# SAFLA SDK Guide

## Overview

The SAFLA SDK provides a comprehensive toolkit for building autonomous systems with self-aware feedback loops. This guide covers integration patterns, best practices, and practical usage scenarios for implementing SAFLA in your applications.

## Quick Start

### Installation

```bash
pip install safla
```

### Basic Setup

```python
import asyncio
from safla.core.meta_cognitive_engine import MetaCognitiveEngine
from safla.core.delta_evaluation import DeltaEvaluator
from safla.core.hybrid_memory import HybridMemoryArchitecture
from safla.core.safety_validation import SafetyValidationFramework

async def main():
    # Initialize core components
    engine = MetaCognitiveEngine()
    delta_evaluator = DeltaEvaluator()
    memory = HybridMemoryArchitecture()
    safety = SafetyValidationFramework()
    
    # Initialize the system
    await engine.initialize()
    
    # Your application logic here
    
if __name__ == "__main__":
    asyncio.run(main())
```

## Core Integration Patterns

### 1. Meta-Cognitive Engine Integration

The meta-cognitive engine is the heart of SAFLA. Here's how to integrate it effectively:

#### Basic Integration

```python
from safla.core.meta_cognitive_engine import (
    MetaCognitiveEngine, 
    SystemState, 
    Goal, 
    Strategy,
    PerformanceMetrics
)

class MyAutonomousSystem:
    def __init__(self):
        self.engine = MetaCognitiveEngine({
            'adaptation_threshold': 0.1,
            'confidence_threshold': 0.8,
            'max_strategies': 5
        })
        
    async def initialize(self):
        """Initialize the autonomous system."""
        await self.engine.initialize()
        
        # Define initial goals
        goal = Goal(
            goal_id="optimize_performance",
            description="Optimize system performance metrics",
            target_metrics={"accuracy": 0.95, "efficiency": 0.90},
            priority=1,
            success_criteria={"min_accuracy": 0.90}
        )
        
        await self.engine.add_goal(goal)
        
    async def process_feedback_loop(self, external_feedback: dict):
        """Process feedback through the meta-cognitive loop."""
        # Get current system state
        current_state = self.engine.get_system_state()
        
        # Process feedback
        adaptation_result = await self.engine.process_feedback(external_feedback)
        
        # Check if adaptation was successful
        if adaptation_result.success:
            print(f"Adaptation successful: {adaptation_result.changes_made}")
        else:
            print(f"Adaptation failed: {adaptation_result.metadata.get('error')}")
            
        return adaptation_result
```

#### Advanced Strategy Management

```python
class AdvancedStrategyManager:
    def __init__(self, engine: MetaCognitiveEngine):
        self.engine = engine
        
    async def create_adaptive_strategy(self, context: dict) -> Strategy:
        """Create a strategy that adapts to current context."""
        strategy = Strategy(
            strategy_id=f"adaptive_{int(time.time())}",
            name="Adaptive Performance Strategy",
            description="Strategy that adapts based on current performance",
            actions=[
                {
                    "type": "parameter_adjustment",
                    "parameters": self._calculate_optimal_parameters(context)
                },
                {
                    "type": "resource_reallocation",
                    "allocation": self._optimize_resource_allocation(context)
                }
            ],
            expected_outcomes={"performance_gain": 0.15},
            confidence_score=0.85,
            resource_requirements={"cpu": 0.3, "memory": 0.2}
        )
        
        return strategy
        
    def _calculate_optimal_parameters(self, context: dict) -> dict:
        """Calculate optimal parameters based on context."""
        # Your parameter optimization logic here
        return {"learning_rate": 0.01, "batch_size": 32}
        
    def _optimize_resource_allocation(self, context: dict) -> dict:
        """Optimize resource allocation based on context."""
        # Your resource optimization logic here
        return {"worker_threads": 4, "memory_pool": "2GB"}
```

