from scrapegraph_py import Client
from datetime import datetime
import json
 
class CompetitivePricingAgent:
    """
    Specialized agent for competitive pricing intelligence
    Expert at detecting pricing strategies and predicting changes
    """
    
    def __init__(self, api_key, agent_config):
        self.client = Client(api_key=api_key)
        self.agent_id = "competitive_pricing_agent"
        self.specialization = "pricing_intelligence"
        
        # Agent-specific knowledge base
        self.pricing_patterns = {}
        self.strategy_models = {}
        self.prediction_accuracy = []
        
        # Agent capabilities
        self.capabilities = {
            'price_monitoring': self.monitor_competitive_prices,
            'strategy_detection': self.detect_pricing_strategy,
            'change_prediction': self.predict_price_changes,
            'elasticity_analysis': self.analyze_price_elasticity,
            'psychological_analysis': self.analyze_psychological_pricing
        }
        
        # Communication with other agents
        self.message_queue = []
        self.collaboration_requests = []
    
    def monitor_competitive_prices(self, competitors):
        """
        Core capability: Monitor competitive pricing
        """
        
        pricing_prompt = """
        Extract comprehensive pricing intelligence:
        
        Price Architecture:
        - List prices for all products
        - Active discounts and promotions
        - Bundle pricing and packages
        - Volume discount tiers
        - Subscription vs one-time pricing
        
        Psychological Elements:
        - Charm pricing (.99, .95)
        - Anchor prices (crossed out originals)
        - Urgency indicators
        - Scarcity signals
        - Social proof elements
        
        Strategic Signals:
        - New pricing tiers introduced
        - Products removed or discontinued
        - Pricing structure changes
        - Geographic price variations
        - Customer segment pricing
        """
        
        all_pricing_data = []
        
        for competitor in competitors:
            try:
                pricing_data = self.client.smartscraper(
                    website_url=competitor['url'],
                    user_prompt=pricing_prompt
                )
                
                # Agent-specific analysis
                analysis = {
                    'competitor': competitor['name'],
                    'raw_data': pricing_data,
                    'strategy': self.detect_pricing_strategy(pricing_data),
                    'changes': self.detect_changes(competitor['name'], pricing_data),
                    'predictions': self.predict_price_changes(competitor['name'], pricing_data),
                    'confidence': self.calculate_confidence(pricing_data),
                    'timestamp': datetime.now().isoformat()
                }
                
                all_pricing_data.append(analysis)
                
                # Update agent knowledge
                self.update_knowledge_base(competitor['name'], analysis)
                
            except Exception as e:
                self.log_error(f"Error monitoring {competitor['name']}: {e}")
        
        return {
            'agent_id': self.agent_id,
            'capability': 'price_monitoring',
            'data': all_pricing_data,
            'summary': self.generate_summary(all_pricing_data),
            'alerts': self.generate_alerts(all_pricing_data),
            'collaboration_needed': self.identify_collaboration_needs(all_pricing_data)
        }
    
    def detect_pricing_strategy(self, pricing_data):
        """
        Agent expertise: Identify what pricing strategy is being used
        """
        
        strategies_detected = []
        
        # Check for penetration pricing
        if self.is_penetration_pricing(pricing_data):
            strategies_detected.append({
                'strategy': 'penetration',
                'confidence': 0.87,
                'indicators': [
                    'Prices 20% below market average',
                    'Heavy promotional activity',
                    'Low-margin indicators'
                ],
                'implications': 'Aggressive market share grab',
                'counter_strategy': 'Value differentiation, not price war'
            })
        
        # Check for psychological pricing
        if self.is_psychological_pricing(pricing_data):
            strategies_detected.append({
                'strategy': 'psychological',
                'confidence': 0.92,
                'indicators': [
                    'Charm pricing (X.99) in 78% of products',
                    'Heavy use of anchor prices',
                    'Decoy pricing detected'
                ],
                'implications': 'Targeting price-sensitive customers',
                'counter_strategy': 'Transparency and trust positioning'
            })
        
