# CLI Commands for Memory Manager Mode

## Memory Optimization Commands

### Memory Usage Optimization
```bash
# Conservative memory optimization for production
python -m safla.memory --optimize \
  --level conservative \
  --target-memory 6144 \
  --preserve-performance

# Balanced optimization for general use
python -m safla.memory --optimize \
  --level balanced \
  --target-memory 4096 \
  --monitor-impact

# Aggressive optimization for resource-constrained environments
python -m safla.memory --optimize \
  --level aggressive \
  --target-memory 2048 \
  --accept-performance-trade-offs
```

### Vector Operations Optimization
```bash
# Optimize vector operations with GPU acceleration
python -m safla.vectors --optimize \
  --batch-size 200 \
  --use-gpu \
  --dimensions 768 \
  --cache-strategy lru

# Memory-constrained vector optimization
python -m safla.vectors --optimize \
  --batch-size 50 \
  --no-gpu \
  --dimensions 512 \
  --memory-limit 2048

# High-throughput vector processing
python -m safla.vectors --optimize \
  --batch-size 500 \
  --use-gpu \
  --parallel-workers 4 \
  --cache-size 4096
```

## Memory Analysis and Profiling

### Memory Usage Analysis
```bash
# Comprehensive memory analysis
python -m safla.memory --analyze \
  --duration 300 \
  --include-profiling \
  --track-allocations \
  --export-report memory_analysis.json

# Quick memory health check
python -m safla.memory --health-check \
  --basic-metrics \
  --alert-thresholds cpu:80,memory:85,swap:50

# Memory leak detection
python -m safla.memory --leak-detection \
  --duration 600 \
  --sampling-rate 1000 \
  --track-objects \
  --generate-flamegraph
```

### Performance Bottleneck Analysis
```bash
# Identify memory bottlenecks
python -m safla.debug --bottlenecks \
  --focus memory \
  --duration 180 \
  --include-stack-traces \
  --export-flamegraph

# Vector operation bottleneck analysis
python -m safla.vectors --analyze-bottlenecks \
  --operations add,search,update \
  --vector-count 10000 \
  --dimensions 768 \
  --detailed-timing

# Cache performance analysis
python -m safla.cache --analyze \
  --hit-rate-target 0.9 \
  --eviction-analysis \
  --access-pattern-detection \
  --optimization-recommendations
```

## Memory Benchmarking

### Vector Operations Benchmarking
```bash
# Standard vector benchmarking
python -m safla.benchmark --vectors \
  --count 5000 \
  --dimensions 512 \
  --operations add,search,update,delete \
  --iterations 10 \
  --export-results vector_benchmark.json

# High-dimensional vector benchmarking
python -m safla.benchmark --vectors \
  --count 1000 \
  --dimensions 1536 \
  --operations search,similarity \
  --gpu-acceleration \
  --batch-sizes 50,100,200

# Vector similarity benchmarking
python -m safla.benchmark --vector-similarity \
  --dataset-size 50000 \
  --query-count 1000 \
  --similarity-metrics cosine,euclidean,dot \
  --top-k 10,50,100
```

### Memory Subsystem Benchmarking
```bash
# Comprehensive memory benchmarking
python -m safla.benchmark --memory \
  --duration 300 \
  --patterns sequential,random,mixed,stride \
  --block-sizes 4k,64k,1m,16m \
  --read-write-ratios 100:0,80:20,50:50

# Cache benchmarking
python -m safla.benchmark --cache \
  --cache-sizes 256,512,1024,2048 \
  --access-patterns temporal,spatial,random \
  --eviction-policies lru,lfu,arc \
  --workload-types read-heavy,write-heavy,mixed

# Memory bandwidth benchmarking
python -m safla.benchmark --bandwidth \
  --test-types copy,scale,add,triad \
  --array-sizes 1m,10m,100m \
  --iterations 100 \
  --numa-awareness
```

## Memory Validation and Testing

### Memory Operations Validation
```bash
# Standard memory validation
python -m safla.memory --validate \
  --test-size 50 \
  --integrity-checks \
  --consistency-validation \
  --export-report validation_report.json

# Stress testing with validation
python -m safla.memory --stress-test \
  --duration 600 \
  --memory-pressure high \
  --concurrent-operations 100 \
  --validate-integrity

# Vector data validation
python -m safla.vectors --validate \
  --vector-count 10000 \
  --dimensions 768 \
  --check-corruption \
  --verify-indexes \
  --consistency-checks
```

### Memory Integrity Testing
```bash
# Data integrity verification
python -m safla.memory --integrity \
  --full-scan \
  --checksum-validation \
  --cross-reference-checks \
  --repair-corruption

# Memory consistency testing
python -m safla.memory --consistency \
  --multi-threaded-access \
  --concurrent-modifications \
  --isolation-testing \
  --deadlock-detection

# Recovery testing
python -m safla.memory --recovery-test \
  --simulate-failures \
  --test-backup-restore \
  --validate-recovery-time \
  --data-loss-assessment
```

## Agent Management for Memory Operations

### Memory Agent Creation and Management
```bash
# Create vector optimization agent
python -m safla.agents --create \
  --type memory \
  --config focus:vector_optimization,dimensions:768,batch_size:100 \
  --timeout 3600 \
  --name vector_optimizer

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

# Create cache management agent
python -m safla.agents --create \
  --type memory \
  --config focus:cache_management,size:2048,policy:adaptive \
  --timeout 1800 \
  --name cache_manager
```