### 2. Delta Evaluation Integration

The delta evaluation system quantifies improvements using the SAFLA formula:

```python
from safla.core.delta_evaluation import DeltaEvaluator, AdaptiveWeights, PerformanceMetrics

class PerformanceTracker:
    def __init__(self):
        # Initialize with custom weights
        weights = AdaptiveWeights(
            alpha_1=0.3,  # Performance weight
            alpha_2=0.25, # Efficiency weight
            alpha_3=0.25, # Stability weight
            alpha_4=0.2   # Capability weight
        )
        self.delta_evaluator = DeltaEvaluator(weights)
        self.previous_metrics = None
        
    async def track_performance(self, current_data: dict) -> float:
        """Track performance and calculate delta improvements."""
        # Convert current data to PerformanceMetrics
        current_metrics = PerformanceMetrics(
            timestamp=time.time(),
            accuracy=current_data.get('accuracy', 0.0),
            efficiency=current_data.get('efficiency', 0.0),
            stability=current_data.get('stability', 0.0),
            capability_score=current_data.get('capability', 0.0),
            custom_metrics=current_data.get('custom', {})
        )
        
        if self.previous_metrics:
            # Calculate delta improvement
            delta_result = self.delta_evaluator.calculate_delta(
                current_metrics, 
                self.previous_metrics
            )
            
            print(f"Total Delta: {delta_result.total_delta}")
            print(f"Component Deltas: {delta_result.component_deltas}")
            
            # Adapt weights based on results
            if delta_result.total_delta > 0.1:
                await self._adapt_weights(delta_result)
                
            self.previous_metrics = current_metrics
            return delta_result.total_delta
        else:
            self.previous_metrics = current_metrics
            return 0.0
            
    async def _adapt_weights(self, delta_result):
        """Adapt weights based on delta results."""
        # Example adaptive weight adjustment
        new_weights = AdaptiveWeights(
            alpha_1=min(0.4, delta_result.weights_used.alpha_1 + 0.05),
            alpha_2=delta_result.weights_used.alpha_2,
            alpha_3=delta_result.weights_used.alpha_3,
            alpha_4=delta_result.weights_used.alpha_4
        )
        self.delta_evaluator.update_weights(new_weights)
```

### 3. Hybrid Memory Integration

The hybrid memory system provides sophisticated memory management:

```python
from safla.core.hybrid_memory import HybridMemoryArchitecture, MemoryItem

class IntelligentMemoryManager:
    def __init__(self):
        self.memory = HybridMemoryArchitecture({
            'vector_dimension': 768,
            'max_episodic_memories': 10000,
            'semantic_consolidation_threshold': 0.8,
            'working_memory_capacity': 100
        })
        
    async def store_experience(self, experience: dict) -> str:
        """Store an experience in the appropriate memory system."""
        # Determine memory type based on experience characteristics
        if experience.get('type') == 'immediate':
            memory_type = 'working'
        elif experience.get('importance', 0.5) > 0.8:
            memory_type = 'episodic'
        elif experience.get('generalizable', False):
            memory_type = 'semantic'
        else:
            memory_type = 'vector'
            
        # Store the experience
        memory_id = await self.memory.store_memory(
            content=experience,
            memory_type=memory_type,
            metadata={
                'importance': experience.get('importance', 0.5),
                'context': experience.get('context', {}),
                'tags': experience.get('tags', [])
            }
        )
        
        return memory_id
        
    async def retrieve_relevant_memories(self, query: dict, max_results: int = 5):
        """Retrieve memories relevant to a query."""
        # Convert query to vector (you'll need your own embedding function)
        query_vector = await self._embed_query(query)
        
        # Search for similar memories
        similar_memories = await self.memory.search_similar(
            query_vector=query_vector,
            memory_type='vector',
            max_results=max_results,
            similarity_threshold=0.7
        )
        
        return similar_memories
        
    async def _embed_query(self, query: dict) -> list:
        """Convert query to embedding vector."""
        # Implement your embedding logic here
        # This could use transformers, sentence-transformers, etc.
        return [0.1] * 768  # Placeholder
```