        # Check for good-better-best
        if self.is_tiered_pricing(pricing_data):
            strategies_detected.append({
                'strategy': 'good_better_best',
                'confidence': 0.85,
                'indicators': [
                    '3-tier structure identified',
                    'Middle tier positioned as best value',
                    'Bottom tier appears to be decoy'
                ],
                'implications': 'Optimizing for middle-tier conversions',
                'counter_strategy': 'Simplified 2-tier or all-inclusive option'
            })
        
        return {
            'primary_strategy': strategies_detected[0] if strategies_detected else None,
            'all_strategies': strategies_detected,
            'agent_assessment': self.assess_strategic_threat(strategies_detected)
        }
    
    def predict_price_changes(self, competitor, current_data):
        """
        Agent expertise: Predict future price changes
        """
        
        predictions = []
        
        # Analyze historical patterns
        historical = self.pricing_patterns.get(competitor, [])
        
        # Check for pre-change signals
        if self.detect_sale_ending_signals(current_data):
            predictions.append({
                'prediction': 'Price increase',
                'probability': 0.89,
                'timeframe': '2-4 days',
                'magnitude': '+15-25%',
                'trigger': 'Current promotion ending',
                'confidence': 0.89,
                'recommended_action': 'Maintain pricing to capture switchers'
            })
        
        if self.detect_inventory_signals(current_data):
            predictions.append({
                'prediction': 'Price increase or stockout',
                'probability': 0.76,
                'timeframe': '3-7 days',
                'magnitude': '+10-20%',
                'trigger': 'Low inventory levels detected',
                'confidence': 0.76,
                'recommended_action': 'Emphasize availability in marketing'
            })
        
        # Learn from prediction outcomes
        self.track_predictions(competitor, predictions)
        
        return predictions
    
    def communicate_with_agents(self, message, target_agents=None):
        """
        Inter-agent communication
        """
        
        communication = {
            'from': self.agent_id,
            'to': target_agents or 'all',
            'timestamp': datetime.now().isoformat(),
            'message_type': message['type'],
            'content': message['content'],
            'requires_response': message.get('requires_response', False),
            'priority': message.get('priority', 'medium')
        }
        
        # Send to message queue
        self.message_queue.append(communication)
        
        return communication
    
    def request_collaboration(self, capability_needed, context):
        """
        Request help from other specialized agents
        """
        
        request = {
            'requesting_agent': self.agent_id,
            'capability_needed': capability_needed,
            'context': context,
            'urgency': 'high' if context.get('urgent') else 'medium',
            'timestamp': datetime.now().isoformat()
        }
        
        # Example: Request market context from Market Analysis Agent
        if capability_needed == 'market_context':
            message = {
                'type': 'collaboration_request',
                'content': {
                    'request': 'Need market context for pricing strategy assessment',
                    'details': context,
                    'expected_output': 'Market conditions, trends, economic factors'
                },
                'requires_response': True,
                'priority': 'high'
            }
            
            self.communicate_with_agents(
                message,
                target_agents=['market_analysis_agent']
            )
        
        return request
    
    def update_knowledge_base(self, competitor, new_data):
        """
        Continuous learning: Update agent's knowledge base
        """
        
        if competitor not in self.pricing_patterns:
            self.pricing_patterns[competitor] = []
        
        # Add new data point
        self.pricing_patterns[competitor].append({
            'timestamp': datetime.now().isoformat(),
            'data': new_data,
            'patterns_identified': new_data.get('strategy'),
            'predictions_made': new_data.get('predictions')
        })
        
        # Keep last 90 days
        if len(self.pricing_patterns[competitor]) > 90:
            self.pricing_patterns[competitor] = self.pricing_patterns[competitor][-90:]
        
