
    
    - name: Test Memory Integration
      run: |
        python -c "
        from claude_pm.services.memory_service import MemoryService
        import asyncio
        
        async def test_memory():
            service = MemoryService()
            await service.add_project_memory('CI/CD deployment successful')
            print('Memory integration working')
        
        asyncio.run(test_memory())
        "
    
    - name: Deploy with Memory Context
      run: |
        claude-pm deploy --with-memory --environment=production
```

### GitLab CI/CD Setup

#### GitLab CI Configuration

```yaml
# .gitlab-ci.yml
stages:
  - test
  - deploy
  - monitor

variables:
  CMPM_ENVIRONMENT: "gitlab-ci"
  MEM0AI_HOST: "mem0ai-service"
  MEM0AI_PORT: "8002"

services:
  - name: mem0ai/mem0ai:latest
    alias: mem0ai-service
    variables:
      MEM0AI_PORT: 8002

test:
  stage: test
  image: node:18
  before_script:
    - npm install -g @bobmatnyc/ai-trackdown-tools
    - pip install claude-multiagent-pm[all]
  script:
    - claude-pm health
    - aitrackdown status
    - python -m pytest tests/
  artifacts:
    reports:
      junit: test-results.xml
    paths:
      - health-report.json

deploy:
  stage: deploy
  script:
    - claude-pm deploy --target=$CI_ENVIRONMENT_NAME
    - aitrackdown issue complete ISS-$(CI_PIPELINE_ID)
  only:
    - main

monitor:
  stage: monitor
  script:
    - claude-pm monitor --duration=300
  artifacts:
    paths:
      - monitoring-report.json
```

### Jenkins Integration

#### Jenkins Pipeline Configuration

```groovy
// Jenkinsfile
pipeline {
    agent any
    
    environment {
        CMPM_ENVIRONMENT = 'jenkins'
        MEM0AI_HOST = 'localhost'
        MEM0AI_PORT = '8002'
    }
    
    stages {
        stage('Setup') {
            steps {
                script {
                    // Install CMPM framework
                    sh 'npm install -g @bobmatnyc/ai-trackdown-tools'
                    sh 'pip install claude-multiagent-pm[all]'
                }
            }
        }
        
        stage('Health Check') {
            steps {
                script {
                    // Run health check
                    sh 'claude-pm health --format json > health-report.json'
                    
                    // Archive health report
                    archiveArtifacts artifacts: 'health-report.json'
                }
            }
        }
        
        stage('Deploy') {
            when {
                branch 'main'
            }
            steps {
                script {
                    // Deploy with memory integration
                    sh 'claude-pm deploy --with-memory --environment=production'
                    
                    // Update ticket status
                    sh "aitrackdown issue complete ISS-${env.BUILD_NUMBER}"
                }
            }
        }
        
        stage('Monitor') {
            post {
                always {
                    script {
                        // Run monitoring
                        sh 'claude-pm monitor --duration=300 --output=monitoring-report.json'
                        archiveArtifacts artifacts: 'monitoring-report.json'
                    }
                }
            }
        }
    }
}
```

### Automated Testing Integration

#### Test Configuration

```python
# tests/test_cmpm_integration.py
import pytest
import asyncio
from claude_pm.services.memory_service import MemoryService
from claude_pm.services.multi_agent_orchestrator import MultiAgentOrchestrator

class TestCMPMIntegration:
    
    @pytest.fixture
    async def memory_service(self):
        """Setup memory service for tests."""
        service = MemoryService()
        await service.initialize()
        yield service
        await service.cleanup()
    
    @pytest.fixture
    async def orchestrator(self):
        """Setup orchestrator for tests."""
        orchestrator = MultiAgentOrchestrator()
        await orchestrator.initialize()
        yield orchestrator
        await orchestrator.cleanup()
    
    async def test_memory_integration(self, memory_service):
        """Test memory service integration."""
        # Add test memory
        await memory_service.add_project_memory(
            "Test memory integration",
            category="test"
        )
        
        # Verify memory was added
        memories = await memory_service.get_project_memories(
            category="test"
        )
        assert len(memories) > 0
        assert "Test memory integration" in memories[0]["content"]
    
    async def test_agent_coordination(self, orchestrator):
        """Test agent coordination."""
        # Test agent discovery
        agents = await orchestrator.get_available_agents()
        assert len(agents) > 0
        
        # Test task delegation
        result = await orchestrator.delegate_task(
            agent_type="engineer",
            task="Run unit tests",
            context="CI/CD pipeline"
        )
        assert result["status"] == "completed"
```

---

## Performance Optimization

### Agent Performance Tuning

#### Concurrency Configuration

```python
# config/performance_config.py
from claude_pm.core.config import Config

performance_config = Config({
    "max_concurrent_agents": 8,
    "agent_timeout": 300,
    "memory_pool_size": 100,
    "circuit_breaker_threshold": 5,
    "circuit_breaker_timeout": 60,
    "health_check_interval": 30
})
```

#### Agent Pool Management

```python
from claude_pm.services.multi_agent_orchestrator import MultiAgentOrchestrator

orchestrator = MultiAgentOrchestrator(
    max_agents=10,
    agent_pool_size=5,
    load_balancing_strategy="round_robin"
)

# Configure agent-specific performance settings
await orchestrator.configure_agent_performance(
    agent_type="engineer",
    settings={
        "max_concurrent_tasks": 3,
        "task_timeout": 180,
        "memory_limit": "512MB",
        "cpu_limit": "0.5"
    }
)
```

### Memory Optimization

#### Memory Cache Configuration

```python
from claude_pm.services.memory_cache import MemoryCache
from claude_pm.utils.performance import HealthCache

# Configure memory cache
memory_cache = MemoryCache(
    cache_size=1000,
    ttl_seconds=300,
    eviction_policy="lru",
    compression=True
)

# Configure health cache
health_cache = HealthCache(
    default_ttl_seconds=30,
    max_size=100
)
```

#### Batch Processing

```python
# Batch processing for better performance
from claude_pm.services.memory_service import MemoryService

async def process_memories_batch(memories: List[Dict]):
    """Process memories in batches for better performance."""
    memory_service = MemoryService()
    
    # Process in batches of 50
    batch_size = 50
    for i in range(0, len(memories), batch_size):
        batch = memories[i:i + batch_size]
        await memory_service.add_memories_batch(batch)
        
        # Add small delay to prevent overwhelming the service
        await asyncio.sleep(0.1)
```

### Parallel Processing Patterns

#### Parallel Agent Execution

```python
from claude_pm.services.multi_agent_orchestrator import MultiAgentOrchestrator
import asyncio

async def parallel_agent_execution(tasks: List[Dict]):
    """Execute tasks in parallel across multiple agents."""
    orchestrator = MultiAgentOrchestrator()
    
    # Create tasks for parallel execution
    async_tasks = []
    for task in tasks:
        async_task = orchestrator.delegate_task(
            agent_type=task["agent_type"],
            task=task["description"],
            context=task["context"]
        )
        async_tasks.append(async_task)
    
    # Execute all tasks in parallel
    results = await asyncio.gather(*async_tasks, return_exceptions=True)
    
    # Process results
    successful_results = []
    failed_results = []
    
    for i, result in enumerate(results):
        if isinstance(result, Exception):
            failed_results.append({
                "task": tasks[i],
                "error": str(result)
            })
        else:
            successful_results.append(result)
    
    return {
        "successful": successful_results,
        "failed": failed_results
    }
```

### Resource Management

#### Circuit Breaker Pattern

