# Memory Manager Mode Examples

## Overview

This document provides practical examples of using the Memory Manager mode through MCP tools and CLI commands. All examples focus on real-world memory management scenarios using SAFLA's MCP interface and command-line tools.

## Basic Memory Management Examples

### Example 1: Memory Health Assessment

#### Using MCP Tools
```bash
# Check current system status
use_mcp_tool safla get_system_info '{}'

# Analyze memory performance bottlenecks
use_mcp_tool safla analyze_performance_bottlenecks '{
  "duration_seconds": 120,
  "include_memory_profile": true
}'

# Validate memory operations
use_mcp_tool safla validate_memory_operations '{
  "test_data_size": 50,
  "include_stress_test": false
}'
```

#### Using CLI Commands
```bash
# Quick memory health check
python -m safla.memory --health-check \
  --basic-metrics \
  --alert-thresholds memory:85,swap:50

# Detailed memory analysis
python -m safla.memory --analyze \
  --duration 120 \
  --include-profiling \
  --export-report memory_health.json

# View results
cat memory_health.json | jq '.memory_metrics'
```

### Example 2: Vector Operations Optimization

#### Using MCP Tools
```bash
# Optimize vector operations for better performance
use_mcp_tool safla optimize_vector_operations '{
  "batch_size": 200,
  "use_gpu": true
}'

# Benchmark vector performance
use_mcp_tool safla benchmark_vector_operations '{
  "vector_count": 1000,
  "vector_dimensions": 768,
  "operations": ["add", "search", "update"]
}'
```

#### Using CLI Commands
```bash
# Optimize vectors with specific parameters
python -m safla.vectors --optimize \
  --batch-size 200 \
  --use-gpu \
  --dimensions 768 \
  --cache-strategy lru

# Benchmark vector operations
python -m safla.benchmark --vectors \
  --count 1000 \
  --dimensions 768 \
  --operations add,search,update \
  --export-results vector_perf.json
```

## Advanced Memory Management Examples

### Example 3: Memory Agent Coordination

#### Creating and Managing Memory Agents
```bash
# Create vector optimization agent
use_mcp_tool safla create_agent_session '{
  "agent_type": "memory",
  "session_config": {
    "focus": "vector_optimization",
    "batch_size": 150,
    "optimization_level": "balanced"
  },
  "timeout_seconds": 3600
}'

# Create memory profiling agent via CLI
python -m safla.agents --create \
  --type memory \
  --config focus:profiling,sampling_rate:1000 \
  --name memory_profiler \
  --timeout 7200

# List active memory agents
use_mcp_tool safla list_agent_sessions '{
  "filter_by_type": "memory",
  "include_inactive": false
}'
```

#### Agent Interaction Examples
```bash
# Interact with vector optimization agent
use_mcp_tool safla interact_with_agent '{
  "session_id": "memory_001",
  "command": "optimize_batch_processing",
  "parameters": {
    "target_latency": "50ms",
    "memory_limit": "4GB"
  }
}'

# CLI agent interaction
python -m safla.agents --interact \
  --session memory_profiler \
  --command analyze_allocation_patterns \
  --params time_window:1h,pattern_depth:detailed
```

### Example 4: Performance Monitoring and Alerting

#### Real-time Memory Monitoring
```bash
# Start real-time memory monitoring
python -m safla.monitor --memory \
  --real-time \
  --interval 5 \
  --thresholds usage:85,fragmentation:30 \
  --alerts webhook:http://alerts.example.com/memory

# Access performance metrics via MCP resource
access_mcp_resource safla "safla://performance-metrics"

# Monitor system health
use_mcp_tool safla monitor_system_health '{
  "check_interval": 30,
  "alert_thresholds": {
    "memory_usage": 85,
    "memory_fragmentation": 30,
    "vector_latency": 100
  }
}'
```