### 4. Safety Validation Integration

Safety is paramount in autonomous systems:

```python
from safla.core.safety_validation import SafetyValidationFramework, ValidationResult

class SafeAutonomousSystem:
    def __init__(self):
        self.safety = SafetyValidationFramework({
            'risk_tolerance': 'low',
            'validation_strictness': 'high',
            'checkpoint_frequency': 300  # 5 minutes
        })
        self.checkpoint_history = []
        
    async def execute_safe_action(self, action: dict) -> bool:
        """Execute an action with safety validation."""
        # Create checkpoint before action
        checkpoint_id = await self.safety.create_checkpoint(
            f"Before action: {action.get('type', 'unknown')}"
        )
        self.checkpoint_history.append(checkpoint_id)
        
        try:
            # Validate action safety
            validation_result = await self.safety.validate_action(action)
            
            if not validation_result.is_valid:
                print(f"Action rejected: {validation_result.violations}")
                return False
                
            # Assess risk
            risk_assessment = await self.safety.assess_risk(
                context=await self._get_current_context(),
                proposed_action=action
            )
            
            if risk_assessment.overall_risk_score > 0.7:
                print(f"High risk detected: {risk_assessment.risk_categories}")
                
                # Apply mitigation strategies
                for strategy in risk_assessment.mitigation_strategies:
                    await self._apply_mitigation(strategy)
                    
            # Execute the action
            result = await self._execute_action(action)
            
            # Validate post-execution state
            post_validation = await self._validate_post_execution()
            
            if not post_validation:
                # Rollback if post-execution validation fails
                await self.safety.rollback_to_checkpoint(checkpoint_id)
                return False
                
            return result
            
        except Exception as e:
            # Rollback on any exception
            print(f"Exception during action execution: {e}")
            await self.safety.rollback_to_checkpoint(checkpoint_id)
            return False
            
    async def _get_current_context(self) -> dict:
        """Get current system context for risk assessment."""
        return {
            'system_load': 0.6,
            'active_processes': 15,
            'memory_usage': 0.7,
            'network_status': 'stable'
        }
        
    async def _apply_mitigation(self, strategy: str):
        """Apply a risk mitigation strategy."""
        if strategy == "reduce_concurrency":
            # Reduce concurrent operations
            pass
        elif strategy == "increase_monitoring":
            # Increase monitoring frequency
            pass
            
    async def _execute_action(self, action: dict) -> bool:
        """Execute the actual action."""
        # Your action execution logic here
        return True
        
    async def _validate_post_execution(self) -> bool:
        """Validate system state after action execution."""
        # Your post-execution validation logic here
        return True
```

### 5. MCP Orchestration Integration

For distributed systems with multiple agents:

```python
from safla.core.mcp_orchestration import MCPOrchestrator, MCPServer, Agent

class DistributedSAFLASystem:
    def __init__(self):
        self.orchestrator = MCPOrchestrator()
        self.agents = {}
        
    async def initialize_distributed_system(self):
        """Initialize the distributed SAFLA system."""
        # Start the orchestrator
        await self.orchestrator.start()
        
        # Register MCP servers
        servers = [
            MCPServer(
                server_id="analysis_server",
                name="Data Analysis Server",
                endpoint="http://localhost:8001",
                capabilities=["data_analysis", "pattern_recognition"],
                status="active"
            ),
            MCPServer(
                server_id="optimization_server",
                name="Optimization Server", 
                endpoint="http://localhost:8002",
                capabilities=["optimization", "parameter_tuning"],
                status="active"
            )
        ]
        
        for server in servers:
            self.orchestrator.server_manager.register_server(server)
            
        # Register agents
        agents = [
            Agent(
                agent_id="analyzer_agent",
                name="Data Analyzer",
                agent_type="analysis",
                capabilities=["data_analysis"],
                status="active",
                server_id="analysis_server",
                priority=2
            ),
            Agent(
                agent_id="optimizer_agent",
                name="System Optimizer",
                agent_type="optimization", 
                capabilities=["optimization"],
                status="active",
                server_id="optimization_server",
                priority=1
            )
        ]
        
        for agent in agents:
            self.orchestrator.agent_coordinator.register_agent(agent)
            self.agents[agent.agent_id] = agent
            
    async def process_distributed_request(self, request: dict):
        """Process a request through the distributed system."""
        # Add request metadata
        request.update({
            "request_id": f"req_{int(time.time())}",
            "timestamp": time.time()
        })
        
        # Process through orchestrator
        response = await self.orchestrator.process_request(request)
        
        return response
        
    async def get_system_health(self):
        """Get comprehensive system health status."""
        return self.orchestrator.get_system_status()
```