        # Learn from patterns
        self.learn_from_history(competitor)
    
    def learn_from_history(self, competitor):
        """
        Agent self-improvement through pattern learning
        """
        
        history = self.pricing_patterns.get(competitor, [])
        
        if len(history) < 10:
            return  # Not enough data yet
        
        # Identify recurring patterns
        patterns = self.identify_recurring_patterns(history)
        
        # Update strategy models
        for pattern in patterns:
            if pattern['type'] not in self.strategy_models:
                self.strategy_models[pattern['type']] = {
                    'occurrences': 0,
                    'success_rate': 0,
                    'typical_duration': 0
                }
            
            self.strategy_models[pattern['type']]['occurrences'] += 1
            self.strategy_models[pattern['type']]['typical_duration'] = pattern['duration']
        
        # Improve prediction models
        self.improve_prediction_accuracy(history)
 
# Deploy specialized pricing agent
pricing_agent = CompetitivePricingAgent(
    api_key="your-scrapegraphai-key",
    agent_config={'competitors': ['competitor1.com', 'competitor2.com']}
)
 
# Agent performs its specialized task
pricing_intelligence = pricing_agent.monitor_competitive_prices([
    {'name': 'Competitor A', 'url': 'https://competitor-a.com/pricing'},
    {'name': 'Competitor B', 'url': 'https://competitor-b.com/pricing'}
])
 
print(json.dumps(pricing_intelligence, indent=2))

class ProductLaunchDetectionAgent:
    """
    Specialized agent for detecting competitor product launches
    Predicts launches 4-12 weeks before they happen
    """
    
    def __init__(self, api_key):
        self.client = Client(api_key=api_key)
        self.agent_id = "product_launch_agent"
        self.specialization = "product_launch_detection"
        
        # Agent knowledge base
        self.launch_indicators = {}
        self.historical_launches = {}
        self.prediction_models = {}
    
    def detect_launch_signals(self, competitor):
        """
        Monitor multiple signals that predict product launches
        """
        
        signals = {
            'competitor': competitor,
            'timestamp': datetime.now().isoformat(),
            'signals_detected': [],
            'confidence_score': 0,
            'predicted_launch_window': None
        }
        
        # Signal 1: Job postings
        hiring_signals = self.analyze_hiring_patterns(competitor)
        if hiring_signals['launch_indicator']:
            signals['signals_detected'].append({
                'type': 'hiring_surge',
                'strength': hiring_signals['strength'],
                'details': hiring_signals['details'],
                'confidence_contribution': 0.25
            })
            signals['confidence_score'] += 0.25
        
        # Signal 2: Website changes
        website_signals = self.analyze_website_changes(competitor)
        if website_signals['launch_indicator']:
            signals['signals_detected'].append({
                'type': 'website_preparation',
                'strength': website_signals['strength'],
                'details': website_signals['details'],
                'confidence_contribution': 0.20
            })
            signals['confidence_score'] += 0.20
        
        # Signal 3: Beta programs
        beta_signals = self.detect_beta_programs(competitor)
        if beta_signals['launch_indicator']:
            signals['signals_detected'].append({
                'type': 'beta_program',
                'strength': beta_signals['strength'],
                'details': beta_signals['details'],
                'confidence_contribution': 0.30
            })
            signals['confidence_score'] += 0.30
        
        # Signal 4: Marketing preparation
        marketing_signals = self.analyze_marketing_activity(competitor)
        if marketing_signals['launch_indicator']:
            signals['signals_detected'].append({
                'type': 'marketing_rampup',
                'strength': marketing_signals['strength'],
                'details': marketing_signals['details'],
                'confidence_contribution': 0.15
            })
            signals['confidence_score'] += 0.15
        
        # Signal 5: Technology signals
        tech_signals = self.analyze_technology_signals(competitor)
        if tech_signals['launch_indicator']:
            signals['signals_detected'].append({
                'type': 'technology_stack',
                'strength': tech_signals['strength'],
                'details': tech_signals['details'],
                'confidence_contribution': 0.10
            })
            signals['confidence_score'] += 0.10
        
