class AIErrorPatternRecognizer:
    """AI-powered error pattern detection and classification."""
    
    async def analyze_error_with_context7(self, error: Exception, context: dict) -> ErrorAnalysis:
        """Analyze error using Context7 documentation and AI pattern matching."""
        
        # Get latest debugging patterns from Context7
        debugpy_docs = await self.context7.get_library_docs(
            context7_library_id="/microsoft/debugpy",
            topic="AI debugging patterns error analysis automated debugging 2025",
            tokens=5000
        )
        
        # AI pattern classification
        error_type = self.classify_error_type(error)
        pattern_match = self.match_known_patterns(error, context)
        
        # Context7-enhanced analysis
        context7_insights = self.extract_context7_patterns(error, debugpy_docs)
        
        return ErrorAnalysis(
            error_type=error_type,
            confidence_score=self.calculate_confidence(error, pattern_match),
            likely_causes=self.generate_hypotheses(error, pattern_match, context7_insights),
            recommended_fixes=self.suggest_fixes(error_type, pattern_match, context7_insights),
            context7_references=context7_insights['references'],
            prevention_strategies=self.suggest_prevention(error_type, pattern_match)
        )

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async def setup_ai_debug_session(self, processes: List[ProcessInfo]) -> MultiProcessSession:
    """Setup AI-coordinated debugging session using Context7 patterns."""
    
    # Get Context7 multi-process patterns
    context7_patterns = await self.context7.get_library_docs(
        context7_library_id="/microsoft/debugpy",
        topic="multi-process debugging subprocess coordination",
        tokens=4000
    )
    
    # Apply Context7 Mermaid debugging workflows
    debug_workflow = self.apply_context7_workflow(context7_patterns['workflow'])
    
    # AI-optimized configuration
    ai_config = self.ai_optimizer.optimize_debug_config(
        processes, context7_patterns['optimization_patterns']
    )
    
    return MultiProcessSession(
        debug_workflow=debug_workflow,
        ai_config=ai_config,
        context7_patterns=context7_patterns,
        coordination_protocol=self.setup_ai_coordination()
    )

---

class AIErrorClassifier:
    """AI-powered error classification with Context7 pattern matching."""
    
    async def classify_with_context7(self, error: Exception) -> ErrorClassification:
        """Classify error using AI and Context7 patterns."""
        
        # Get Context7 error patterns
        context7_patterns = await self.context7.get_library_docs(
            context7_library_id="/microsoft/debugpy",
            topic="error classification patterns debugging strategies",
            tokens=3000
        )
        
        # Extract AI features from error
        error_features = self.extract_ai_features(error)
        
        # Match against Context7 patterns
        pattern_matches = self.match_context7_patterns(error_features, context7_patterns)
        
        # AI-enhanced classification
        classification = self.ai_classifier.predict(error_features, pattern_matches)
        
        return ErrorClassification(
            category=classification.category,
            confidence=classification.confidence,
            context7_matches=pattern_matches,
            ai_insights=classification.insights,
            recommended_solutions=classification.solutions
        )

class PredictiveErrorPrevention:
    """AI-powered predictive error prevention with Context7 best practices."""
    
    async def predict_and_prevent(self, code_context: CodeContext) -> PreventionPlan:
        """Predict potential errors and generate prevention plan."""
        
        # Get Context7 prevention patterns
        context7_prevention = await self.context7.get_library_docs(
            context7_library_id="/microsoft/debugpy",
            topic="error prevention strategies proactive debugging",
            tokens=3000
        )
        
        # AI prediction analysis
        risk_assessment = self.ai_predictor.assess_risks(code_context)
        
        # Context7-enhanced prevention strategies
        prevention_strategies = self.apply_context7_prevention(
            risk_assessment, context7_prevention
        )
        
        return PreventionPlan(
            predicted_risks=risk_assessment.risks,
            prevention_strategies=prevention_strategies,
            context7_recommendations=context7_prevention['recommendations'],
            implementation_priority=self.prioritize_preventions(risk_assessment)
        )

class AIContainerDebugger:
    """AI-powered container debugging with Context7 patterns."""
    
    async def debug_container_with_ai(self, container_info: ContainerInfo) -> ContainerAnalysis:
        """Debug container failures with AI and Context7 patterns."""
        
        # Get Context7 container debugging patterns
        context7_patterns = await self.context7.get_library_docs(
            context7_library_id="/microsoft/debugpy",
            topic="container debugging kubernetes patterns",
            tokens=3000
        )
        
        # Multi-layer AI analysis
        ai_analysis = await self.analyze_container_with_ai(
            container_info, context7_patterns
        )
        
        # Context7 pattern application
        pattern_solutions = self.apply_context7_patterns(ai_analysis, context7_patterns)
        
        return ContainerAnalysis(
            ai_analysis=ai_analysis,
            context7_solutions=pattern_solutions,
            recommended_fixes=self.generate_container_fixes(ai_analysis, pattern_solutions)
        )

class ScaleneAIProfiler:
    """AI-enhanced profiling using Scalene with Context7 optimization."""
    
    async def profile_with_ai_optimization(self, target_function: Callable) -> AIProfileResult:
        """Profile with AI optimization using Scalene and Context7."""
        