#### Performance Baseline and Comparison
```bash
# Establish performance baseline
python -m safla.benchmark --memory \
  --duration 300 \
  --patterns sequential,random \
  --baseline-mode \
  --export baseline_memory.json

# Compare current performance to baseline
python -m safla.benchmark --memory \
  --duration 60 \
  --compare-with baseline_memory.json \
  --export current_memory.json

# View performance comparison
python -m safla.analyze --compare \
  --baseline baseline_memory.json \
  --current current_memory.json \
  --metrics latency,throughput,memory_usage
```

## Production Deployment Examples

### Example 5: Memory-Optimized Deployment

#### Deploying with Memory Optimization
```bash
# Deploy memory-optimized SAFLA instance
use_mcp_tool safla deploy_safla_instance '{
  "instance_name": "prod_memory_optimized",
  "environment": "production",
  "config_overrides": {
    "memory_optimization": "enabled",
    "vector_cache_size": 8192,
    "batch_processing": true,
    "gpu_acceleration": true
  }
}'

# Check deployment status
use_mcp_tool safla check_deployment_status '{
  "instance_name": "prod_memory_optimized"
}'

# Monitor deployment via CLI
python -m safla.monitor --deployment prod_memory_optimized \
  --memory-focus \
  --real-time \
  --export-logs deployment_memory.log
```

#### Scaling Memory Resources
```bash
# Scale memory resources based on load
use_mcp_tool safla scale_deployment '{
  "instance_name": "prod_memory_optimized",
  "scale_factor": 1.5,
  "resource_type": "memory"
}'

# Validate scaling effectiveness
python -m safla.benchmark --deployment prod_memory_optimized \
  --memory \
  --duration 120 \
  --export scaling_validation.json
```

### Example 6: Memory Leak Detection and Recovery

#### Leak Detection Workflow
```bash
# Start memory leak detection
python -m safla.memory --leak-detection \
  --duration 1800 \
  --sampling-rate 500 \
  --track-objects \
  --generate-flamegraph leak_analysis.svg

# Validate memory integrity
use_mcp_tool safla validate_memory_operations '{
  "test_data_size": 100,
  "include_stress_test": true
}'

# Check system logs for memory issues
access_mcp_resource safla "safla://system-logs"
```

#### Automatic Recovery
```bash
# Trigger memory optimization on leak detection
use_mcp_tool safla optimize_memory_usage '{
  "optimization_level": "aggressive",
  "target_memory_mb": 4096
}'

# Re-validate after optimization
use_mcp_tool safla validate_memory_operations '{
  "test_data_size": 50,
  "include_stress_test": false
}'

# Monitor recovery progress
python -m safla.monitor --memory \
  --recovery-mode \
  --duration 300 \
  --export recovery_metrics.json
```

## Learning and Adaptation Examples

### Example 7: Memory Pattern Learning

#### Analyzing Usage Patterns
```bash
# Analyze memory usage patterns
python -m safla.memory --pattern-analysis \
  --duration 24h \
  --pattern-types allocation,access,deallocation \
  --export-patterns memory_patterns.json

# Update learning parameters
use_mcp_tool safla update_learning_parameters '{
  "learning_rate": 0.1,
  "adaptation_threshold": 0.15,
  "memory_retention": 0.85,
  "exploration_factor": 0.2
}'

# Trigger learning cycle with memory focus
use_mcp_tool safla trigger_learning_cycle '{
  "learning_type": "incremental",
  "data_sources": ["memory_patterns", "performance_metrics"],
  "focus_areas": ["memory_optimization", "vector_performance"]
}'
```

#### Adaptive Memory Management
```bash
# Enable adaptive memory management
python -m safla.memory --adaptive \
  --learning-enabled \
  --adaptation-rate 0.1 \
  --performance-targets latency:50ms,throughput:1000ops

# Monitor learning effectiveness
access_mcp_resource safla "safla://learning-metrics"

# Analyze adaptation patterns
use_mcp_tool safla analyze_adaptation_patterns '{
  "pattern_type": "performance",
  "analysis_depth": "detailed",
  "time_window_days": 7
}'
```