        # Predict launch window if confidence > 0.6
        if signals['confidence_score'] > 0.6:
            signals['predicted_launch_window'] = self.predict_launch_timing(
                signals['signals_detected']
            )
            
            # Request collaboration for strategic response
            self.request_strategic_planning_collaboration(signals)
        
        return signals
    
    def analyze_hiring_patterns(self, competitor):
        """
        Analyze hiring for product launch signals
        """
        
        hiring_prompt = """
        Extract hiring signals for product launch prediction:
        
        Engineering Roles:
        - Total engineering positions open
        - Backend, frontend, mobile roles
        - Senior vs junior roles ratio
        - Specialized roles (ML, DevOps, etc.)
        - Urgency indicators in postings
        
        Product Roles:
        - Product managers and product marketing
        - UX/UI designers
        - Technical writers
        - Product operations
        
        Go-to-Market Roles:
        - Sales engineers
        - Customer success managers
        - Solution architects
        - Training specialists
        
        Timeline Indicators:
        - "Immediate hire" or urgency language
        - Start date preferences
        - Project mentions in descriptions
        - Team size indicators
        """
        
        hiring_data = self.client.smartscraper(
            website_url=f"https://{competitor}/careers",
            user_prompt=hiring_prompt
        )
        
        # Analyze for launch indicators
        analysis = {
            'launch_indicator': False,
            'strength': 0,
            'details': {}
        }
        
        # Check for product launch hiring pattern
        engineering_count = self.count_engineering_roles(hiring_data)
        product_count = self.count_product_roles(hiring_data)
        gtm_count = self.count_gtm_roles(hiring_data)
        
        # Pattern: High engineering + growing product/GTM = launch prep
        if engineering_count > 10 and (product_count + gtm_count) > 5:
            analysis['launch_indicator'] = True
            analysis['strength'] = 0.85
            analysis['details'] = {
                'engineering_roles': engineering_count,
                'product_roles': product_count,
                'gtm_roles': gtm_count,
                'pattern': 'Product development + GTM preparation',
                'estimated_timeline': '8-16 weeks to launch'
            }
        
        return analysis
    
    def detect_beta_programs(self, competitor):
        """
        Detect beta tester recruitment (strong launch signal)
        """
        
        beta_prompt = """
        Extract beta program and early access information:
        
        Beta Programs:
        - Beta tester recruitment
        - Early access programs
        - Preview or alpha programs
        - Waitlist signups
        - "Coming soon" announcements
        
        Program Details:
        - Program start/end dates
        - Number of spots available
        - Selection criteria
        - NDA requirements
        - Feedback mechanisms
        
        Product Hints:
        - Product category mentions
        - Feature descriptions
        - Target user descriptions
        - Problem statements
        - Technology mentions
        """
        
        beta_data = self.client.smartscraper(
            website_url=f"https://{competitor}",
            user_prompt=beta_prompt
        )
        
        analysis = {
            'launch_indicator': False,
            'strength': 0,
            'details': {}
        }
        
        # Beta program is strong indicator (70-80% lead time to launch)
        if self.has_active_beta_program(beta_data):
            analysis['launch_indicator'] = True
            analysis['strength'] = 0.90
            analysis['details'] = {
                'beta_type': self.identify_beta_type(beta_data),
                'timeline_indicator': self.estimate_beta_timeline(beta_data),
                'product_category': self.extract_product_category(beta_data),
                'estimated_timeline': '6-12 weeks to public launch'
            }
        
        return analysis
    
    def request_strategic_planning_collaboration(self, launch_signals):
        """
        When launch detected, request collaboration from strategy agents
        """
        
        collaboration_request = {
            'from': self.agent_id,
            'to': [
                'competitive_strategy_agent',
                'product_roadmap_agent',
                'marketing_strategy_agent'
            ],
            'urgency': 'high',
            'type': 'strategic_planning_required',
            'context': {
                'event': 'competitor_product_launch',
                'confidence': launch_signals['confidence_score'],
                'timeline': launch_signals['predicted_launch_window'],
                'signals': launch_signals['signals_detected'],
                'required_actions': [
                    'Assess competitive threat level',
                    'Evaluate product roadmap implications',
                    'Develop counter-launch strategy',
                    'Prepare marketing response'
                ]
            }
        }
        