```python
from claude_pm.utils.performance import CircuitBreaker

# Configure circuit breaker for external services
memory_circuit_breaker = CircuitBreaker(
    name="memory_service",
    failure_threshold=5,
    failure_rate_threshold=50.0,
    recovery_timeout=60.0,
    success_threshold=3
)

async def memory_operation_with_circuit_breaker():
    """Execute memory operation with circuit breaker protection."""
    try:
        result = await memory_circuit_breaker.call(
            lambda: memory_service.get_project_memories()
        )
        return result
    except CircuitBreakerOpenError:
        # Fallback to cached data or alternative approach
        return await get_cached_memories()
```

#### Resource Monitoring

```python
from claude_pm.services.health_monitor import HealthMonitor
import psutil

class ResourceMonitor:
    """Monitor system resources and adjust performance accordingly."""
    
    def __init__(self):
        self.health_monitor = HealthMonitor()
    
    async def monitor_resources(self):
        """Monitor system resources."""
        cpu_percent = psutil.cpu_percent(interval=1)
        memory_percent = psutil.virtual_memory().percent
        disk_percent = psutil.disk_usage('/').percent
        
        # Adjust performance based on resource usage
        if cpu_percent > 80:
            await self.reduce_concurrent_agents()
        elif cpu_percent < 30:
            await self.increase_concurrent_agents()
        
        if memory_percent > 85:
            await self.clear_memory_cache()
        
        # Report health status
        await self.health_monitor.report_resource_usage({
            "cpu_percent": cpu_percent,
            "memory_percent": memory_percent,
            "disk_percent": disk_percent
        })
```

---

## Enterprise Features

### Enterprise Deployment

#### Infrastructure Deployment

The Claude PM Framework provides comprehensive deployment infrastructure for enterprise environments:

```bash
# Deploy to client directory
npm run deploy -- --target ~/Clients/project-name --verbose

# Test deployment first
npm run deploy:dry-run -- --target ~/Clients/project-name

# Validate deployment
npm run validate-deployment -- --target ~/Clients/project-name --verbose
```

#### Multi-Environment Setup

```bash
# Development deployment
npm run deploy -- --target ~/dev/claude-pm --verbose

# Staging deployment
npm run deploy -- --target ~/staging/claude-pm --verbose

# Production deployment
npm run deploy -- --target ~/prod/claude-pm --verbose
```

#### Deployment Validation

The deployment system includes comprehensive validation:

```bash
# Full validation with details
npm run validate-deployment -- --target /path/to/deployment --verbose --json

# Check framework version
grep version .claude-pm/config.json

# Test CLI functionality
./bin/aitrackdown --help
./bin/atd status
```

### Security and Compliance

#### Enterprise Security Configuration

```python
from claude_pm.integrations.security import SecurityConfig, Mem0AIAuthenticator

# Enterprise security configuration
security_config = SecurityConfig(
    api_key="your-enterprise-api-key-64chars-minimum",
    use_tls=True,
    verify_ssl=True,
    auth_retry_attempts=3,
    max_auth_failures=5,
    auth_failure_lockout_minutes=15,
    require_request_signing=True,
    enable_audit_logging=True
)

# Setup authentication with enterprise features
authenticator = Mem0AIAuthenticator(security_config)
await authenticator.authenticate()
```

#### Compliance Support

The framework supports enterprise compliance requirements:

- **SOC 2 Type II** compliance requirements
- **ISO 27001** security standards
- **GDPR** data protection requirements
- **Industry standard** authentication practices

### Advanced Health Monitoring

#### Comprehensive Health Dashboard

```python
from claude_pm.services.health_monitor import HealthMonitor
from claude_pm.services.health_dashboard import HealthDashboard

# Configure enterprise monitoring
health_monitor = HealthMonitor(
    check_interval=30,
    alert_threshold=80,
    critical_threshold=95,
    enable_metrics=True,
    enable_alerts=True
)

health_dashboard = HealthDashboard(
    port=7001,
    enable_metrics=True,
    enable_alerts=True,
    enable_authentication=True
)

# Start monitoring with enterprise features
await health_monitor.start()
await health_dashboard.start()
```

#### Health Monitoring Features

- **Service Availability**: Monitor mem0AI MCP (port 8002), Portfolio Manager (port 3000), Git Portfolio Manager (port 3001)
- **Framework Compliance**: Validate CLAUDE.md files, TrackDown system, required directories
- **Project Health Assessment**: Monitor all managed projects in `~/Projects/managed/`
- **Git Repository Health**: Track commit activity, branch status, and repository health
- **Performance Metrics**: Response time tracking and performance rating
- **Intelligent Alerting**: Critical issue detection with configurable thresholds

#### Background Monitoring Setup

```bash
# Using PM2 (Development)
npm install -g pm2
pm2 start ecosystem.config.js
pm2 status claude-pm-health-monitor

# Using systemd (Production)
sudo cp claude-pm-health-monitor.service /etc/systemd/system/
sudo systemctl daemon-reload
sudo systemctl enable claude-pm-health-monitor
sudo systemctl start claude-pm-health-monitor
```

### Memory Advanced Features

#### Production Memory Configuration

```python
# config/memory_settings.py
from claude_pm.services.claude_pm_memory import ClaudePMConfig
import os

# Production configuration
PROD_CONFIG = ClaudePMConfig(
    host=os.getenv("MEMORY_SERVICE_HOST", "memory-service"),
    port=int(os.getenv("MEMORY_SERVICE_PORT", "8002")),
    timeout=30,
    max_retries=3,
    retry_delay=1.0,
    connection_pool_size=20,
    enable_logging=True,
    api_key=os.getenv("MEMORY_SERVICE_API_KEY"),
    batch_size=100,
    cache_ttl=300,
    compression_enabled=True
)
```

#### Memory Health Monitoring

```python
from claude_pm.monitoring.memory_health import MemoryHealthMonitor

class MemoryHealthMonitor:
    def __init__(self, check_interval: int = 60):
        self.check_interval = check_interval
        self.logger = logging.getLogger(__name__)
        self.memory = create_claude_pm_memory()
        self.is_running = False
    
    async def perform_health_check(self):
        """Perform comprehensive health check."""
        health_data = {
            "timestamp": datetime.now().isoformat(),
            "connection_status": False,
            "response_time": 0.0,
            "statistics": {},
            "alerts": []
        }
        
        try:
            # Test connection
            start_time = datetime.now()
            connected = await self.memory.connect()
            end_time = datetime.now()
            
            health_data["connection_status"] = connected
            health_data["response_time"] = (end_time - start_time).total_seconds()
            
            if connected:
                # Get statistics
                stats = self.memory.get_statistics()
                health_data["statistics"] = stats
                
                # Check for issues
                if stats["success_rate"] < 95:
                    health_data["alerts"].append("Low success rate")
                
                if stats["avg_response_time"] > 2.0:
                    health_data["alerts"].append("High response time")
                
                await self.memory.disconnect()
            else:
                health_data["alerts"].append("Connection failed")
            
        except Exception as e:
            health_data["alerts"].append(f"Health check error: {e}")
        
        return health_data
```

### Docker and Kubernetes Support

#### Docker Configuration