### Memory Agent Interactions
```bash
# Optimize vector storage
python -m safla.agents --interact \
  --session vector_optimizer \
  --command optimize_storage \
  --params vector_count:10000,compression:balanced,index_type:hnsw

# Analyze memory patterns
python -m safla.agents --interact \
  --session memory_profiler \
  --command analyze_patterns \
  --params time_window:24h,pattern_types:allocation,access,deallocation

# Optimize cache configuration
python -m safla.agents --interact \
  --session cache_manager \
  --command optimize_cache \
  --params hit_rate_target:0.95,max_memory:4096,strategy:adaptive
```

## Memory Monitoring and Alerting

### Real-Time Memory Monitoring
```bash
# Continuous memory monitoring
python -m safla.monitor --memory \
  --real-time \
  --interval 10 \
  --thresholds usage:85,fragmentation:30,leak_rate:1mb_per_hour \
  --alerts email,slack,webhook

# Vector operations monitoring
python -m safla.monitor --vectors \
  --operations-per-second \
  --latency-percentiles 50,95,99 \
  --error-rates \
  --queue-depths \
  --dashboard-export

# Cache performance monitoring
python -m safla.monitor --cache \
  --hit-rates \
  --eviction-rates \
  --memory-usage \
  --access-patterns \
  --optimization-opportunities
```

### Memory Health Dashboards
```bash
# Generate memory health dashboard
python -m safla.dashboard --memory \
  --metrics usage,performance,errors,trends \
  --time-range 24h \
  --export-format html,json \
  --auto-refresh 30s

# Vector operations dashboard
python -m safla.dashboard --vectors \
  --operations add,search,update,delete \
  --performance-metrics \
  --error-analysis \
  --capacity-planning \
  --export dashboard_vectors.html

# Memory optimization dashboard
python -m safla.dashboard --optimization \
  --before-after-comparisons \
  --optimization-impact \
  --recommendations \
  --cost-benefit-analysis
```

## Memory Configuration and Tuning

### Memory Configuration Management
```bash
# Export memory configuration
python -m safla.config --export \
  --section memory \
  --format yaml \
  --output memory_config.yaml \
  --include-defaults

# Import optimized configuration
python -m safla.config --import \
  --file optimized_memory_config.yaml \
  --validate \
  --backup-current \
  --apply-gradually

# Tune memory parameters
python -m safla.config --tune \
  --parameter cache_size:2048 \
  --parameter vector_batch_size:150 \
  --parameter gc_threshold:0.8 \
  --test-impact \
  --rollback-on-degradation
```

### Auto-Tuning and Optimization
```bash
# Auto-tune memory parameters
python -m safla.memory --auto-tune \
  --workload-profile production \
  --optimization-goals performance,efficiency \
  --safety-constraints conservative \
  --duration 3600

# Adaptive memory management
python -m safla.memory --adaptive \
  --learning-enabled \
  --adaptation-rate 0.1 \
  --performance-targets latency:50ms,throughput:1000ops \
  --continuous-optimization

# Memory capacity planning
python -m safla.memory --capacity-planning \
  --growth-projections 6months,1year,2years \
  --usage-patterns historical \
  --scaling-recommendations \
  --cost-analysis
```

## Backup and Recovery Operations

### Memory State Backup
```bash
# Create memory state backup
python -m safla.backup --memory \
  --include-vectors \
  --include-cache \
  --include-indexes \
  --destination /backup/memory \
  --compression gzip

# Incremental memory backup
python -m safla.backup --memory \
  --incremental \
  --since-last-backup \
  --verify-integrity \
  --destination /backup/memory/incremental

# Vector database backup
python -m safla.backup --vectors \
  --full-export \
  --include-metadata \
  --verify-checksums \
  --destination /backup/vectors
```

### Memory Recovery Operations
```bash
# Restore memory state
python -m safla.restore --memory \
  --backup-path /backup/memory/full_backup.tar.gz \
  --verify-integrity \
  --test-functionality \
  --rollback-on-failure

# Partial memory recovery
python -m safla.restore --memory \
  --backup-path /backup/memory/vectors_only.tar.gz \
  --components vectors,indexes \
  --preserve-existing-cache \
  --validate-consistency

# Emergency memory recovery
python -m safla.restore --memory \
  --emergency-mode \
  --backup-path /backup/memory/emergency_backup.tar.gz \
  --skip-validation \
  --force-restore
```

## Troubleshooting and Diagnostics

### Memory Diagnostics
```bash
# Comprehensive memory diagnostics
python -m safla.diagnose --memory \
  --full-analysis \
  --include-system-memory \
  --check-fragmentation \
  --analyze-allocators \
  --export-report diagnostics.json

# Memory leak diagnostics
python -m safla.diagnose --memory-leaks \
  --track-allocations \
  --stack-traces \
  --object-tracking \
  --growth-analysis \
  --leak-sources

# Vector operation diagnostics
python -m safla.diagnose --vectors \
  --operation-analysis \
  --index-health \
  --corruption-detection \
  --performance-regression \
  --optimization-opportunities
```

### Performance Regression Analysis
```bash
# Detect memory performance regressions
python -m safla.regression --memory \
  --baseline-period 7d \
  --current-period 1d \
  --metrics latency,throughput,error_rate \
  --statistical-significance 0.05

# Vector performance regression analysis
python -m safla.regression --vectors \
  --operations add,search,update \
  --baseline-benchmarks baseline_vectors.json \
  --current-benchmarks current_vectors.json \
  --regression-threshold 10%

# Memory efficiency regression
python -m safla.regression --efficiency \
  --memory-usage-trends \
  --optimization-effectiveness \
  --resource-utilization \
  --cost-per-operation