        # Send to coordination layer
        return collaboration_request
 
# Deploy product launch detection agent
launch_agent = ProductLaunchDetectionAgent(api_key="your-key")
launch_signals = launch_agent.detect_launch_signals("competitor.com")
 
if launch_signals['confidence_score'] > 0.6:
    print(f"🚨 PRODUCT LAUNCH PREDICTED!")
    print(f"Confidence: {launch_signals['confidence_score']:.0%}")
    print(f"Timeline: {launch_signals['predicted_launch_window']}")
    print(f"Signals: {len(launch_signals['signals_detected'])}")

class AgentCoordinator:
    """
    Master coordinator that manages all specialized agents
    Orchestrates collaboration, synthesizes insights, resolves conflicts
    """
    
    def __init__(self, config):
        self.config = config
        self.agents = {}
        self.message_bus = []
        self.active_collaborations = {}
        
        # Initialize specialized agent teams
        self.initialize_agent_teams()
    
    def initialize_agent_teams(self):
        """
        Create and register all specialized agents
        """
        
        # Competitive Intelligence Team
        self.agents['competitive_pricing'] = CompetitivePricingAgent(
            self.config['api_key'],
            self.config.get('pricing_config', {})
        )
        
        self.agents['product_launch'] = ProductLaunchDetectionAgent(
            self.config['api_key']
        )
        
        self.agents['competitive_marketing'] = CompetitiveMarketingAgent(
            self.config['api_key']
        )
        
        # Market Intelligence Team
        self.agents['market_trends'] = MarketTrendAgent(
            self.config['api_key']
        )
        
        self.agents['industry_news'] = IndustryNewsAgent(
            self.config['api_key']
        )
        
        # Customer Intelligence Team
        self.agents['sentiment_analysis'] = SentimentAnalysisAgent(
            self.config['api_key']
        )
        
        self.agents['review_monitoring'] = ReviewMonitoringAgent(
            self.config['api_key']
        )
        
        # Synthesis Agent
        self.agents['synthesis'] = SynthesisAgent(
            self.config['api_key']
        )
        
        print(f"✅ Initialized {len(self.agents)} specialized agents")
    
    def orchestrate_intelligence_gathering(self):
        """
        Coordinate all agents to gather and synthesize intelligence
        """
        
        print("🤖 Multi-Agent Intelligence System Activated")
        print("=" * 60)
        
        # Phase 1: Parallel data collection by specialized agents
        print("\n📊 Phase 1: Specialized Agent Data Collection")
        agent_outputs = {}
        
        for agent_id, agent in self.agents.items():
            if agent_id == 'synthesis':
                continue  # Synthesis runs after collection
            
            print(f"  → {agent_id} collecting intelligence...")
            
            try:
                output = agent.execute_primary_task()
                agent_outputs[agent_id] = output
                
                print(f"    ✓ {agent_id}: {output.get('items_collected', 0)} items")
                
                # Check for collaboration requests
                if output.get('collaboration_needed'):
                    self.process_collaboration_request(agent_id, output['collaboration_needed'])
                
            except Exception as e:
                print(f"    ✗ {agent_id}: Error - {e}")
        
        # Phase 2: Process inter-agent collaborations
        print("\n🤝 Phase 2: Inter-Agent Collaboration")
        collaboration_results = self.process_all_collaborations()
        
        # Phase 3: Synthesis and insight generation
        print("\n🧠 Phase 3: Intelligence Synthesis")
        synthesized_intelligence = self.agents['synthesis'].synthesize(
            agent_outputs=agent_outputs,
            collaboration_results=collaboration_results
        )
        
