Phase 14: Enhanced performance optimizations with connection pooling and lazy loading

README.md

@@ -20,6 +20,7 @@ Un agente configurabile basato su DeepAgents che supporta più provider LLM.
 - **Comandi integrati**: Comandi speciali con prefisso "/" per gestione rapida
 - **Monitoraggio**: Sistema di logging e monitoraggio delle performance
 - **Dashboard performance**: Monitoraggio dettagliato delle performance
+- **Ottimizzazioni performance**: Caching, connection pooling, lazy loading
 - **Sistema di plugin**: Estendi le funzionalità con plugin personalizzati
 - **Plugin inclusi**: File tools, System utils, Example plugin
 - **Supporto multilingue**: Interfaccia in italiano e inglese
@@ -374,6 +375,41 @@ La dashboard include:
 
 La dashboard si aggiorna automaticamente ogni 30 secondi. Puoi anche aggiornare manualmente con il pulsante "🔄 Aggiorna Dati".
 
+## Ottimizzazioni Performance
+
+MaxClaw Agent include diverse ottimizzazioni per migliorare le performance:
+
+### Caching
+- **Configurazioni**: Le configurazioni vengono cachate per 30 secondi
+- **Provider tools**: Gli strumenti provider vengono cachati per 5 minuti
+- **Creazione agente**: L'agente viene cachato per 10 minuti
+- **Risposte API**: Le risposte API vengono cachate per 10 secondi
+
+### Connection Pooling
+- **Pool di connessioni**: Gestisce le connessioni ai provider
+- **Riutilizzo connessioni**: Riutilizza le connessioni esistenti
+- **Pulizia automatica**: Rimuove le connessioni inattive dopo 5 minuti
+
+### Lazy Loading
+- **Caricamento moduli**: I moduli vengono caricati solo quando necessari
+- **Caricamento strumenti**: Gli strumenti vengono caricati on-demand
+- **Risorse pesanti**: Le risorse pesanti vengono caricate in modo lazy
+
+### Batch Processing
+- **Elaborazione parallela**: Le operazioni vengono eseguite in parallelo
+- **Thread pool**: Utilizza un pool di thread per le operazioni concorrenti
+- **Timeout configurabile**: Timeout configurabile per le operazioni batch
+
+### Applicare le Ottimizzazioni
+
+```bash
+# Applica tutte le ottimizzazioni
+python performance.py
+
+# Visualizza il report delle performance
+python performance.py
+```
+
 ## Strumenti Unificati per Provider
 
 MaxClaw Agent ora fornisce un'interfaccia unificata per gestire qualsiasi provider LLM. Una volta configurato un provider, puoi usare gli stessi comandi per tutti.

performance.py

@@ -1,18 +1,209 @@
 """
 Performance optimizations for MaxClaw Agent
+Enhanced with connection pooling, lazy loading, and advanced caching
 """
 import os
 import sys
 import time
 import functools
-from typing import Dict, Any, Optional, Callable
+import threading
+from typing import Dict, Any, Optional, Callable, List
 from pathlib import Path
 import json
+from concurrent.futures import ThreadPoolExecutor, as_completed
 
