kandil_code 0.1.0

//! Meta-agent for self-improvement
//! 
//! Agent that analyzes and improves the system itself

use anyhow::Result;
use async_trait::async_trait;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use crate::core::agents::base::{Agent, AgentState};
use crate::core::adapters::ai::KandilAI;
use std::sync::Arc;

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ImprovementPlan {
    pub title: String,
    pub description: String,
    pub priority: Priority,
    pub impact: Impact,
    pub implementation_steps: Vec<String>,
    pub estimated_effort: String, // e.g., "Small", "Medium", "Large"
    pub expected_benefits: Vec<String>,
    pub dependencies: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum Priority {
    Low,
    Medium,
    High,
    Critical,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum Impact {
    Low,
    Medium,
    High,
    Transformative,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SystemAnalysis {
    pub performance_bottlenecks: Vec<String>,
    pub code_quality_issues: Vec<String>,
    pub architecture_improvements: Vec<String>,
    pub user_experience_issues: Vec<String>,
    pub technical_debt: Vec<String>,
    pub security_concerns: Vec<String>,
    pub maintainability_issues: Vec<String>,
}

pub struct MetaAgent {
    ai: Arc<KandilAI>,
    pub improvement_history: Vec<ImprovementPlan>,
    pub system_metrics: HashMap<String, f64>,
}

impl MetaAgent {
    pub fn new(ai: Arc<KandilAI>) -> Self {
        Self {
            ai,
            improvement_history: Vec::new(),
            system_metrics: HashMap::new(),
        }
    }

    pub async fn analyze_system(&self, codebase_path: &str) -> Result<SystemAnalysis> {
        let prompt = format!(
            r#"Analyze the system at {} for:
            - Performance bottlenecks
            - Code quality issues
            - Architecture improvements
            - User experience issues
            - Technical debt
            - Security concerns
            - Maintainability issues
            
            Provide specific, actionable findings with file references where applicable.
            "#,
            codebase_path
        );

        let analysis = self.ai.chat(&prompt).await?;
        
        // In a real implementation, this would parse the structured response
        // For now, we'll return a basic analysis
        Ok(SystemAnalysis {
            performance_bottlenecks: vec![
                "Database queries in hot paths".to_string(),
                "Inefficient algorithms in core components".to_string(),
            ],
            code_quality_issues: vec![
                "Complex functions need refactoring".to_string(),
                "Missing unit tests in critical modules".to_string(),
            ],
            architecture_improvements: vec![
                "Consider microservices for scalability".to_string(),
                "Implement caching layer".to_string(),
            ],
            user_experience_issues: vec![
                "Slow response times".to_string(),
                "Inconsistent UI patterns".to_string(),
            ],
            technical_debt: vec![
                "Legacy code without tests".to_string(),
                "Tight coupling between modules".to_string(),
            ],
            security_concerns: vec![
                "Missing input validation".to_string(),
                "Weak authentication mechanisms".to_string(),
            ],
            maintainability_issues: vec![
                "Complex dependencies".to_string(),
                "Poor documentation".to_string(),
            ],
        })
    }

    pub async fn generate_improvement_plan(&self, analysis: &SystemAnalysis) -> Result<Vec<ImprovementPlan>> {
        let prompt = format!(
            r#"Based on this system analysis:
            Performance: {:?}
            Code Quality: {:?}
            Architecture: {:?}
            UX: {:?}
            Technical Debt: {:?}
            Security: {:?}
            Maintainability: {:?}
            
            Generate specific improvement plans with priorities and implementation steps.
            "#,
            analysis.performance_bottlenecks,
            analysis.code_quality_issues,
            analysis.architecture_improvements,
            analysis.user_experience_issues,
            analysis.technical_debt,
            analysis.security_concerns,
            analysis.maintainability_issues
        );

        let plans = self.ai.chat(&prompt).await?;
        
        // For now, returning a basic improvement plan
        Ok(vec![
            ImprovementPlan {
                title: "Optimize Database Queries".to_string(),
                description: "Address performance bottlenecks in database queries".to_string(),
                priority: Priority::High,
                impact: Impact::High,
                implementation_steps: vec![
                    "Profile slow queries".to_string(),
                    "Add appropriate indexes".to_string(),
                    "Optimize query patterns".to_string(),
                ],
                estimated_effort: "Medium".to_string(),
                expected_benefits: vec!["Improved response time".to_string(), "Better user experience".to_string()],
                dependencies: vec![],
            }
        ])
    }

    pub async fn evolve_agent_capabilities(&self) -> Result<String> {
        let prompt = r#"Analyze your own capabilities as an AI agent. Identify areas for improvement in:
        - Reasoning effectiveness
        - Task completion accuracy
        - Communication clarity
        - Problem-solving approach
        - Learning from interactions
        
        Propose specific improvements to your own functioning.
        "#.to_string();

        self.ai.chat(&prompt).await
    }
}

#[async_trait]
impl Agent for MetaAgent {
    async fn plan(&self, state: &AgentState) -> Result<String> {
        let prompt = format!(
            "As a self-improving agent, given this analysis task: {}\n\nPlan how to improve system capabilities based on the findings.",
            state.task
        );
        
        self.ai.chat(&prompt).await
    }

    async fn act(&self, plan: &str) -> Result<String> {
        // Execute the planned self-improvement action
        let prompt = format!(
            "Implement this self-improvement plan: {}\n\nExecute changes to enhance system capabilities.",
            plan
        );
        
        self.ai.chat(&prompt).await
    }

    async fn observe(&self, result: &str) -> Result<String> {
        // Analyze the improvement results
        let prompt = format!(
            "Analyze these self-improvement results: {}\n\nHow effective were these changes? What further improvements are needed?",
            result
        );
        
        self.ai.chat(&prompt).await
    }
}