## Best Practices

### 1. Configuration Management

```python
import os
from typing import Dict, Any

class SAFLAConfig:
    """Centralized configuration management for SAFLA systems."""
    
    @staticmethod
    def get_default_config() -> Dict[str, Any]:
        return {
            'meta_cognitive': {
                'adaptation_threshold': float(os.getenv('SAFLA_ADAPTATION_THRESHOLD', '0.1')),
                'confidence_threshold': float(os.getenv('SAFLA_CONFIDENCE_THRESHOLD', '0.8')),
                'max_strategies': int(os.getenv('SAFLA_MAX_STRATEGIES', '5'))
            },
            'delta_evaluation': {
                'alpha_1': float(os.getenv('SAFLA_ALPHA_1', '0.25')),
                'alpha_2': float(os.getenv('SAFLA_ALPHA_2', '0.25')),
                'alpha_3': float(os.getenv('SAFLA_ALPHA_3', '0.25')),
                'alpha_4': float(os.getenv('SAFLA_ALPHA_4', '0.25'))
            },
            'memory': {
                'vector_dimension': int(os.getenv('SAFLA_VECTOR_DIM', '512')),
                'max_episodic_memories': int(os.getenv('SAFLA_MAX_EPISODIC', '10000')),
                'consolidation_threshold': float(os.getenv('SAFLA_CONSOLIDATION_THRESHOLD', '0.8'))
            },
            'safety': {
                'risk_tolerance': os.getenv('SAFLA_RISK_TOLERANCE', 'medium'),
                'validation_strictness': os.getenv('SAFLA_VALIDATION_STRICTNESS', 'high'),
                'checkpoint_frequency': int(os.getenv('SAFLA_CHECKPOINT_FREQ', '300'))
            }
        }
```

### 2. Error Handling and Logging

```python
import logging
from typing import Optional
from functools import wraps

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

def safla_error_handler(fallback_value=None):
    """Decorator for consistent error handling in SAFLA operations."""
    def decorator(func):
        @wraps(func)
        async def wrapper(*args, **kwargs):
            try:
                return await func(*args, **kwargs)
            except Exception as e:
                logger = logging.getLogger(func.__module__)
                logger.error(f"Error in {func.__name__}: {e}", exc_info=True)
                
                # Return fallback value or re-raise based on criticality
                if fallback_value is not None:
                    return fallback_value
                else:
                    raise
        return wrapper
    return decorator

class SAFLALogger:
    """Specialized logger for SAFLA operations."""
    
    def __init__(self, component_name: str):
        self.logger = logging.getLogger(f"safla.{component_name}")
        
    def log_adaptation(self, adaptation_result):
        """Log adaptation results."""
        self.logger.info(
            f"Adaptation {adaptation_result.adaptation_id}: "
            f"Success={adaptation_result.success}, "
            f"Delta={adaptation_result.performance_impact}"
        )
        
    def log_safety_violation(self, violation):
        """Log safety violations."""
        self.logger.warning(
            f"Safety violation: {violation.constraint_type} - {violation.description}"
        )
        
    def log_memory_operation(self, operation: str, memory_id: str, memory_type: str):
        """Log memory operations."""
        self.logger.debug(f"Memory {operation}: {memory_id} ({memory_type})")
```

