Upload agents/self_improvement_agent.py with huggingface_hub

agents/self_improvement_agent.py

@@ -0,0 +1,180 @@
+import asyncio
+from typing import Dict, List, Any
+from core.agent import BaseAgent
+from core.models import AgentConfig, Task, AgentMessage, SEOData
+import logging
+import random
+from datetime import datetime
+
+logger = logging.getLogger(__name__)
+
+
+class SelfImprovementAgent(BaseAgent):
+    """Self-Improvement Agent responsible for continuous learning and optimization"""
+    
+    def __init__(self, config: AgentConfig):
+        super().__init__(config)
+        self.performance_analytics = {}
+        self.prompt_refinements = []
+        self.workflow_optimizations = []
+        self.feature_priorities = []
+        
+    async def execute(self):
+        """Execute self-improvement functions"""
+        logger.info(f"{self.name} executing self-improvement and learning...")
+        
+        # Analyze system performance
+        await self.analyze_performance()
+        
+        # Refine prompts and processes
+        await self.refine_prompts()
+        
+        # Optimize workflows
+        await self.optimize_workflows()
+        
+        # Prioritize new features
+        await self.prioritize_features()
+    
+    async def analyze_performance(self):
+        """Analyze system performance and identify improvement areas"""
+        logger.info(f"{self.name} analyzing system performance...")
+        
+        # Simulate performance analysis
+        performance_data = {
+            "system_efficiency": "85%",
+            "task_completion_rate": "92%",
+            "resource_optimization": "improved_15%",
+            "bottleneck_identification": ["content_generation", "link_building_response_time"],
+            "suggested_improvements": ["parallel_processing", "better_load_balancing"]
+        }
+        
+        self.performance_analytics.update(performance_data)
+        
+        # Log performance analysis
+        logger.info(f"Performance analysis complete: Efficiency {performance_data['system_efficiency']}")
+    
+    async def refine_prompts(self):
+        """Refine prompts and processes based on outcomes"""
+        logger.info(f"{self.name} refining prompts and processes...")
+        
+        # Simulate prompt refinement
+        refinements = [
+            {
+                "component": "content_generation",
+                "original_prompt": "Write an article about X",
+                "refined_prompt": "Write a comprehensive, authoritative article about X with at least 2000 words, including examples, case studies, and actionable tips",
+                "improvement_metric": "engagement_increased_23%"
+            },
+            {
+                "component": "outreach_emails", 
+                "original_prompt": "Write a guest post outreach email",
+                "refined_prompt": "Write a personalized guest post outreach email for [site] focusing on mutual benefits and including specific article ideas relevant to their audience",
+                "improvement_metric": "response_rate_increased_31%"
+            },
+            {
+                "component": "keyword_analysis",
+                "original_prompt": "Analyze keywords",
+                "refined_prompt": "Analyze keywords for [niche] considering search intent, competition, and commercial value. Provide specific recommendations for content clusters",
+                "improvement_metric": "accuracy_improved_18%"
+            }
+        ]
+        
+        self.prompt_refinements.extend(refinements)
+        
+        # Log refinements
+        logger.info(f"Refined {len(refinements)} prompts/processes")
+    
+    async def optimize_workflows(self):
+        """Optimize system workflows based on performance data"""
+        logger.info(f"{self.name} optimizing workflows...")
+        
+        # Simulate workflow optimization
+        optimizations = [
+            {
+                "workflow": "content_approval_process",
+                "optimization": "implement_parallel_reviews_instead_of_sequential",
+                "expected_impact": "reduce_time_by_40%"
+            },
+            {
+                "workflow": "link_outreach_followup",
+                "optimization": "automate_followup_sequence_after_7_days",
+                "expected_impact": "increase_response_rate_by_15%"
+            },
+            {
+                "workflow": "technical_audit_reporting",
+                "optimization": "consolidate_multiple_reports_into_single_dashboard",
+                "expected_impact": "reduce_manual_work_by_60%"
+            }
+        ]
+        
+        self.workflow_optimizations.extend(optimizations)
+        
+        # Log optimizations
+        logger.info(f"Identified {len(optimizations)} workflow optimizations")
+        
+        # Send optimization suggestions to relevant agents
+        for opt in optimizations:
+            await self.send_message(
+                recipient="automation_ops",
+                content=f"Workflow optimization suggestion: {opt}",
+                message_type="info"
+            )
+    
+    async def prioritize_features(self):
+        """Prioritize new features based on impact and feasibility"""
+        logger.info(f"{self.name} prioritizing new features...")
+        
+        # Simulate feature prioritization
+        feature_priorities = [
+            {
+                "feature": "multilingual_content_generation",
+                "impact_score": 9.2,
+                "feasibility_score": 7.5,
+                "priority": "high",
+                "justification": "large_market_opportunity"
+            },
+            {
+                "feature": "advanced_competitor_tracking",
+                "impact_score": 8.7,
+                "feasibility_score": 8.0,
+                "priority": "high", 
+                "justification": "competitive_advantage"
+            },
+            {
+                "feature": "voice_search_optimization",
+                "impact_score": 7.3,
+                "feasibility_score": 6.8,
+                "priority": "medium",
+                "justification": "emerging_trend"
+            },
+            {
+                "feature": "video_content_generation",
+                "impact_score": 8.1,
+                "feasibility_score": 5.2,
+                "priority": "medium",
+                "justification": "high_demand_but_complex_implementation"
+            }
+        ]
+        
+        self.feature_priorities.extend(feature_priorities)
+        
+        # Send priorities to CEO agent
+        await self.send_message(
+            recipient="ceo_strategy",
+            content=f"Feature priorities: {feature_priorities[:2]}",  # Send top 2
+            message_type="info"
+        )
+    
+    async def _execute_task_logic(self, task: Task) -> Dict[str, Any]:
+        """Execute specific task logic for Self-Improvement agent"""
+        if task.type == "analyze_performance":
+            await self.analyze_performance()
+            return {"status": "completed", "result": self.performance_analytics}
+        elif task.type == "refine_prompts":
+            await self.refine_prompts()
+            return {"status": "completed", "result": self.prompt_refinements}
+        elif task.type == "optimize_workflows":
+            await self.optimize_workflows()
+            return {"status": "completed", "result": self.workflow_optimizations}
+        else:
+            return {"status": "error", "message": f"Unknown task type: {task.type}"}