### Example 8: Meta-Cognitive Memory Management

#### Self-Awareness and Introspection
```bash
# Get current system awareness state
use_mcp_tool safla get_system_awareness '{}'

# Update awareness with memory focus
use_mcp_tool safla update_awareness_state '{
  "awareness_level": 0.8,
  "focus_areas": ["memory_optimization", "vector_performance", "resource_utilization"],
  "introspection_depth": "deep"
}'

# Perform introspective analysis
use_mcp_tool safla analyze_system_introspection '{
  "analysis_type": "performance",
  "time_window_hours": 24
}'
```

#### Goal-Driven Memory Optimization
```bash
# Create memory optimization goal
use_mcp_tool safla create_goal '{
  "goal_name": "memory_efficiency_improvement",
  "description": "Improve memory efficiency by 20% while maintaining performance",
  "priority": "high",
  "target_metrics": {
    "memory_usage_reduction": 0.2,
    "latency_threshold": "100ms",
    "throughput_minimum": 500
  },
  "deadline": 1735689600
}'

# Evaluate goal progress
use_mcp_tool safla evaluate_goal_progress '{
  "goal_id": "memory_efficiency_improvement",
  "include_recommendations": true
}'
```

## Integration and Testing Examples

### Example 9: Memory Integration Testing

#### Comprehensive Integration Tests
```bash
# Run memory-focused integration tests
use_mcp_tool safla run_integration_tests '{
  "test_suite": "memory_integration",
  "parallel": true,
  "verbose": true
}'

# Test MCP connectivity for memory operations
use_mcp_tool safla test_mcp_connectivity '{
  "target_server": "safla",
  "test_depth": "comprehensive"
}'

# Validate memory operations in integrated environment
use_mcp_tool safla validate_memory_operations '{
  "test_data_size": 500,
  "include_stress_test": true
}'
```

#### Performance Regression Testing
```bash
# Run memory performance regression tests
python -m safla.regression --memory \
  --baseline-period 7d \
  --current-period 1d \
  --metrics latency,throughput,memory_usage \
  --statistical-significance 0.05 \
  --export regression_report.json

# Benchmark memory performance against baselines
python -m safla.benchmark --memory \
  --regression-mode \
  --baseline-file memory_baseline.json \
  --export regression_benchmark.json
```

### Example 10: Backup and Recovery

#### Memory State Backup
```bash
# Backup memory configuration and state
use_mcp_tool safla backup_safla_data '{
  "backup_type": "full",
  "destination": "/backups/memory_state_backup.tar.gz",
  "compress": true
}'

# Backup memory patterns and learning data
python -m safla.backup --memory-patterns \
  --learning-data \
  --destination /backups/memory_learning_backup.json \
  --compress

# Check backup status
access_mcp_resource safla "safla://backup-status"
```

#### Memory State Restoration
```bash
# Restore memory configuration from backup
use_mcp_tool safla restore_safla_data '{
  "backup_path": "/backups/memory_state_backup.tar.gz",
  "restore_type": "full",
  "verify_integrity": true
}'

# Restore memory patterns and learning data
python -m safla.restore --memory-patterns \
  --learning-data \
  --source /backups/memory_learning_backup.json \
  --verify-integrity

# Validate restoration
use_mcp_tool safla validate_memory_operations '{
  "test_data_size": 100,
  "include_stress_test": false
}'
```