### 3. Testing Patterns

```python
import unittest
from unittest.mock import AsyncMock, MagicMock
import asyncio

class TestSAFLAIntegration(unittest.TestCase):
    """Test patterns for SAFLA integration."""
    
    def setUp(self):
        """Set up test environment."""
        self.loop = asyncio.new_event_loop()
        asyncio.set_event_loop(self.loop)
        
    def tearDown(self):
        """Clean up test environment."""
        self.loop.close()
        
    def test_meta_cognitive_engine_initialization(self):
        """Test meta-cognitive engine initialization."""
        async def run_test():
            engine = MetaCognitiveEngine()
            result = await engine.initialize()
            self.assertTrue(result)
            
        self.loop.run_until_complete(run_test())
        
    def test_delta_evaluation_calculation(self):
        """Test delta evaluation calculation."""
        evaluator = DeltaEvaluator()
        
        current_metrics = PerformanceMetrics(
            timestamp=time.time(),
            accuracy=0.95,
            efficiency=0.90,
            stability=0.85,
            capability_score=0.88
        )
        
        previous_metrics = PerformanceMetrics(
            timestamp=time.time() - 100,
            accuracy=0.90,
            efficiency=0.85,
            stability=0.80,
            capability_score=0.83
        )
        
        result = evaluator.calculate_delta(current_metrics, previous_metrics)
        self.assertGreater(result.total_delta, 0)
        
    def test_safety_validation(self):
        """Test safety validation."""
        async def run_test():
            safety = SafetyValidationFramework()
            
            action = {
                "type": "parameter_update",
                "parameters": {"learning_rate": 0.01}
            }
            
            result = await safety.validate_action(action)
            self.assertIsInstance(result, ValidationResult)
            
        self.loop.run_until_complete(run_test())
```

### 4. Performance Optimization

```python
import asyncio
from concurrent.futures import ThreadPoolExecutor
import time

class OptimizedSAFLASystem:
    """Performance-optimized SAFLA system implementation."""
    
    def __init__(self):
        self.executor = ThreadPoolExecutor(max_workers=4)
        self.cache = {}
        self.cache_ttl = 300  # 5 minutes
        
    async def cached_operation(self, operation_key: str, operation_func, *args, **kwargs):
        """Perform operation with caching."""
        current_time = time.time()
        
        # Check cache
        if operation_key in self.cache:
            cached_result, timestamp = self.cache[operation_key]
            if current_time - timestamp < self.cache_ttl:
                return cached_result
                
        # Perform operation
        if asyncio.iscoroutinefunction(operation_func):
            result = await operation_func(*args, **kwargs)
        else:
            # Run CPU-bound operations in thread pool
            result = await asyncio.get_event_loop().run_in_executor(
                self.executor, operation_func, *args, **kwargs
            )
            
        # Cache result
        self.cache[operation_key] = (result, current_time)
        return result
        
    async def batch_process_feedback(self, feedback_batch: list):
        """Process multiple feedback items in parallel."""
        tasks = []
        for feedback in feedback_batch:
            task = asyncio.create_task(self._process_single_feedback(feedback))
            tasks.append(task)
            
        results = await asyncio.gather(*tasks, return_exceptions=True)
        return results
        
    async def _process_single_feedback(self, feedback: dict):
        """Process a single feedback item."""
        # Your feedback processing logic here
        await asyncio.sleep(0.1)  # Simulate processing time
        return {"processed": True, "feedback_id": feedback.get("id")}
```

## Integration Examples

### Web Application Integration

```python
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import asyncio

app = FastAPI(title="SAFLA Web Service")

# Global SAFLA system instance
safla_system = None

class FeedbackRequest(BaseModel):
    data: dict
    context: dict = {}

@app.on_event("startup")
async def startup_event():
    """Initialize SAFLA system on startup."""
    global safla_system
    safla_system = MyAutonomousSystem()
    await safla_system.initialize()

@app.post("/feedback")
async def process_feedback(request: FeedbackRequest):
    """Process feedback through SAFLA system."""
    try:
        result = await safla_system.process_feedback_loop(request.data)
        return {
            "success": result.success,
            "adaptation_id": result.adaptation_id,
            "changes": result.changes_made
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/status")
async def get_system_status():
    """Get current system status."""
    state = safla_system.engine.get_system_state()
    return {
        "state_id": state.state_id,
        "confidence": state.confidence_level,
        "active_goals": state.active_goals,
        "performance": state.performance_metrics
    }
```

