# Memory Manager Mode Workflow

## Overview

The Memory Manager mode workflow focuses on intelligent memory operations, vector storage optimization, and knowledge management using SAFLA's MCP tools and CLI commands. All workflows emphasize MCP tool usage and CLI operations rather than direct code implementation.

## Core Workflow Phases

### 1. Memory Assessment and Analysis

#### Initial Memory Analysis Using MCP Tools
```bash
# Analyze current memory performance bottlenecks
use_mcp_tool safla analyze_performance_bottlenecks '{
  "duration_seconds": 300,
  "include_memory_profile": true
}'

# Validate current memory operations
use_mcp_tool safla validate_memory_operations '{
  "test_data_size": 100,
  "include_stress_test": false
}'

# Get system information for memory planning
use_mcp_tool safla get_system_info '{}'
```

#### Memory Health Assessment Using CLI
```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
```

### 2. Memory Optimization Phase

#### Vector Operations Optimization
```bash
# Optimize vector operations using MCP
use_mcp_tool safla optimize_vector_operations '{
  "batch_size": 150,
  "use_gpu": true
}'

# CLI-based vector optimization
python -m safla.vectors --optimize \
  --batch-size 200 \
  --use-gpu \
  --dimensions 768 \
  --cache-strategy lru
```

#### General Memory Optimization
```bash
# Memory usage optimization via MCP
use_mcp_tool safla optimize_memory_usage '{
  "optimization_level": "balanced",
  "target_memory_mb": 4096
}'

# CLI memory optimization
python -m safla.memory --optimize \
  --level balanced \
  --target-memory 4096 \
  --monitor-impact
```

### 3. Performance Monitoring and Validation

#### Continuous Performance Monitoring
```bash
# Real-time 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

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

# Monitor system status
access_mcp_resource safla "safla://status"
```

#### Performance Validation
```bash
# Validate memory operations after optimization
use_mcp_tool safla validate_memory_operations '{
  "test_data_size": 200,
  "include_stress_test": true
}'

# Benchmark memory performance
use_mcp_tool safla benchmark_memory_performance '{
  "test_duration": 180,
  "memory_patterns": ["sequential", "random", "mixed"]
}'
```

## Agent-Based Memory Management Workflows

### 1. Memory Agent Creation and Coordination

#### Create Specialized Memory Agents
```bash
# Create vector optimization agent via MCP
use_mcp_tool safla create_agent_session '{
  "agent_type": "memory",
  "session_config": {
    "focus": "vector_optimization",
    "vector_dimensions": 768,
    "batch_size": 100,
    "optimization_level": "balanced"
  },
  "timeout_seconds": 3600
}'

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

#### Agent Interaction Workflows
```bash
# Interact with vector optimization agent
use_mcp_tool safla interact_with_agent '{
  "session_id": "memory_001",
  "command": "optimize_vector_storage",
  "parameters": {
    "vector_count": 10000,
    "compression_level": "balanced",
    "index_type": "hnsw"
  }
}'

# CLI agent interaction for memory analysis
python -m safla.agents --interact \
  --session memory_profiler \
  --command analyze_patterns \
  --params time_window:24h,pattern_types:allocation,access,deallocation
```

### 2. Multi-Agent Memory Coordination

#### Parallel Memory Operations
```bash
# Create multiple specialized agents
python -m safla.agents --batch \
  --create-config memory_agents.yaml \
  --coordinate \
  --monitor

# Coordinate agents for comprehensive memory management
python -m safla.agents --coordinate \
  --sessions memory_001,memory_002,memory_003 \
  --task comprehensive_memory_optimization \
  --monitor-progress
```

## Benchmarking and Performance Workflows

### 1. Vector Operations Benchmarking

#### Comprehensive Vector Benchmarking
```bash
# Benchmark vector operations via MCP
use_mcp_tool safla benchmark_vector_operations '{
  "vector_count": 5000,
  "vector_dimensions": 768,
  "operations": ["add", "search", "update", "delete"]
}'