## Maintenance and Automation Examples

### Example 11: Automated Memory Maintenance

#### Daily Memory Maintenance Script
```bash
#!/bin/bash
# Daily memory maintenance automation

# Health check
python -m safla.memory --health-check \
  --comprehensive \
  --export-report daily_memory_health.json

# Get health score
health_score=$(cat daily_memory_health.json | jq '.overall_score')

# Optimize if health score is below threshold
if (( $(echo "$health_score < 80" | bc -l) )); then
  echo "Memory health below threshold, optimizing..."
  
  use_mcp_tool safla optimize_memory_usage '{
    "optimization_level": "balanced",
    "target_memory_mb": 4096
  }'
  
  # Validate optimization
  use_mcp_tool safla validate_memory_operations '{
    "test_data_size": 100,
    "include_stress_test": false
  }'
fi

# Update learning from daily patterns
use_mcp_tool safla trigger_learning_cycle '{
  "learning_type": "incremental",
  "data_sources": ["daily_metrics"],
  "focus_areas": ["routine_optimization"]
}'

echo "Daily memory maintenance completed"
```

#### Weekly Performance Optimization
```bash
#!/bin/bash
# Weekly memory performance optimization

# Comprehensive performance analysis
use_mcp_tool safla analyze_performance_bottlenecks '{
  "duration_seconds": 3600,
  "include_memory_profile": true
}'

# Benchmark current performance
python -m safla.benchmark --comprehensive \
  --memory \
  --vectors \
  --export weekly_benchmark.json

# Get optimization recommendations
access_mcp_resource safla "safla://optimization-recommendations"

# Apply vector optimizations
use_mcp_tool safla optimize_vector_operations '{
  "batch_size": 250,
  "use_gpu": true
}'

# Apply memory optimizations
use_mcp_tool safla optimize_memory_usage '{
  "optimization_level": "balanced"
}'

# Validate optimization effectiveness
python -m safla.benchmark --memory \
  --compare-with weekly_baseline.json \
  --export weekly_optimized.json

echo "Weekly optimization cycle completed"
```

### Example 12: Emergency Memory Recovery

#### Critical Memory Issue Response
```bash
#!/bin/bash
# Emergency memory recovery procedure

echo "Emergency memory recovery initiated"

# Immediate memory optimization
use_mcp_tool safla optimize_memory_usage '{
  "optimization_level": "aggressive",
  "target_memory_mb": 2048
}'

# Force garbage collection and cleanup
python -m safla.memory --emergency-cleanup \
  --force-gc \
  --clear-caches \
  --compact-memory

# Validate system stability
use_mcp_tool safla validate_memory_operations '{
  "test_data_size": 10,
  "include_stress_test": false
}'

# Monitor recovery progress
python -m safla.monitor --memory \
  --emergency-mode \
  --duration 300 \
  --real-time \
  --alerts critical

# Scale down if necessary
use_mcp_tool safla scale_deployment '{
  "instance_name": "current_instance",
  "scale_factor": 0.7,
  "resource_type": "memory"
}'

echo "Emergency recovery procedures completed"
```

## Resource Access Examples

### Example 13: Real-time Resource Monitoring

#### Accessing SAFLA Resources
```bash
# Monitor performance metrics
access_mcp_resource safla "safla://performance-metrics"

# Check system status
access_mcp_resource safla "safla://status"

# View optimization recommendations
access_mcp_resource safla "safla://optimization-recommendations"

# Access learning metrics
access_mcp_resource safla "safla://learning-metrics"

# View adaptation patterns
access_mcp_resource safla "safla://adaptation-patterns"
```

#### Resource-Based Decision Making
```bash
# Get current performance metrics
metrics=$(access_mcp_resource safla "safla://performance-metrics")

# Parse memory usage from metrics
memory_usage=$(echo "$metrics" | jq '.memory.usage_percentage')

# Make optimization decisions based on resource data
if (( $(echo "$memory_usage > 85" | bc -l) )); then
  echo "High memory usage detected, triggering optimization"
  
  use_mcp_tool safla optimize_memory_usage '{
    "optimization_level": "aggressive"
  }'
fi

# Check optimization recommendations
recommendations=$(access_mcp_resource safla "safla://optimization-recommendations")
echo "Current recommendations: $recommendations"
```

These examples demonstrate practical usage of the Memory Manager mode through MCP tools and CLI commands, focusing on real-world memory management scenarios without direct code implementation.