"""
Learning Agent for the Agents Hub framework.

This module provides a learning agent that improves through experience.

TODO: Implement the LearningAgent class.
"""

from agents_hub import Agent
from agents_hub.llm.base import BaseLLM
from typing import Dict, Any, Optional, List

class LearningAgent(Agent):
    """
    Agent that learns and improves through experience.
    
    TODO: Implement learning capabilities.
    """
    
    def __init__(
        self,
        name: str,
        llm: BaseLLM,
        learning_rate: float = 0.1,
        feedback_integration: str = "immediate",
        system_prompt: Optional[str] = None,
        tools: Optional[List[Any]] = None,
        **kwargs
    ):
        """
        Initialize the learning agent.
        
        Args:
            name: Agent name
            llm: LLM provider
            learning_rate: Rate at which the agent learns from feedback
            feedback_integration: How feedback is integrated ("immediate" or "batch")
            system_prompt: System prompt for the agent
            tools: Optional list of tools
            **kwargs: Additional arguments
        """
        super().__init__(
            name=name,
            llm=llm,
            system_prompt=system_prompt or "You are a helpful assistant that learns from interactions.",
            tools=tools or [],
            **kwargs
        )
        
        self.learning_rate = learning_rate
        self.feedback_integration = feedback_integration
        self.feedback_history = []
    
    async def provide_feedback(self, feedback: str, score: float, conversation_id: str) -> None:
        """
        Provide feedback to help the agent learn.
        
        Args:
            feedback: Feedback text
            score: Feedback score (0.0 to 1.0)
            conversation_id: Conversation identifier
        """
        # TODO: Implement feedback integration
        self.feedback_history.append({
            "feedback": feedback,
            "score": score,
            "conversation_id": conversation_id,
            "timestamp": "2023-06-15T14:30:00Z",  # Replace with actual timestamp
        })
        
        # Log the feedback
        self.logger.info(f"Received feedback for conversation {conversation_id}: {score}")
        
        # If immediate feedback integration, update the agent
        if self.feedback_integration == "immediate":
            await self._integrate_feedback()
    
    async def _integrate_feedback(self) -> None:
        """
        Integrate feedback to improve the agent.
        
        TODO: Implement feedback integration.
        """
        # This is a placeholder for the actual implementation
        self.logger.info("Integrating feedback (placeholder implementation)")
        
        # In a real implementation, this would:
        # 1. Analyze feedback patterns
        # 2. Update the agent's behavior based on feedback
        # 3. Adjust the system prompt or other parameters
        # 4. Track learning progress