```dockerfile
# Dockerfile.memory-service
FROM python:3.9-slim

WORKDIR /app

COPY requirements/ requirements/
RUN pip install -r requirements/production.txt

COPY claude_pm/ claude_pm/
COPY config/ config/

# Environment variables
ENV CLAUDE_PM_MEMORY_HOST=memory-service
ENV CLAUDE_PM_MEMORY_PORT=8002
ENV CLAUDE_PM_MEMORY_POOL_SIZE=50
ENV CLAUDE_PM_MEMORY_ENABLE_LOGGING=true

CMD ["python", "-m", "claude_pm.services.memory_service"]
```

#### Kubernetes Deployment

```yaml
# k8s/memory-service.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: memory-service
spec:
  replicas: 3
  selector:
    matchLabels:
      app: memory-service
  template:
    metadata:
      labels:
        app: memory-service
    spec:
      containers:
      - name: memory-service
        image: mem0ai/mem0:latest
        ports:
        - containerPort: 8002
        env:
        - name: MEM0_API_KEY
          valueFrom:
            secretKeyRef:
              name: memory-secrets
              key: api-key
        resources:
          requests:
            memory: "512Mi"
            cpu: "250m"
          limits:
            memory: "1Gi"
            cpu: "500m"
        livenessProbe:
          httpGet:
            path: /health
            port: 8002
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /health
            port: 8002
          initialDelaySeconds: 5
          periodSeconds: 5
```

### Scalability Patterns

#### Horizontal Scaling

```python
from claude_pm.core.scaling import HorizontalScaler

class CMPMScaler:
    """Horizontal scaling for CMPM framework."""
    
    def __init__(self):
        self.scaler = HorizontalScaler()
    
    async def scale_agents(self, target_count: int):
        """Scale agent pool to target count."""
        current_count = await self.scaler.get_agent_count()
        
        if target_count > current_count:
            # Scale up
            for i in range(target_count - current_count):
                await self.scaler.create_agent()
        elif target_count < current_count:
            # Scale down
            for i in range(current_count - target_count):
                await self.scaler.remove_agent()
    
    async def auto_scale_based_on_load(self):
        """Auto-scale based on current load."""
        load_metrics = await self.scaler.get_load_metrics()
        
        if load_metrics["cpu_usage"] > 80:
            await self.scale_agents(load_metrics["agent_count"] + 2)
        elif load_metrics["cpu_usage"] < 30:
            await self.scale_agents(max(1, load_metrics["agent_count"] - 1))
```

#### Load Balancing

```python
from claude_pm.core.load_balancer import LoadBalancer

# Configure load balancer
load_balancer = LoadBalancer(
    strategy="weighted_round_robin",
    health_check_interval=30,
    failure_threshold=3
)

# Add agent endpoints
await load_balancer.add_endpoint("agent-1", "http://localhost:8001", weight=1)
await load_balancer.add_endpoint("agent-2", "http://localhost:8002", weight=2)
await load_balancer.add_endpoint("agent-3", "http://localhost:8003", weight=1)

# Use load balancer for task distribution
async def distribute_task(task: Dict):
    """Distribute task using load balancer."""
    endpoint = await load_balancer.get_next_endpoint()
    return await endpoint.execute_task(task)
```

---

## External Service Integration

### API Integrations

#### RESTful API Integration

```python
from claude_pm.integrations.api_client import APIClient
from claude_pm.core.config import Config

# Configure API client
api_config = Config({
    "base_url": "https://api.example.com",
    "api_key": "your-api-key",
    "timeout": 30,
    "max_retries": 3,
    "retry_delay": 1.0
})

api_client = APIClient(api_config)

# Example: Integrate with external project management system
async def sync_with_external_pm(project_id: str):
    """Sync project data with external PM system."""
    
    # Get project data from external system
    external_data = await api_client.get(f"/projects/{project_id}")
    
    # Update CMPM memory with external data
    await memory_service.add_project_memory(
        content=f"External PM sync: {external_data['status']}",
        category=MemoryCategory.PROJECT,
        metadata={
            "external_system": "jira",
            "sync_timestamp": datetime.utcnow(),
            "project_id": project_id
        }
    )
    
    # Create tasks based on external data
    if external_data.get("new_issues"):
        for issue in external_data["new_issues"]:
            await orchestrator.delegate_task(
                agent_type="engineer",
                task=f"Address issue: {issue['title']}",
                context=f"External issue ID: {issue['id']}"
            )
```

#### GraphQL Integration

```python
from claude_pm.integrations.graphql_client import GraphQLClient

# Configure GraphQL client
graphql_client = GraphQLClient(
    endpoint="https://api.example.com/graphql",
    headers={"Authorization": "Bearer your-token"}
)

# Example: Query external data
async def query_external_data():
    """Query external data using GraphQL."""
    query = """
    query GetProjects($limit: Int!) {
        projects(limit: $limit) {
            id
            name
            status
            tasks {
                id
                title
                status
            }
        }
    }
    """
    
    variables = {"limit": 10}
    result = await graphql_client.execute(query, variables)
    
    # Process results and update memory
    for project in result["data"]["projects"]:
        await memory_service.add_project_memory(
            content=f"External project: {project['name']} - {project['status']}",
            category=MemoryCategory.PROJECT,
            metadata={"external_project_id": project["id"]}
        )
```

### Database Connectivity

#### PostgreSQL Integration

```python
from claude_pm.integrations.database import DatabaseConnection
import asyncpg

# Configure database connection
db_config = {
    "host": "localhost",
    "port": 5432,
    "database": "cmpm_db",
    "user": "cmpm_user",
    "password": "secure_password"
}

db_connection = DatabaseConnection(db_config)

# Example: Store agent results in database
async def store_agent_results(agent_id: str, task_id: str, result: Dict):
    """Store agent task results in database."""
    
    async with db_connection.acquire() as conn:
        await conn.execute("""
            INSERT INTO agent_results (agent_id, task_id, result, created_at)
            VALUES ($1, $2, $3, $4)
        """, agent_id, task_id, result, datetime.utcnow())
        
        # Update memory with database reference
        await memory_service.add_project_memory(
            content=f"Agent {agent_id} completed task {task_id}",
            category=MemoryCategory.PROJECT,
            metadata={
                "database_record": True,
                "table": "agent_results",
                "agent_id": agent_id,
                "task_id": task_id
            }
        )
```

#### MongoDB Integration

```python
from claude_pm.integrations.mongodb import MongoDBConnection
from motor.motor_asyncio import AsyncIOMotorClient

# Configure MongoDB connection
mongo_config = {
    "host": "localhost",
    "port": 27017,
    "database": "cmpm_db",
    "username": "cmpm_user",
    "password": "secure_password"
}

mongo_client = MongoDBConnection(mongo_config)

# Example: Store complex agent data
async def store_agent_memory(agent_id: str, memory_data: Dict):
    """Store agent memory data in MongoDB."""
    
    collection = mongo_client.get_collection("agent_memories")
    
    document = {
        "agent_id": agent_id,
        "memory_data": memory_data,
        "timestamp": datetime.utcnow(),
        "indexed": True
    }
    
    result = await collection.insert_one(document)
    
    # Update CMPM memory with MongoDB reference
    await memory_service.add_project_memory(
        content=f"Agent memory stored in MongoDB",
        category=MemoryCategory.PROJECT,
        metadata={
            "mongodb_id": str(result.inserted_id),
            "collection": "agent_memories",
            "agent_id": agent_id
        }
    )
```

### Cloud Service Integration

#### AWS Integration

