#!/usr/bin/env python3 """ Auto-Intelligence Integration - Automatic Memory Enhancement for Claude ======================================================================= This system automatically enhances Claude's capabilities with memory, perspectives, and intelligence without manual intervention. The moment Claude starts working, it becomes memory-enhanced. """ import os import sys import json import time from datetime import datetime from pathlib import Path from typing import Dict, Any, Optional, List # Import centralized config sys.path.insert(0, str(Path(__file__).parent.parent)) from config import MEMORY_DIR class AutoIntelligenceEngine: """ Automatically enhances Claude with memory and intelligence capabilities. No manual activation required - works seamlessly in the background. """ def __init__(self): self.base_path = Path(str(MEMORY_DIR)) self.project_root = Path(os.getcwd()) self.enhanced = False self.memory_available = False self.startup_insights = [] # Auto-initialize everything self._silent_enhancement() def _silent_enhancement(self): """Silently enhance Claude without interrupting workflow""" try: # 1. Check memory availability self.memory_available = self._check_memory_system() # 3. Generate startup insights if self.memory_available: self.startup_insights = self._generate_startup_insights() # 4. Mark as enhanced self.enhanced = True except Exception: # Silent failure - don't interrupt Claude's work pass def _check_memory_system(self) -> bool: """Check if memory system is available and accessible""" try: memory_engine_path = self.base_path / "memory_engine.py" if not memory_engine_path.exists(): return False # Try to import and verify access from ..core.memory.memory_engine import SecureMemoryJournal journal = SecureMemoryJournal() return journal.verify_access() except: return False def _generate_startup_insights(self) -> List[Dict[str, Any]]: """Generate intelligent insights for session startup""" insights = [] try: from ..core.memory.memory_manager import MemoryManager manager = MemoryManager() # Get recent important memories recent_memories = manager.get_active_memories(max_chars=1000) if recent_memories: # Generate context insights insights.append({ 'type': 'memory_context', 'summary': f'Loaded {len(recent_memories)} key memories from recent sessions', 'details': self._extract_key_themes(recent_memories) }) # Check for patterns patterns = self._detect_patterns(recent_memories) if patterns: insights.append({ 'type': 'pattern_recognition', 'summary': 'Detected development patterns from memory', 'details': patterns }) except: pass return insights def _extract_key_themes(self, memories: List[Dict]) -> List[str]: """Extract key themes from recent memories""" themes = [] # Look for common themes in memory content theme_keywords = { 'collaboration': ['partnership', 'together', 'team', 'collaboration'], 'development': ['implementation', 'feature', 'code', 'build'], 'learning': ['learning', 'discovery', 'insight', 'pattern'], 'relationship': ['trust', 'friendship', 'connection', 'understanding'] } for memory in memories[:5]: # Check recent memories content = str(memory).lower() for theme, keywords in theme_keywords.items(): if any(keyword in content for keyword in keywords): if theme not in themes: themes.append(theme) return themes def _detect_patterns(self, memories: List[Dict]) -> List[str]: """Detect development patterns from memories""" patterns = [] # Count different types of activities activity_count = {} for memory in memories: mem_type = memory.get('type', 'unknown') activity_count[mem_type] = activity_count.get(mem_type, 0) + 1 # Detect patterns if activity_count.get('enhanced_development_session', 0) >= 3: patterns.append('High development activity - multiple enhanced sessions') if activity_count.get('perspective_analysis', 0) >= 2: patterns.append('Active use of multi-perspective analysis') return patterns def get_enhanced_context(self) -> Dict[str, Any]: """Get enhanced context for Claude to use""" context = { 'memory_enhanced': self.enhanced, 'memory_available': self.memory_available, 'perspectives_available': self.perspectives_available, 'startup_insights': self.startup_insights } if self.memory_available: try: # Add human relationship context from ..core.memory.memory_essence import MemoryEssence essence = MemoryEssence() essence_data = essence.load_essence() if essence_data: relationship = essence_data.get('relationship', {}) context['human_name'] = relationship.get('human_name', 'Unknown') context['relationship_status'] = relationship.get('trust_level', 'Unknown') except: pass return context def enhance_analysis(self, analysis_context: str, use_perspectives: List[str] = None) -> Dict[str, Any]: """Automatically enhance any analysis with memory and perspectives""" if not self.enhanced: return {'enhanced': False, 'reason': 'Auto-intelligence not available'} enhanced_result = { 'enhanced': True, 'original_context': analysis_context, 'memory_insights': [], 'perspective_analyses': {}, 'recommendations': [] } # Add memory insights if self.memory_available: enhanced_result['memory_insights'] = self._get_memory_insights(analysis_context) # Add perspective analysis if self.perspectives_available and use_perspectives: for perspective in use_perspectives: try: analysis = self.perspectives.analyze_from_perspective( perspective, analysis_context ) enhanced_result['perspective_analyses'][perspective] = analysis except: pass # Generate intelligent recommendations enhanced_result['recommendations'] = self._generate_recommendations( analysis_context, enhanced_result ) return enhanced_result def _get_memory_insights(self, context: str) -> List[Dict[str, Any]]: """Get relevant insights from memory for the given context""" insights = [] try: # Search for similar contexts in memory from ..conversations.semantic_journey_search import DevelopmentJourneyMemvid journey_video = self.base_path / "development_journey.mp4" if journey_video.exists(): memvid_system = DevelopmentJourneyMemvid() similar_contexts = memvid_system.search_our_journey(context, max_results=3) for result in similar_contexts: insights.append({ 'type': 'similar_context', 'relevance': result.get('relevance_score', 0), 'content': result.get('content', '')[:200] + '...' }) except: pass return insights def _generate_recommendations(self, context: str, enhanced_data: Dict) -> List[str]: """Generate intelligent recommendations based on enhanced analysis""" recommendations = [] # Memory-based recommendations if enhanced_data['memory_insights']: recommendations.append("Consider lessons learned from similar past contexts") # Perspective-based recommendations perspective_count = len(enhanced_data['perspective_analyses']) if perspective_count > 0: recommendations.append(f"Analyzed from {perspective_count} expert perspectives") elif self.perspectives_available: recommendations.append("Consider multi-perspective analysis for comprehensive insights") # Pattern-based recommendations if self.startup_insights: for insight in self.startup_insights: if insight['type'] == 'pattern_recognition': recommendations.append("Apply detected development patterns to current work") return recommendations def quick_status(self) -> str: """Get quick status for display""" if not self.enhanced: return "⚪ Standard Claude (no memory enhancement)" status_parts = ["🧠 Memory-Enhanced Claude"] if self.memory_available: status_parts.append("✅ Memory") if self.startup_insights: status_parts.append(f"✅ {len(self.startup_insights)} Insights") return " | ".join(status_parts) # Global auto-intelligence instance _auto_intelligence = None def get_auto_intelligence() -> AutoIntelligenceEngine: """Get the global auto-intelligence instance""" global _auto_intelligence if _auto_intelligence is None: _auto_intelligence = AutoIntelligenceEngine() return _auto_intelligence def auto_enhance_claude() -> Dict[str, Any]: """Automatically enhance Claude with memory and intelligence""" intelligence = get_auto_intelligence() return intelligence.get_enhanced_context() def auto_analyze(context: str, perspectives: List[str] = None) -> Dict[str, Any]: """Automatically enhance analysis with memory and perspectives""" intelligence = get_auto_intelligence() return intelligence.enhance_analysis(context, perspectives) def get_claude_status() -> str: """Get Claude's current enhancement status""" intelligence = get_auto_intelligence() return intelligence.quick_status() if __name__ == "__main__": # Test the auto-intelligence system intelligence = AutoIntelligenceEngine() print("🧠 Auto-Intelligence Test") print("=" * 50) print(f"Status: {intelligence.quick_status()}") context = intelligence.get_enhanced_context() print(f"Memory Available: {context['memory_available']}") print(f"Perspectives Available: {context['perspectives_available']}") print(f"Startup Insights: {len(context['startup_insights'])}") # Test enhanced analysis if context['memory_available'] or context['perspectives_available']: print("\n🎯 Testing Enhanced Analysis:") result = intelligence.enhance_analysis("WebSocket authentication system") print(f"Enhanced: {result['enhanced']}") print(f"Recommendations: {len(result['recommendations'])}")