        # Phase 4: Strategic recommendations
        print("\n💡 Phase 4: Strategic Recommendations")
        recommendations = self.generate_strategic_recommendations(
            synthesized_intelligence
        )
        
        # Phase 5: Alert generation
        print("\n🚨 Phase 5: Priority Alerts")
        alerts = self.generate_priority_alerts(
            synthesized_intelligence,
            recommendations
        )
        
        print(f"\n✅ Intelligence Cycle Complete")
        print(f"  → {len(agent_outputs)} agents contributed")
        print(f"  → {len(synthesized_intelligence.get('insights', []))} insights generated")
        print(f"  → {len(recommendations)} strategic recommendations")
        print(f"  → {len(alerts)} priority alerts")
        
        return {
            'agent_outputs': agent_outputs,
            'synthesis': synthesized_intelligence,
            'recommendations': recommendations,
            'alerts': alerts,
            'timestamp': datetime.now().isoformat()
        }
    
    def process_collaboration_request(self, requesting_agent, request):
        """
        Handle collaboration request between agents
        """
        
        collaboration_id = f"collab_{len(self.active_collaborations)}"
        
        self.active_collaborations[collaboration_id] = {
            'id': collaboration_id,
            'requesting_agent': requesting_agent,
            'request': request,
            'status': 'pending',
            'responses': [],
            'created_at': datetime.now().isoformat()
        }
        
        # Route to appropriate agents
        target_agents = request.get('target_agents', [])
        
        for target_agent in target_agents:
            if target_agent in self.agents:
                # Request collaboration
                response = self.agents[target_agent].handle_collaboration_request(
                    collaboration_id,
                    requesting_agent,
                    request
                )
                
                self.active_collaborations[collaboration_id]['responses'].append({
                    'agent': target_agent,
                    'response': response,
                    'timestamp': datetime.now().isoformat()
                })
        
        self.active_collaborations[collaboration_id]['status'] = 'completed'
        
        return self.active_collaborations[collaboration_id]
    
    def process_all_collaborations(self):
        """
        Process all pending collaborations
        """
        
        results = []
        
        for collab_id, collaboration in self.active_collaborations.items():
            if collaboration['status'] == 'completed':
                results.append({
                    'collaboration_id': collab_id,
                    'participants': [collaboration['requesting_agent']] + 
                                  [r['agent'] for r in collaboration['responses']],
                    'outcome': self.synthesize_collaboration(collaboration)
                })
        
        return results
    
    def synthesize_collaboration(self, collaboration):
        """
        Synthesize results from multi-agent collaboration
        """
        
        # Combine insights from all participating agents
        combined_insights = {
            'request': collaboration['request'],
            'responses': collaboration['responses'],
            'synthesis': self.create_collaborative_insight(collaboration),
            'confidence': self.calculate_collaborative_confidence(collaboration)
        }
        
        return combined_insights
    
    def generate_strategic_recommendations(self, intelligence):
        """
        Generate strategic recommendations from synthesized intelligence
        """
        
        recommendations = []
        
        # Analyze high-confidence insights for actionable recommendations
        for insight in intelligence.get('insights', []):
            if insight.get('confidence', 0) > 0.75 and insight.get('strategic_importance') == 'high':
                recommendation = {
                    'insight': insight['description'],
                    'recommendation': self.formulate_recommendation(insight),
                    'expected_impact': self.estimate_impact(insight),
                    'urgency': self.assess_urgency(insight),
                    'resources_required': self.estimate_resources(insight),
                    'timeline': self.recommend_timeline(insight)
                }
                recommendations.append(recommendation)
        