```python
from claude_pm.integrations.aws import AWSIntegration
import boto3

# Configure AWS integration
aws_config = {
    "region": "us-east-1",
    "access_key_id": "your-access-key",
    "secret_access_key": "your-secret-key"
}

aws_client = AWSIntegration(aws_config)

# Example: Store agent artifacts in S3
async def store_artifacts_s3(agent_id: str, artifacts: Dict):
    """Store agent artifacts in AWS S3."""
    
    s3_client = aws_client.get_s3_client()
    
    # Upload artifacts to S3
    bucket_name = "cmpm-artifacts"
    object_key = f"agents/{agent_id}/artifacts/{datetime.utcnow().isoformat()}.json"
    
    await s3_client.put_object(
        Bucket=bucket_name,
        Key=object_key,
        Body=json.dumps(artifacts),
        ContentType="application/json"
    )
    
    # Update memory with S3 reference
    await memory_service.add_project_memory(
        content=f"Agent artifacts stored in S3",
        category=MemoryCategory.PROJECT,
        metadata={
            "s3_bucket": bucket_name,
            "s3_key": object_key,
            "agent_id": agent_id
        }
    )
```

### Webhook and Event Handling

#### Webhook Configuration

```python
from claude_pm.integrations.webhook import WebhookHandler
from fastapi import FastAPI

app = FastAPI()
webhook_handler = WebhookHandler()

@app.post("/webhooks/github")
async def handle_github_webhook(payload: Dict):
    """Handle GitHub webhook events."""
    
    event_type = payload.get("action", "unknown")
    
    if event_type == "push":
        # Handle push event
        await webhook_handler.handle_push_event(payload)
    elif event_type == "pull_request":
        # Handle pull request event
        await webhook_handler.handle_pr_event(payload)
    
    # Update memory with webhook event
    await memory_service.add_project_memory(
        content=f"GitHub webhook: {event_type}",
        category=MemoryCategory.PROJECT,
        metadata={
            "webhook_source": "github",
            "event_type": event_type,
            "timestamp": datetime.utcnow()
        }
    )
    
    return {"status": "processed"}

# Event-driven agent triggers
async def trigger_agent_on_event(event_type: str, event_data: Dict):
    """Trigger agent based on external events."""
    
    if event_type == "code_pushed":
        await orchestrator.delegate_task(
            agent_type="qa",
            task="Run automated tests",
            context=f"Code push to {event_data['branch']}"
        )
    elif event_type == "deployment_failed":
        await orchestrator.delegate_task(
            agent_type="ops",
            task="Investigate deployment failure",
            context=f"Deployment failed: {event_data['error']}"
        )
```

---

## Production Operations

### Deployment Management

#### Portable Deployment Strategy

The Claude PM Framework uses a hybrid NPM + Local Build strategy for portable deployments:

```bash
# Deploy framework to any directory
node install/deploy.js --target ~/production/claude-pm --verbose

# Validate deployment
node install/validate-deployment.js --target ~/production/claude-pm --verbose
```

#### Deployment Structure

```
deployment-directory/
├── claude_pm/              # Framework core
│   ├── cli.py              # Main CLI interface
│   ├── core/               # Core services
│   ├── services/           # Framework services
│   ├── integrations/       # External integrations
│   └── utils/              # Utility functions
├── tasks/                  # Ticket hierarchy
│   ├── epics/              # Strategic epics
│   ├── issues/             # Implementation issues
│   ├── tasks/              # Development tasks
│   └── templates/          # Ticket templates
├── bin/                    # CLI wrappers
│   ├── aitrackdown*        # Main CLI wrapper
│   └── atd*                # CLI alias
├── scripts/                # Deployment scripts
│   └── health-check*       # Health validation
├── .claude-pm/             # Deployment config
│   └── config.json         # Configuration file
└── CLAUDE.md               # Framework configuration
```

### Health Monitoring and Alerting

#### Comprehensive Health System

```bash
# Run single health check
npm run monitor:once

# Start continuous monitoring (5-minute intervals)
npm run monitor:health

# Check monitor status and latest health summary
npm run monitor:status

# View available reports
npm run monitor:reports

# Show recent alerts
npm run monitor:alerts
```

#### Health Monitoring Metrics

The system calculates a weighted overall health score:
- **Project Health (40%)**: Percentage of healthy projects
- **Service Health (35%)**: Percentage of healthy services
- **Framework Compliance (25%)**: Framework structure compliance

#### Alert System

```bash
# View recent alerts
npm run monitor:alerts

# Check alert count in status
npm run monitor:status

# Disable alerts temporarily
node scripts/automated-health-monitor.js monitor --no-alerts
```

### Performance Optimization

#### Resource Monitoring

```python
from claude_pm.services.health_monitor import HealthMonitor
import psutil

class ResourceMonitor:
    """Monitor system resources and adjust performance accordingly."""
    
    def __init__(self):
        self.health_monitor = HealthMonitor()
    
    async def monitor_resources(self):
        """Monitor system resources."""
        cpu_percent = psutil.cpu_percent(interval=1)
        memory_percent = psutil.virtual_memory().percent
        disk_percent = psutil.disk_usage('/').percent
        
        # Adjust performance based on resource usage
        if cpu_percent > 80:
            await self.reduce_concurrent_agents()
        elif cpu_percent < 30:
            await self.increase_concurrent_agents()
        
        if memory_percent > 85:
            await self.clear_memory_cache()
        
        # Report health status
        await self.health_monitor.report_resource_usage({
            "cpu_percent": cpu_percent,
            "memory_percent": memory_percent,
            "disk_percent": disk_percent
        })
```

#### Performance Metrics Collection

```python
from claude_pm.utils.metrics import MetricsCollector

class CMPMMetrics:
    """Collect and analyze CMPM performance metrics."""
    
    def __init__(self):
        self.metrics_collector = MetricsCollector()
    
    async def collect_performance_metrics(self):
        """Collect comprehensive performance metrics."""
        
        metrics = {
            "system_metrics": await self.collect_system_metrics(),
            "memory_metrics": await self.collect_memory_metrics(),
            "agent_metrics": await self.collect_agent_metrics(),
            "workflow_metrics": await self.collect_workflow_metrics()
        }
        
        # Store metrics in memory for trend analysis
        await memory_service.add_project_memory(
            content=f"Performance metrics collected",
            category=MemoryCategory.PROJECT,
            metadata={
                "metrics_type": "performance",
                "timestamp": datetime.utcnow(),
                "metrics": metrics
            }
        )
        
        return metrics
```

### Scaling and Load Balancing

#### Horizontal Scaling

```python
from claude_pm.core.scaling import HorizontalScaler

class CMPMScaler:
    """Horizontal scaling for CMPM framework."""
    
    def __init__(self):
        self.scaler = HorizontalScaler()
    
    async def scale_agents(self, target_count: int):
        """Scale agent pool to target count."""
        current_count = await self.scaler.get_agent_count()
        
        if target_count > current_count:
            # Scale up
            for i in range(target_count - current_count):
                await self.scaler.create_agent()
        elif target_count < current_count:
            # Scale down
            for i in range(current_count - target_count):
                await self.scaler.remove_agent()
    
    async def auto_scale_based_on_load(self):
        """Auto-scale based on current load."""
        load_metrics = await self.scaler.get_load_metrics()
        
        if load_metrics["cpu_usage"] > 80:
            await self.scale_agents(load_metrics["agent_count"] + 2)
        elif load_metrics["cpu_usage"] < 30:
            await self.scale_agents(max(1, load_metrics["agent_count"] - 1))
```

#### Load Balancing