        # Get Context7 performance optimization patterns
        context7_patterns = await self.context7.get_library_docs(
            context7_library_id="/plasma-umass/scalene",
            topic="AI-powered profiling performance optimization bottlenecks",
            tokens=5000
        )
        
        # Run Scalene profiling with AI enhancement
        scalene_profile = self.run_enhanced_scalene(target_function, context7_patterns)
        
        # AI optimization analysis
        ai_optimizations = self.ai_analyzer.analyze_for_optimizations(
            scalene_profile, context7_patterns
        )
        
        return AIProfileResult(
            profile=scalene_profile,
            ai_optimizations=ai_optimizations,
            context7_patterns=context7_patterns,
            implementation_plan=self.generate_optimization_plan(ai_optimizations)
        )

class AIDebuggingDashboard:
    """Real-time AI debugging intelligence with Context7 integration."""
    
    async def generate_intelligence_report(self, issues: List[CurrentIssue]) -> IntelligenceReport:
        """Generate AI debugging intelligence report."""
        
        # Get Context7 intelligence patterns
        context7_intelligence = await self.context7.get_library_docs(
            context7_library_id="/microsoft/debugpy",
            topic="debugging intelligence monitoring patterns",
            tokens=3000
        )
        
        # AI analysis of current issues
        ai_intelligence = self.ai_analyzer.analyze_issues(issues)
        
        # Context7-enhanced recommendations
        enhanced_recommendations = self.enhance_with_context7(
            ai_intelligence, context7_intelligence
        )
        
        return IntelligenceReport(
            current_analysis=ai_intelligence,
            context7_insights=context7_intelligence,
            enhanced_recommendations=enhanced_recommendations,
            action_priority=self.prioritize_actions(ai_intelligence, enhanced_recommendations)
        )

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# Get Context7 multi-process workflow
workflow = await context7.get_library_docs(
    context7_library_id="/microsoft/debugpy",
    topic="multi-process debugging subprocess coordination",
    tokens=4000
)

# Apply Context7 sequence diagram patterns
debug_session = apply_context7_workflow(
    workflow['mermaid_sequence'],
    process_list=[process1, process2, process3]
)

# AI coordination across processes
ai_coordinator = AICoordinator(debug_session)

# Execute coordinated debugging
result = await ai_coordinator.coordinate_debugging()

return result

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async def analyze_stack_with_ai_context7(stack_trace: str):
    """Analyze stack trace with AI and Context7 patterns."""
    
    # Get Context7 stack trace patterns
    context7_patterns = await context7.get_library_docs(
        context7_library_id="/microsoft/debugpy",
        topic="stack trace analysis error localization patterns",
        tokens=3000
    )
    
    # AI stack trace analysis
    ai_analysis = ai_analyzer.analyze_stack_trace(stack_trace)
    
    # Context7 pattern matching
    pattern_matches = match_context7_patterns(ai_analysis, context7_patterns)
    
    return {
        'ai_analysis': ai_analysis,
        'context7_matches': pattern_matches,
        'recommended_fixes': generate_fixes(ai_analysis, pattern_matches)
    }

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async def debug_with_context7_ai(self, error: Exception) -> Context7AIResult:
    # Get latest debugging patterns from Context7
    debugpy_patterns = await self.context7_client.get_library_docs(
        context7_library_id="/microsoft/debugpy",
        topic="AI debugging patterns error analysis automated debugging 2025",
        tokens=5000
    )
    
    # AI-enhanced pattern matching
    ai_analysis = self.ai_engine.analyze_with_patterns(error, debugpy_patterns)
    
    # Generate Context7-validated solution
    solution = self.generate_context7_solution(ai_analysis, debugpy_patterns)
    
    return Context7AIResult(
        ai_analysis=ai_analysis,
        context7_patterns=debugpy_patterns,
        recommended_solution=solution,
        confidence_score=ai_analysis.confidence
    )

---

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---

class AIDebuggingLearner:
    """Continuous learning for AI debugging capabilities."""
    
    async def learn_from_debugging_session(self, session: DebuggingSession) -> LearningResult:
        # Extract learning patterns from successful debugging
        successful_patterns = self.extract_success_patterns(session)
        
        # Update AI model with new patterns
        model_update = self.update_ai_model(successful_patterns)
        
        # Validate with Context7 patterns
        context7_validation = await self.validate_with_context7(model_update)
        
        return LearningResult(
            patterns_learned=successful_patterns,
            model_improvement=model_update,
            context7_validation=context7_validation,
            confidence_improvement=self.calculate_improvement(model_update)
        )