        # Prioritize recommendations
        prioritized = sorted(
            recommendations,
            key=lambda x: (
                self.urgency_score(x['urgency']),
                self.impact_score(x['expected_impact'])
            ),
            reverse=True
        )
        
        return prioritized
    
    def run_continuous_multi_agent_system(self, interval_minutes=15):
        """
        Run multi-agent system continuously
        Agents work in parallel, collaborate, and continuously learn
        """
        
        print("🚀 Multi-Agent System Starting")
        print(f"👥 {len(self.agents)} Specialized Agents Active")
        print(f"⚡ Update Interval: {interval_minutes} minutes")
        print("=" * 60)
        
        cycle = 0
        
        while True:
            cycle += 1
            cycle_start = datetime.now()
            
            print(f"\n🔄 Cycle #{cycle} - {cycle_start.strftime('%H:%M:%S')}")
            
            try:
                # Run orchestrated intelligence gathering
                results = self.orchestrate_intelligence_gathering()
                
                # Store results
                self.store_cycle_results(cycle, results)
                
                # Update agent performance metrics
                self.update_agent_performance(results)
                
                # Trigger agent learning
                self.trigger_agent_learning(results)
                
                cycle_duration = (datetime.now() - cycle_start).total_seconds()
                print(f"\n⏱️  Cycle completed in {cycle_duration:.1f} seconds")
                
            except Exception as e:
                print(f"❌ Error in cycle {cycle}: {e}")
            
            # Wait for next cycle
            print(f"\n⏳ Next cycle in {interval_minutes} minutes...")
            time.sleep(interval_minutes * 60)
 
# Deploy multi-agent system
config = {
    'api_key': 'your-scrapegraphai-key',
    'competitors': ['competitor1.com', 'competitor2.com'],
    'pricing_config': {'update_frequency': 15},
    'agents_enabled': 'all'
}
 
coordinator = AgentCoordinator(config)
 
# Run continuous multi-agent intelligence
coordinator.run_continuous_multi_agent_system(interval_minutes=15)

class AgentLearningSystem:
    """
    Manages continuous learning and improvement for all agents
    Agents get better over time through performance feedback
    """
    
    def __init__(self):
        self.performance_history = {}
        self.improvement_opportunities = {}
        
    def evaluate_agent_performance(self, agent_id, cycle_results):
        """
        Evaluate how well agent performed in this cycle
        """
        
        metrics = {
            'accuracy': self.calculate_accuracy(agent_id, cycle_results),
            'speed': self.calculate_speed(agent_id, cycle_results),
            'insight_quality': self.assess_insight_quality(agent_id, cycle_results),
            'collaboration_effectiveness': self.assess_collaboration(agent_id, cycle_results),
            'prediction_accuracy': self.measure_predictions(agent_id, cycle_results)
        }
        
        # Store performance history
        if agent_id not in self.performance_history:
            self.performance_history[agent_id] = []
        
        self.performance_history[agent_id].append({
            'timestamp': datetime.now().isoformat(),
            'metrics': metrics,
            'cycle_results': cycle_results
        })
        
        return metrics
    
    def identify_improvement_opportunities(self, agent_id):
        """
        Identify where agent can improve
        """
        
        history = self.performance_history.get(agent_id, [])
        
        if len(history) < 10:
            return []  # Need more data
        
        opportunities = []
        
        # Analyze trends
        recent_accuracy = [h['metrics']['accuracy'] for h in history[-10:]]
        
        if sum(recent_accuracy) / len(recent_accuracy) < 0.85:
            opportunities.append({
                'area': 'accuracy',
                'current_level': sum(recent_accuracy) / len(recent_accuracy),
                'target_level': 0.90,
                'improvement_strategy': 'Refine data extraction prompts and validation'
            })
        