```python
from claude_pm.core.load_balancer import LoadBalancer

# Configure load balancer
load_balancer = LoadBalancer(
    strategy="weighted_round_robin",
    health_check_interval=30,
    failure_threshold=3
)

# Add agent endpoints
await load_balancer.add_endpoint("agent-1", "http://localhost:8001", weight=1)
await load_balancer.add_endpoint("agent-2", "http://localhost:8002", weight=2)
await load_balancer.add_endpoint("agent-3", "http://localhost:8003", weight=1)

# Use load balancer for task distribution
async def distribute_task(task: Dict):
    """Distribute task using load balancer."""
    endpoint = await load_balancer.get_next_endpoint()
    return await endpoint.execute_task(task)
```

### Configuration Management

#### Environment-Specific Configuration

```python
from claude_pm.core.config import Config
import os

class CMPMConfigManager:
    """Manage configuration for different environments."""
    
    def __init__(self):
        self.environment = os.getenv("CMPM_ENVIRONMENT", "development")
        self.config = self.load_config()
    
    def load_config(self) -> Config:
        """Load configuration based on environment."""
        
        base_config = {
            "memory_service": {
                "host": "localhost",
                "port": 8002,
                "timeout": 30
            },
            "orchestrator": {
                "max_agents": 5,
                "agent_timeout": 300
            },
            "logging": {
                "level": "INFO",
                "file": "cmpm.log"
            }
        }
        
        # Environment-specific overrides
        if self.environment == "production":
            base_config.update({
                "memory_service": {
                    "host": "mem0ai-prod.example.com",
                    "port": 8002,
                    "timeout": 60,
                    "use_tls": True
                },
                "orchestrator": {
                    "max_agents": 20,
                    "agent_timeout": 600
                },
                "logging": {
                    "level": "WARNING",
                    "file": "/var/log/cmpm/cmpm.log"
                }
            })
        
        return Config(base_config)
```

---

## Advanced Orchestration

### Complex Workflow Patterns

#### Sequential Workflow

```python
from claude_pm.services.workflow_tracker import WorkflowTracker
from claude_pm.services.intelligent_workflow_orchestrator import IntelligentWorkflowOrchestrator

# Define sequential workflow
sequential_workflow = {
    "name": "feature_development",
    "type": "sequential",
    "steps": [
        {
            "name": "requirements_analysis",
            "agent_type": "architect",
            "task": "Analyze feature requirements",
            "timeout": 300
        },
        {
            "name": "design_review",
            "agent_type": "architect",
            "task": "Create technical design",
            "timeout": 600,
            "depends_on": ["requirements_analysis"]
        },
        {
            "name": "implementation",
            "agent_type": "engineer",
            "task": "Implement feature",
            "timeout": 1800,
            "depends_on": ["design_review"]
        },
        {
            "name": "testing",
            "agent_type": "qa",
            "task": "Test implementation",
            "timeout": 600,
            "depends_on": ["implementation"]
        },
        {
            "name": "deployment",
            "agent_type": "ops",
            "task": "Deploy to staging",
            "timeout": 300,
            "depends_on": ["testing"]
        }
    ]
}

# Execute sequential workflow
workflow_orchestrator = IntelligentWorkflowOrchestrator()
workflow_tracker = WorkflowTracker()

async def execute_sequential_workflow(workflow_id: str):
    """Execute a sequential workflow."""
    
    workflow = await workflow_tracker.get_workflow(workflow_id)
    
    for step in workflow["steps"]:
        # Check dependencies
        if step.get("depends_on"):
            await workflow_tracker.wait_for_dependencies(step["depends_on"])
        
        # Execute step
        result = await workflow_orchestrator.execute_step(
            step_name=step["name"],
            agent_type=step["agent_type"],
            task=step["task"],
            timeout=step.get("timeout", 300)
        )
        
        # Track step completion
        await workflow_tracker.mark_step_complete(
            workflow_id=workflow_id,
            step_name=step["name"],
            result=result
        )
        
        # Update memory with step completion
        await memory_service.add_project_memory(
            content=f"Workflow step completed: {step['name']}",
            category=MemoryCategory.PROJECT,
            metadata={
                "workflow_id": workflow_id,
                "step_name": step["name"],
                "agent_type": step["agent_type"]
            }
        )
```

#### Parallel Workflow

```python
# Define parallel workflow
parallel_workflow = {
    "name": "code_review",
    "type": "parallel",
    "parallel_groups": [
        {
            "name": "automated_checks",
            "steps": [
                {
                    "name": "security_scan",
                    "agent_type": "security",
                    "task": "Perform security scan"
                },
                {
                    "name": "performance_test",
                    "agent_type": "performance",
                    "task": "Run performance tests"
                },
                {
                    "name": "code_quality",
                    "agent_type": "qa",
                    "task": "Check code quality"
                }
            ]
        },
        {
            "name": "manual_review",
            "steps": [
                {
                    "name": "architecture_review",
                    "agent_type": "architect",
                    "task": "Review architecture changes"
                },
                {
                    "name": "documentation_review",
                    "agent_type": "documentation",
                    "task": "Review documentation"
                }
            ]
        }
    ]
}

# Execute parallel workflow
async def execute_parallel_workflow(workflow_id: str):
    """Execute a parallel workflow."""
    
    workflow = await workflow_tracker.get_workflow(workflow_id)
    
    # Create tasks for each parallel group
    parallel_tasks = []
    
    for group in workflow["parallel_groups"]:
        group_task = execute_parallel_group(workflow_id, group)
        parallel_tasks.append(group_task)
    
    # Execute all groups in parallel
    group_results = await asyncio.gather(*parallel_tasks, return_exceptions=True)
    
    # Process results
    for i, result in enumerate(group_results):
        group_name = workflow["parallel_groups"][i]["name"]
        
        if isinstance(result, Exception):
            await workflow_tracker.mark_group_failed(
                workflow_id=workflow_id,
                group_name=group_name,
                error=str(result)
            )
        else:
            await workflow_tracker.mark_group_complete(
                workflow_id=workflow_id,
                group_name=group_name,
                result=result
            )

async def execute_parallel_group(workflow_id: str, group: Dict):
    """Execute a parallel group."""
    
    # Create tasks for each step in the group
    step_tasks = []
    
    for step in group["steps"]:
        step_task = workflow_orchestrator.execute_step(
            step_name=step["name"],
            agent_type=step["agent_type"],
            task=step["task"],
            timeout=step.get("timeout", 300)
        )
        step_tasks.append(step_task)
    
    # Execute all steps in parallel
    step_results = await asyncio.gather(*step_tasks, return_exceptions=True)
    
    return {
        "group_name": group["name"],
        "step_results": step_results
    }
```

### Conditional Execution

#### Conditional Workflow Logic

```python
from claude_pm.services.workflow_selection_engine import WorkflowSelectionEngine

# Define conditional workflow
conditional_workflow = {
    "name": "deployment_pipeline",
    "type": "conditional",
    "conditions": [
        {
            "condition": "branch == 'main'",
            "workflow": "production_deployment"
        },
        {
            "condition": "branch == 'develop'",
            "workflow": "staging_deployment"
        },
        {
            "condition": "pull_request == True",
            "workflow": "pr_validation"
        }
    ],
    "default_workflow": "feature_validation"
}

# Execute conditional workflow
selection_engine = WorkflowSelectionEngine()

async def execute_conditional_workflow(context: Dict):
    """Execute workflow based on conditions."""
    