 # Add current directory to path
 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
 
 
+class ConnectionPool:
+    """Connection pool for managing API connections"""
+    
+    def __init__(self, max_connections: int = 10):
+        """
+        Initialize connection pool
+        
+        Args:
+            max_connections: Maximum number of connections in the pool
+        """
+        self.max_connections = max_connections
+        self.connections = {}
+        self.lock = threading.Lock()
+    
+    def get_connection(self, provider: str, **kwargs):
+        """
+        Get a connection from the pool
+        
+        Args:
+            provider: Provider name
+            **kwargs: Connection parameters
+            
+        Returns:
+            Connection object
+        """
+        with self.lock:
+            key = f"{provider}:{str(sorted(kwargs.items()))}"
+            
+            if key not in self.connections:
+                # Create new connection
+                if provider == "ollama":
+                    import requests
+                    base_url = kwargs.get("base_url", "http://localhost:11434")
+                    session = requests.Session()
+                    session.headers.update({"Content-Type": "application/json"})
+                    self.connections[key] = {
+                        "session": session,
+                        "base_url": base_url,
+                        "last_used": time.time()
+                    }
+                elif provider in ["openai", "openrouter", "anthropic", "google"]:
+                    import requests
+                    session = requests.Session()
+                    api_key = kwargs.get("api_key", "")
+                    if api_key:
+                        session.headers.update({"Authorization": f"Bearer {api_key}"})
+                    self.connections[key] = {
+                        "session": session,
+                        "last_used": time.time()
+                    }
+            
+            # Update last used time
+            if key in self.connections:
+                self.connections[key]["last_used"] = time.time()
+            
+            return self.connections.get(key)
+    
+    def cleanup_idle_connections(self, max_idle_time: int = 300):
+        """
+        Clean up idle connections
+        
+        Args:
+            max_idle_time: Maximum idle time in seconds
+        """
+        with self.lock:
+            current_time = time.time()
+            keys_to_remove = []
+            
+            for key, conn in self.connections.items():
+                if current_time - conn["last_used"] > max_idle_time:
+                    keys_to_remove.append(key)
+            
+            for key in keys_to_remove:
+                del self.connections[key]
+    
+    def get_stats(self) -> Dict[str, Any]:
+        """Get connection pool statistics"""
+        with self.lock:
+            return {
+                "total_connections": len(self.connections),
+                "max_connections": self.max_connections,
+                "connections": list(self.connections.keys())
+            }
+
+
+class LazyLoader:
+    """Lazy loading for heavy resources"""
+    
+    def __init__(self):
+        self.loaded_modules = {}
+        self.loaded_tools = {}
+        self.lock = threading.Lock()
+    
+    def load_module(self, module_name: str, import_path: str):
+        """
+        Lazily load a module
+        
+        Args:
+            module_name: Name to store the module under
+            import_path: Import path for the module
+            
+        Returns:
+            Loaded module
+        """
+        with self.lock:
+            if module_name not in self.loaded_modules:
+                try:
+                    module = __import__(import_path, fromlist=[''])
+                    self.loaded_modules[module_name] = module
+                except ImportError as e:
+                    print(f"Warning: Could not load module {module_name}: {e}")
+                    self.loaded_modules[module_name] = None
+            
+            return self.loaded_modules.get(module_name)
+    
+    def load_tool(self, tool_name: str, loader_func: Callable):
+        """
+        Lazily load a tool
+        
+        Args:
+            tool_name: Name to store the tool under
+            loader_func: Function to load the tool
+            
+        Returns:
+            Loaded tool
+        """
+        with self.lock:
+            if tool_name not in self.loaded_tools:
+                try:
+                    tool = loader_func()
+                    self.loaded_tools[tool_name] = tool
+                except Exception as e:
+                    print(f"Warning: Could not load tool {tool_name}: {e}")
+                    self.loaded_tools[tool_name] = None
+            
+            return self.loaded_tools.get(tool_name)
+    
+    def clear(self):
+        """Clear all loaded resources"""
+        with self.lock:
+            self.loaded_modules.clear()
+            self.loaded_tools.clear()
+
+
+class BatchProcessor:
+    """Batch processing for multiple operations"""
+    
+    def __init__(self, max_workers: int = 5):
+        """
+        Initialize batch processor
+        
+        Args:
+            max_workers: Maximum number of worker threads
+        """
+        self.max_workers = max_workers
+        self.executor = ThreadPoolExecutor(max_workers=max_workers)
+    
+    def process_batch(self, tasks: List[Callable], timeout: int = 30) -> List[Any]:
+        """
+        Process a batch of tasks in parallel
+        
+        Args:
+            tasks: List of callable tasks
+            timeout: Timeout in seconds
+            
+        Returns:
+            List of results
+        """
+        futures = [self.executor.submit(task) for task in tasks]
+        results = []
+        
+        try:
+            for future in as_completed(futures, timeout=timeout):
+                try:
+                    result = future.result()
+                    results.append(result)
+                except Exception as e:
+                    results.append({"error": str(e)})
+        except Exception as e:
+            print(f"Batch processing error: {e}")
+        
+        return results
+    
+    def shutdown(self):
+        """Shutdown the executor"""
+        self.executor.shutdown(wait=True)
+
+
 class PerformanceMonitor:
     """Monitor and optimize performance"""
     
@@ -20,6 +211,9 @@ class PerformanceMonitor:
         self.metrics = {}
         self.cache = {}
         self.cache_ttl = 300  # 5 minutes default TTL
+        self.connection_pool = ConnectionPool()
+        self.lazy_loader = LazyLoader()
+        self.batch_processor = BatchProcessor()
     
     def timing(self, func: Callable) -> Callable:
         """Decorator to measure function execution time"""
@@ -90,6 +284,20 @@ class PerformanceMonitor:
     def clear_metrics(self):
         """Clear performance metrics"""
         self.metrics.clear()
+    
+    def get_connection_pool_stats(self) -> Dict[str, Any]:
+        """Get connection pool statistics"""
+        return self.connection_pool.get_stats()
+    
+    def cleanup_resources(self):
+        """Clean up resources"""
+        self.connection_pool.cleanup_idle_connections()
+        self.clear_cache()
+    
+    def shutdown(self):
+        """Shutdown all resources"""
+        self.batch_processor.shutdown()
+        self.cleanup_resources()
 
 
 # Global performance monitor instance
@@ -288,6 +496,12 @@ def get_performance_report() -> str:
     report += f"\nCache Statistics:\n"
     report += f"  Cached Items: {cache_size}\n"
     
+    # Connection pool statistics
+    pool_stats = performance_monitor.get_connection_pool_stats()
+    report += f"\nConnection Pool:\n"
+    report += f"  Total Connections: {pool_stats['total_connections']}\n"
+    report += f"  Max Connections: {pool_stats['max_connections']}\n"
+    
     return report