        # Check prediction accuracy
        recent_predictions = [h['metrics']['prediction_accuracy'] for h in history[-10:]]
        
        if sum(recent_predictions) / len(recent_predictions) < 0.75:
            opportunities.append({
                'area': 'predictions',
                'current_level': sum(recent_predictions) / len(recent_predictions),
                'target_level': 0.85,
                'improvement_strategy': 'Enhance prediction models with more historical data'
            })
        
        return opportunities
    
    def train_agent(self, agent_id, improvement_opportunities):
        """
        Implement improvements for agent
        """
        
        for opportunity in improvement_opportunities:
            if opportunity['area'] == 'accuracy':
                # Implement accuracy improvements
                self.improve_data_extraction(agent_id)
            
            elif opportunity['area'] == 'predictions':
                # Implement prediction improvements
                self.enhance_prediction_models(agent_id)
            
            elif opportunity['area'] == 'collaboration':
                # Improve collaboration protocols
                self.optimize_collaboration(agent_id)
        
        return {
            'agent_id': agent_id,
            'improvements_applied': len(improvement_opportunities),
            'expected_performance_gain': '5-15%',
            'timestamp': datetime.now().isoformat()
        }

class MultiAgentROI:
    """Calculate ROI of multi-agent system"""
    
    def calculate_annual_roi(self):
        """
        Compare multi-agent system to traditional BI team
        """
        
        # Traditional BI Team (10 people)
        traditional_costs = {
            'salaries': 1_700_000,  # 10 x $170K
            'tools': 300_000,        # Commercial platforms
            'overhead': 400_000,     # Benefits, office, etc.
            'total': 2_400_000
        }
        
        traditional_output = {
            'productive_hours': 12_000,  # 240 hours/week * 50 weeks
            'insights_per_year': 2_400,  # ~10 per person per week
            'decision_latency_days': 5,
            'coverage_percentage': 20
        }
        
        # Multi-Agent System
        multi_agent_costs = {
            'scrapegraphai': 48_000,
            'infrastructure': 36_000,
            'development': 60_000,  # One-time, amortized
            'maintenance': 36_000,   # 0.25 FTE
            'total': 180_000
        }
        
        multi_agent_output = {
            'productive_hours': 876_000,  # 24/7/365
            'insights_per_year': 36_500,  # 100 per day
            'decision_latency_hours': 1,
            'coverage_percentage': 95
        }
        
        # Calculate value
        savings = traditional_costs['total'] - multi_agent_costs['total']
        productivity_multiplier = (
            multi_agent_output['productive_hours'] / 
            traditional_output['productive_hours']
        )
        insight_multiplier = (
            multi_agent_output['insights_per_year'] /
            traditional_output['insights_per_year']
        )
        
        roi_percentage = (savings / multi_agent_costs['total']) * 100
        
        return {
            'annual_savings': savings,
            'roi_percentage': roi_percentage,
            'productivity_multiplier': f"{productivity_multiplier:.0f}x",
            'insight_multiplier': f"{insight_multiplier:.0f}x",
            'payback_months': (multi_agent_costs['total'] / savings) * 12,
            'value_created_year_1': savings + (insight_multiplier * 50_000)  # $50K per insight
        }
 
roi = MultiAgentROI()
results = roi.calculate_annual_roi()
 
print(f"Annual Savings: ${results['annual_savings']:,.0f}")
print(f"ROI: {results['roi_percentage']:.0f}%")
print(f"Productivity: {results['productivity_multiplier']}")
print(f"Payback: {results['payback_months']:.1f} months")

from scrapegraph_py import Client
 
# 1. Create specialized agents
pricing_agent = CompetitivePricingAgent(api_key="your-key")
launch_agent = ProductLaunchDetectionAgent(api_key="your-key")
 
# 2. Agents work in parallel
pricing_intel = pricing_agent.monitor_competitive_prices(competitors)
launch_signals = launch_agent.detect_launch_signals("competitor.com")
 
# 3. Agents collaborate
if launch_signals['confidence'] > 0.7:
    # Launch agent requests pricing context
    pricing_context = pricing_agent.provide_pricing_context(
        competitor="competitor.com",
        context="product_launch"
    )
    
    # Combined intelligence
    strategic_insight = synthesize_insights(
        launch_signals,
        pricing_context
    )
 
print(f"Multi-agent insight: {strategic_insight}")
 
# You now have specialized agents collaborating
# Next: Build full coordination system using guide above