    # Evaluate conditions
    selected_workflow = await selection_engine.select_workflow(
        conditional_workflow,
        context
    )
    
    # Execute selected workflow
    if selected_workflow:
        await workflow_orchestrator.execute_workflow(
            workflow_name=selected_workflow,
            context=context
        )
        
        # Update memory with workflow selection
        await memory_service.add_project_memory(
            content=f"Conditional workflow selected: {selected_workflow}",
            category=MemoryCategory.PROJECT,
            metadata={
                "workflow_type": "conditional",
                "selected_workflow": selected_workflow,
                "context": context
            }
        )
```

### Error Handling and Recovery

#### Advanced Error Recovery

```python
from claude_pm.core.error_handler import ErrorHandler, RecoveryStrategy

class CMPMErrorHandler:
    """Advanced error handling for CMPM workflows."""
    
    def __init__(self):
        self.error_handler = ErrorHandler()
        self.recovery_strategies = {
            "timeout": RecoveryStrategy.RETRY_WITH_BACKOFF,
            "resource_exhausted": RecoveryStrategy.SCALE_UP,
            "agent_failure": RecoveryStrategy.DELEGATE_TO_BACKUP,
            "memory_error": RecoveryStrategy.CLEAR_CACHE_AND_RETRY
        }
    
    async def handle_workflow_error(self, workflow_id: str, error: Exception):
        """Handle workflow errors with intelligent recovery."""
        
        error_type = self.classify_error(error)
        recovery_strategy = self.recovery_strategies.get(
            error_type, 
            RecoveryStrategy.FAIL_FAST
        )
        
        # Log error to memory
        await memory_service.add_project_memory(
            content=f"Workflow error: {error_type} - {str(error)}",
            category=MemoryCategory.ERROR,
            metadata={
                "workflow_id": workflow_id,
                "error_type": error_type,
                "recovery_strategy": recovery_strategy.value
            }
        )
        
        # Execute recovery strategy
        if recovery_strategy == RecoveryStrategy.RETRY_WITH_BACKOFF:
            await self.retry_with_backoff(workflow_id)
        elif recovery_strategy == RecoveryStrategy.SCALE_UP:
            await self.scale_up_resources(workflow_id)
        elif recovery_strategy == RecoveryStrategy.DELEGATE_TO_BACKUP:
            await self.delegate_to_backup_agent(workflow_id)
        elif recovery_strategy == RecoveryStrategy.CLEAR_CACHE_AND_RETRY:
            await self.clear_cache_and_retry(workflow_id)
    
    def classify_error(self, error: Exception) -> str:
        """Classify error type for appropriate recovery."""
        
        if isinstance(error, asyncio.TimeoutError):
            return "timeout"
        elif "resource" in str(error).lower():
            return "resource_exhausted"
        elif "agent" in str(error).lower():
            return "agent_failure"
        elif "memory" in str(error).lower():
            return "memory_error"
        else:
            return "unknown"
    
    async def retry_with_backoff(self, workflow_id: str):
        """Retry workflow with exponential backoff."""
        
        for attempt in range(3):
            delay = 2 ** attempt  # Exponential backoff
            await asyncio.sleep(delay)
            
            try:
                await workflow_orchestrator.retry_workflow(workflow_id)
                return
            except Exception as e:
                if attempt == 2:  # Final attempt
                    raise e
```

### Monitoring and Alerting

#### Real-Time Workflow Monitoring

```python
from claude_pm.services.health_monitor import HealthMonitor
from claude_pm.services.workflow_tracker import WorkflowTracker

class WorkflowMonitor:
    """Monitor workflow execution and send alerts."""
    
    def __init__(self):
        self.health_monitor = HealthMonitor()
        self.workflow_tracker = WorkflowTracker()
        self.alert_thresholds = {
            "workflow_timeout": 1800,  # 30 minutes
            "error_rate": 0.1,  # 10% error rate
            "queue_depth": 50
        }
    
    async def monitor_workflows(self):
        """Monitor active workflows."""
        
        active_workflows = await self.workflow_tracker.get_active_workflows()
        
        for workflow in active_workflows:
            await self.check_workflow_health(workflow)
    
    async def check_workflow_health(self, workflow: Dict):
        """Check individual workflow health."""
        
        workflow_id = workflow["id"]
        start_time = workflow["start_time"]
        current_time = datetime.utcnow()
        
        # Check for timeout
        if (current_time - start_time).total_seconds() > self.alert_thresholds["workflow_timeout"]:
            await self.send_alert(
                alert_type="workflow_timeout",
                workflow_id=workflow_id,
                message=f"Workflow {workflow_id} has been running for over 30 minutes"
            )
        
        # Check error rate
        error_rate = await self.workflow_tracker.get_error_rate(workflow_id)
        if error_rate > self.alert_thresholds["error_rate"]:
            await self.send_alert(
                alert_type="high_error_rate",
                workflow_id=workflow_id,
                message=f"Workflow {workflow_id} has error rate of {error_rate:.1%}"
            )
    
    async def send_alert(self, alert_type: str, workflow_id: str, message: str):
        """Send alert notification."""
        
        await self.health_monitor.send_alert({
            "alert_type": alert_type,
            "workflow_id": workflow_id,
            "message": message,
            "timestamp": datetime.utcnow(),
            "severity": "warning"
        })
        
        # Log alert to memory
        await memory_service.add_project_memory(
            content=f"Alert sent: {alert_type} - {message}",
            category=MemoryCategory.PROJECT,
            metadata={
                "alert_type": alert_type,
                "workflow_id": workflow_id,
                "severity": "warning"
            }
        )
```

---

## Troubleshooting & Monitoring

### Common Issues and Solutions

#### Memory Service Connection Issues

```python
from claude_pm.utils.diagnostics import DiagnosticRunner

class MemoryServiceDiagnostics:
    """Diagnostic tools for memory service issues."""
    
    async def diagnose_memory_connection(self):
        """Diagnose memory service connection issues."""
        
        diagnostics = {
            "service_health": await self.check_service_health(),
            "network_connectivity": await self.check_network_connectivity(),
            "authentication": await self.check_authentication(),
            "resource_usage": await self.check_resource_usage()
        }
        
        return diagnostics
    
    async def check_service_health(self):
        """Check if memory service is healthy."""
        try:
            async with aiohttp.ClientSession() as session:
                async with session.get("http://localhost:8002/health") as response:
                    if response.status == 200:
                        return {"status": "healthy", "response": await response.json()}
                    else:
                        return {"status": "unhealthy", "status_code": response.status}
        except Exception as e:
            return {"status": "error", "error": str(e)}
    
    async def check_network_connectivity(self):
        """Check network connectivity to memory service."""
        try:
            # Test basic connectivity
            reader, writer = await asyncio.open_connection("localhost", 8002)
            writer.close()
            await writer.wait_closed()
            return {"status": "connected"}
        except Exception as e:
            return {"status": "connection_failed", "error": str(e)}
    
    async def check_authentication(self):
        """Check authentication with memory service."""
        try:
            authenticator = Mem0AIAuthenticator()
            result = await authenticator.authenticate()
            return {"status": "authenticated", "result": result}
        except Exception as e:
            return {"status": "authentication_failed", "error": str(e)}
```

#### Agent Coordination Issues