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

#### Performance Comparison Workflow
```bash
# Baseline benchmarking
python -m safla.benchmark --vectors \
  --count 1000 \
  --dimensions 768 \
  --baseline-mode \
  --export baseline_vectors.json

# Apply optimizations
use_mcp_tool safla optimize_vector_operations '{
  "batch_size": 200,
  "use_gpu": true
}'

# Post-optimization benchmarking
python -m safla.benchmark --vectors \
  --count 1000 \
  --dimensions 768 \
  --compare-with baseline_vectors.json \
  --export optimized_vectors.json
```

### 2. Memory Subsystem Benchmarking

#### Memory Performance Analysis
```bash
# Comprehensive memory benchmarking via MCP
use_mcp_tool safla benchmark_memory_performance '{
  "test_duration": 300,
  "memory_patterns": ["sequential", "random", "mixed", "stride"]
}'

# CLI 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
```

## Error Handling and Recovery Workflows

### 1. Memory Error Detection and Recovery

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

# Memory integrity validation
use_mcp_tool safla validate_memory_operations '{
  "test_data_size": 50,
  "include_stress_test": true
}'

# Access system logs for error analysis
access_mcp_resource safla "safla://system-logs"
```

#### Automatic Recovery Workflow
```bash
# Trigger memory optimization on error detection
if [ $memory_error_detected ]; then
  use_mcp_tool safla optimize_memory_usage '{
    "optimization_level": "conservative"
  }'
  
  # Re-validate after optimization
  use_mcp_tool safla validate_memory_operations '{
    "test_data_size": 10,
    "include_stress_test": false
  }'
fi
```

### 2. Performance Degradation Recovery

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

# Apply performance recovery optimizations
use_mcp_tool safla optimize_vector_operations '{
  "batch_size": 100,
  "use_gpu": true
}'

use_mcp_tool safla optimize_memory_usage '{
  "optimization_level": "aggressive",
  "target_memory_mb": 6144
}'

# Validate recovery
python -m safla.benchmark --memory \
  --duration 60 \
  --compare-with-baseline \
  --export recovery_validation.json
```

## Learning and Adaptation Workflows

### 1. Memory Pattern Learning

#### Usage Pattern Analysis
```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 based on patterns
use_mcp_tool safla update_learning_parameters '{
  "learning_rate": 0.15,
  "adaptation_threshold": 0.1,
  "memory_retention": 0.9
}'

# 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"]
}'
```

### 2. Adaptive Memory Management

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

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

# Adjust adaptation parameters based on results
use_mcp_tool safla update_learning_parameters '{
  "exploration_factor": 0.2,
  "adaptation_threshold": 0.08
}'
```

## Integration and Deployment Workflows

### 1. Memory System Integration

#### Integration Testing Workflow
```bash
# Test memory system integration
use_mcp_tool safla run_integration_tests '{
  "test_suite": "memory_integration",
  "parallel": true,
  "verbose": true
}'

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

# Monitor integration health
python -m safla.monitor --integration \
  --memory-systems \
  --real-time \
  --alerts
```

### 2. Production Deployment

#### Memory-Optimized Deployment
```bash
# Deploy with memory optimization
use_mcp_tool safla deploy_safla_instance '{
  "instance_name": "memory_optimized_production",
  "environment": "production",
  "config_overrides": {
    "memory_optimization": "enabled",
    "vector_batch_size": 200,
    "cache_size": 4096
  }
}'

# Monitor deployment memory performance
python -m safla.monitor --deployment memory_optimized_production \
  --memory-metrics \
  --real-time \
  --optimization-triggers

# Scale memory resources if needed
use_mcp_tool safla scale_deployment '{
  "instance_name": "memory_optimized_production",
  "scale_factor": 1.5,
  "resource_type": "memory"
}'
```

## Maintenance and Optimization Workflows

### 1. Routine Memory Maintenance

#### Daily Memory Maintenance
```bash
#!/bin/bash
# Daily memory maintenance script

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

# Optimize if needed
if [ $memory_health_score -lt 80 ]; then
  use_mcp_tool safla optimize_memory_usage '{
    "optimization_level": "balanced"
  }'
fi

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

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

### 2. Performance Optimization Cycles

#### Weekly Optimization Cycle
```bash
#!/bin/bash
# Weekly memory optimization cycle

# 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

# Apply learned optimizations
access_mcp_resource safla "safla://optimization-recommendations"

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