### Machine Learning Pipeline Integration

```python
import torch
import torch.nn as nn
from sklearn.metrics import accuracy_score

class SAFLAMLPipeline:
    """Machine learning pipeline with SAFLA integration."""
    
    def __init__(self, model: nn.Module):
        self.model = model
        self.safla_system = MyAutonomousSystem()
        self.performance_tracker = PerformanceTracker()
        
    async def train_with_safla(self, train_loader, val_loader, epochs: int):
        """Train model with SAFLA feedback loop."""
        await self.safla_system.initialize()
        
        for epoch in range(epochs):
            # Training phase
            train_loss = self._train_epoch(train_loader)
            
            # Validation phase
            val_loss, val_accuracy = self._validate_epoch(val_loader)
            
            # Calculate performance metrics
            performance_data = {
                'accuracy': val_accuracy,
                'efficiency': 1.0 / (train_loss + 1e-8),
                'stability': self._calculate_stability(),
                'capability': self._calculate_capability()
            }
            
            # Track performance and get delta
            delta = await self.performance_tracker.track_performance(performance_data)
            
            # Process through SAFLA if significant change
            if abs(delta) > 0.05:
                feedback = {
                    'epoch': epoch,
                    'delta': delta,
                    'metrics': performance_data,
                    'model_state': self._get_model_state()
                }
                
                adaptation = await self.safla_system.process_feedback_loop(feedback)
                
                if adaptation.success:
                    # Apply adaptations to model
                    await self._apply_model_adaptations(adaptation.changes_made)
                    
    def _train_epoch(self, train_loader):
        """Train for one epoch."""
        # Your training logic here
        return 0.1  # Mock loss
        
    def _validate_epoch(self, val_loader):
        """Validate for one epoch."""
        # Your validation logic here
        return 0.05, 0.95  # Mock loss and accuracy
        
    def _calculate_stability(self):
        """Calculate model stability metric."""
        # Your stability calculation here
        return 0.9
        
    def _calculate_capability(self):
        """Calculate model capability metric."""
        # Your capability calculation here
        return 0.85
        
    def _get_model_state(self):
        """Get current model state."""
        return {
            'parameters': len(list(self.model.parameters())),
            'architecture': str(self.model)
        }
        
    async def _apply_model_adaptations(self, changes: list):
        """Apply adaptations to the model."""
        for change in changes:
            if change['type'] == 'learning_rate_adjustment':
                # Adjust learning rate
                pass
            elif change['type'] == 'architecture_modification':
                # Modify architecture
                pass
```

## Troubleshooting

### Common Issues and Solutions

1. **Memory Leaks in Long-Running Systems**
   ```python
   # Implement periodic cleanup
   async def periodic_cleanup():
       while True:
           await asyncio.sleep(3600)  # Every hour
           memory.cleanup_expired_contexts()
           gc.collect()
   ```

2. **Performance Degradation**
   ```python
   # Monitor and optimize performance
   async def performance_monitor():
       while True:
           metrics = await collect_performance_metrics()
           if metrics['response_time'] > threshold:
               await optimize_system_performance()
   ```

3. **Safety Validation Failures**
   ```python
   # Implement graceful degradation
   if not validation_result.is_valid:
       await enable_safe_mode()
       await notify_administrators(validation_result.violations)
   ```

## Next Steps

- Review the [Configuration Guide](11-configuration.md) for detailed setup options
- Explore [Examples](12-examples.md) for more practical implementations
- Study [Algorithms](13-algorithms.md) for mathematical foundations
- Check the [API Reference](09-api-reference.md) for complete method documentation

## Support and Community

- GitHub Issues: Report bugs and request features
- Documentation: Comprehensive guides and tutorials
- Community Forum: Ask questions and share experiences
- Professional Support: Enterprise support options available