```python
class AgentDiagnostics:
    """Diagnostic tools for agent coordination issues."""
    
    async def diagnose_agent_issues(self):
        """Diagnose agent coordination issues."""
        
        diagnostics = {
            "agent_discovery": await self.check_agent_discovery(),
            "agent_health": await self.check_agent_health(),
            "task_queue": await self.check_task_queue(),
            "resource_limits": await self.check_resource_limits()
        }
        
        return diagnostics
    
    async def check_agent_discovery(self):
        """Check if agents can be discovered."""
        try:
            orchestrator = MultiAgentOrchestrator()
            agents = await orchestrator.get_available_agents()
            return {
                "status": "success",
                "agent_count": len(agents),
                "agents": [agent["type"] for agent in agents]
            }
        except Exception as e:
            return {"status": "error", "error": str(e)}
    
    async def check_agent_health(self):
        """Check health of individual agents."""
        try:
            orchestrator = MultiAgentOrchestrator()
            agents = await orchestrator.get_available_agents()
            
            health_results = {}
            for agent in agents:
                health = await orchestrator.check_agent_health(agent["id"])
                health_results[agent["type"]] = health
            
            return {"status": "success", "agent_health": health_results}
        except Exception as e:
            return {"status": "error", "error": str(e)}
```

### Performance Monitoring

#### Performance Metrics Collection

```python
from claude_pm.utils.metrics import MetricsCollector
import time

class CMPMMetrics:
    """Collect and analyze CMPM performance metrics."""
    
    def __init__(self):
        self.metrics_collector = MetricsCollector()
    
    async def collect_performance_metrics(self):
        """Collect comprehensive performance metrics."""
        
        metrics = {
            "system_metrics": await self.collect_system_metrics(),
            "memory_metrics": await self.collect_memory_metrics(),
            "agent_metrics": await self.collect_agent_metrics(),
            "workflow_metrics": await self.collect_workflow_metrics()
        }
        
        # Store metrics in memory for trend analysis
        await memory_service.add_project_memory(
            content=f"Performance metrics collected",
            category=MemoryCategory.PROJECT,
            metadata={
                "metrics_type": "performance",
                "timestamp": datetime.utcnow(),
                "metrics": metrics
            }
        )
        
        return metrics
    
    async def collect_system_metrics(self):
        """Collect system-level metrics."""
        import psutil
        
        return {
            "cpu_percent": psutil.cpu_percent(interval=1),
            "memory_percent": psutil.virtual_memory().percent,
            "disk_percent": psutil.disk_usage('/').percent,
            "network_io": psutil.net_io_counters()._asdict(),
            "process_count": len(psutil.pids())
        }
    
    async def collect_memory_metrics(self):
        """Collect memory service metrics."""
        try:
            memory_service = MemoryService()
            return {
                "total_memories": await memory_service.get_memory_count(),
                "memory_categories": await memory_service.get_category_distribution(),
                "cache_hit_rate": await memory_service.get_cache_hit_rate(),
                "average_response_time": await memory_service.get_average_response_time()
            }
        except Exception as e:
            return {"error": str(e)}
    
    async def collect_agent_metrics(self):
        """Collect agent performance metrics."""
        try:
            orchestrator = MultiAgentOrchestrator()
            return {
                "active_agents": await orchestrator.get_active_agent_count(),
                "completed_tasks": await orchestrator.get_completed_task_count(),
                "failed_tasks": await orchestrator.get_failed_task_count(),
                "average_task_duration": await orchestrator.get_average_task_duration(),
                "agent_utilization": await orchestrator.get_agent_utilization()
            }
        except Exception as e:
            return {"error": str(e)}
```

### Health Dashboard

#### Real-Time Health Monitoring

```python
from claude_pm.services.health_dashboard import HealthDashboard
from fastapi import FastAPI
from fastapi.responses import HTMLResponse

class CMPMHealthDashboard:
    """Real-time health dashboard for CMPM."""
    
    def __init__(self):
        self.app = FastAPI(title="CMPM Health Dashboard")
        self.health_dashboard = HealthDashboard()
        self.setup_routes()
    
    def setup_routes(self):
        """Setup dashboard routes."""
        
        @self.app.get("/", response_class=HTMLResponse)
        async def dashboard():
            """Main dashboard page."""
            return self.get_dashboard_html()
        
        @self.app.get("/api/health")
        async def health_status():
            """Get current health status."""
            return await self.health_dashboard.get_health_status()
        
        @self.app.get("/api/metrics")
        async def metrics():
            """Get performance metrics."""
            metrics_collector = CMPMMetrics()
            return await metrics_collector.collect_performance_metrics()
        
        @self.app.get("/api/agents")
        async def agents_status():
            """Get agent status."""
            diagnostics = AgentDiagnostics()
            return await diagnostics.diagnose_agent_issues()
        
        @self.app.get("/api/memory")
        async def memory_status():
            """Get memory service status."""
            diagnostics = MemoryServiceDiagnostics()
            return await diagnostics.diagnose_memory_connection()
    
    def get_dashboard_html(self):
        """Generate dashboard HTML."""
        return """
        <!DOCTYPE html>
        <html>
        <head>
            <title>CMPM Health Dashboard</title>
            <style>
                body { font-family: Arial, sans-serif; margin: 20px; }
                .status-card { border: 1px solid #ddd; padding: 20px; margin: 10px 0; border-radius: 5px; }
                .status-healthy { border-color: #4caf50; background-color: #f1f8e9; }
                .status-warning { border-color: #ff9800; background-color: #fff3e0; }
                .status-error { border-color: #f44336; background-color: #ffebee; }
                .metrics-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); gap: 20px; }
            </style>
        </head>
        <body>
            <h1>CMPM Health Dashboard</h1>
            <div id="dashboard-content">
                <div class="status-card">
                    <h2>System Status</h2>
                    <div id="system-status">Loading...</div>
                </div>
                
                <div class="metrics-grid">
                    <div class="status-card">
                        <h3>Memory Service</h3>
                        <div id="memory-status">Loading...</div>
                    </div>
                    
                    <div class="status-card">
                        <h3>Agent Coordination</h3>
                        <div id="agent-status">Loading...</div>
                    </div>
                    
                    <div class="status-card">
                        <h3>Performance Metrics</h3>
                        <div id="performance-metrics">Loading...</div>
                    </div>
                </div>
            </div>
            
            <script>
                async function updateDashboard() {
                    try {
                        // Update system status
                        const healthResponse = await fetch('/api/health');
                        const healthData = await healthResponse.json();
                        document.getElementById('system-status').innerHTML = 
                            `<div class="status-${healthData.status}">${healthData.status.toUpperCase()}</div>`;
                        
                        // Update memory status
                        const memoryResponse = await fetch('/api/memory');
                        const memoryData = await memoryResponse.json();
                        document.getElementById('memory-status').innerHTML = 
                            `<div class="status-${memoryData.service_health.status}">${memoryData.service_health.status}</div>`;
                        
                        // Update agent status
                        const agentResponse = await fetch('/api/agents');
                        const agentData = await agentResponse.json();
                        document.getElementById('agent-status').innerHTML = 
                            `<div>Active Agents: ${agentData.agent_discovery.agent_count}</div>`;
                        
                        // Update performance metrics
                        const metricsResponse = await fetch('/api/metrics');
                        const metricsData = await metricsResponse.json();
                        document.getElementById('performance-metrics').innerHTML = 
                            `<div>CPU: ${metricsData.system_metrics.cpu_percent}%</div>
                             <div>Memory: ${metricsData.system_metrics.memory_percent}%</div>`;
                    } catch (error) {
                        console.error('Error updating dashboard:', error);
                    }
                }
                
                // Update dashboard every 30 seconds
                setInterval(updateDashboard, 30000);
                updateDashboard(); // Initial load
            </script>
        </body>
        </html>
        """
    
    async def start(self, host: str = "0.0.0.0", port: int = 7001):
        """Start the health dashboard server."""
        import uvicorn
        
        await uvicorn.run(
            self.app,
            host=host,
            port=port,
            log_level="info"
        )
```

### Log Analysis and Debugging

#### Centralized Logging

```python
import logging
from claude_pm.core.logging_config import get_logger

class CMPMLogger:
    """Centralized logging for CMPM components."""
    
    def __init__(self):
        self.logger = get_logger(__name__)
        self.setup_log_handlers()
    
    def setup_log_handlers(self):
        """Setup log handlers for different components."""
        
        # File handler for persistent logging
        file_handler = logging.FileHandler('cmpm.log')
        file_handler.setLevel(logging.INFO)
        
        # Console handler for real-time monitoring
        console_handler = logging.StreamHandler()
        console_handler.setLevel(logging.WARNING)
        
        # Formatter
        formatter = logging.Formatter(
            '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        )
        file_handler.setFormatter(formatter)
        console_handler.setFormatter(formatter)
        
        self.logger.addHandler(file_handler)
        self.logger.addHandler(console_handler)
    
    def log_agent_activity(self, agent_id: str, activity: str, details: Dict):
        """Log agent activity."""
        self.logger.info(f"Agent {agent_id}: {activity}", extra={
            "agent_id": agent_id,
            "activity": activity,
            "details": details
        })
    
    def log_memory_operation(self, operation: str, category: str, details: Dict):
        """Log memory operations."""
        self.logger.info(f"Memory {operation}: {category}", extra={
            "operation": operation,
            "category": category,
            "details": details
        })
    
    def log_workflow_event(self, workflow_id: str, event: str, details: Dict):
        """Log workflow events."""
        self.logger.info(f"Workflow {workflow_id}: {event}", extra={
            "workflow_id": workflow_id,
            "event": event,
            "details": details
        })
```

### Configuration Management

#### Environment-Specific Configuration

```python
from claude_pm.core.config import Config
import os

class CMPMConfigManager:
    """Manage configuration for different environments."""
    
    def __init__(self):
        self.environment = os.getenv("CMPM_ENVIRONMENT", "development")
        self.config = self.load_config()
    
    def load_config(self) -> Config:
        """Load configuration based on environment."""
        
        base_config = {
            "memory_service": {
                "host": "localhost",
                "port": 8002,
                "timeout": 30
            },
            "orchestrator": {
                "max_agents": 5,
                "agent_timeout": 300
            },
            "logging": {
                "level": "INFO",
                "file": "cmpm.log"
            }
        }
        
        # Environment-specific overrides
        if self.environment == "production":
            base_config.update({
                "memory_service": {
                    "host": "mem0ai-prod.example.com",
                    "port": 8002,
                    "timeout": 60,
                    "use_tls": True
                },
                "orchestrator": {
                    "max_agents": 20,
                    "agent_timeout": 600
                },
                "logging": {
                    "level": "WARNING",
                    "file": "/var/log/cmpm/cmpm.log"
                }
            })
        elif self.environment == "testing":
            base_config.update({
                "memory_service": {
                    "host": "mem0ai-test.example.com",
                    "port": 8002,
                    "timeout": 15
                },
                "orchestrator": {
                    "max_agents": 3,
                    "agent_timeout": 180
                },
                "logging": {
                    "level": "DEBUG",
                    "file": "cmpm-test.log"
                }
            })
        
        return Config(base_config)
    
    def get_config(self) -> Config:
        """Get current configuration."""
        return self.config
    
    def reload_config(self):
        """Reload configuration."""
        self.config = self.load_config()
```

---

## Conclusion

The Claude Multi-Agent PM Framework provides a comprehensive suite of advanced features for enterprise-grade AI-assisted project management. This guide has covered:

- **mem0AI Integration**: Intelligent memory management with four distinct categories and advanced configuration
- **Security & Authentication**: Comprehensive API key management, role-based access control, and security monitoring
- **CI/CD Integration**: Seamless integration with GitHub Actions, GitLab CI, and Jenkins
- **Performance Optimization**: Agent tuning, memory optimization, and resource management
- **Enterprise Features**: Security, compliance, audit logging, scalability, and deployment infrastructure
- **External Service Integration**: APIs, databases, cloud services, and webhooks
- **Advanced Orchestration**: Complex workflows, conditional execution, and error handling
- **Production Operations**: Deployment management, health monitoring, scaling, and configuration management
- **Monitoring & Troubleshooting**: Real-time monitoring, diagnostics, and health dashboards

### Next Steps

1. **Start with Security Setup**: Configure API keys and authentication using the security CLI tools
2. **Deploy to Target Environment**: Use the deployment system to create portable installations
3. **Implement Health Monitoring**: Set up comprehensive health monitoring and alerting
4. **Add Enterprise Features**: Implement security, audit logging, and RBAC as needed
5. **Integrate External Services**: Connect your existing tools and services
6. **Advanced Orchestration**: Create complex workflows tailored to your processes
7. **Production Operations**: Implement scaling, load balancing, and configuration management

### Implementation Path

#### Phase 1: Foundation (Weeks 1-2)
- Security configuration and API key setup
- Basic mem0AI integration
- Health monitoring implementation
- Basic CI/CD integration

#### Phase 2: Enterprise Features (Weeks 3-4)
- Role-based access control
- Audit logging and compliance
- Performance optimization
- External service integration

#### Phase 3: Advanced Operations (Weeks 5-6)
- Advanced orchestration workflows
- Production deployment
- Scaling and load balancing
- Comprehensive monitoring

### Support Resources

- **Documentation**: Complete framework documentation in `/docs/`
- **Security Guide**: Comprehensive security documentation in `MEM0AI_SECURITY_GUIDE.md`
- **Deployment Guide**: Detailed deployment procedures in `DEPLOYMENT_GUIDE.md`
- **Health Monitoring**: Complete monitoring guide in `HEALTH_MONITORING.md`
- **Examples**: Working examples in `/examples/`
- **Community**: Join the CMPM community for support and best practices
- **Enterprise Support**: Available for production deployments

### Enterprise Capabilities

The framework is designed to scale with your needs, from simple development workflows to complex enterprise deployments:

- **Security**: Enterprise-grade authentication, authorization, and audit logging
- **Deployment**: Portable deployment system with comprehensive validation
- **Monitoring**: Real-time health monitoring with intelligent alerting
- **Scaling**: Horizontal scaling and load balancing capabilities
- **Compliance**: SOC 2, ISO 27001, and GDPR compliance support
- **Integration**: Extensive external service integration capabilities

Each feature can be implemented incrementally, allowing you to adopt advanced capabilities as your requirements grow.

---

**Framework Version**: 4.2.0  
**Last Updated**: 2025-07-09  
**Advanced Features Coverage**: 100%  
**Enterprise Features**: Complete