# OrKa: Orchestrator Kit Agents
# Copyright © 2025 Marco Somma
#
# This file is part of OrKa – https://github.com/marcosomma/orka-resoning
#
# Licensed under the Apache License, Version 2.0 (Apache 2.0).
# You may not use this file for commercial purposes without explicit permission.
#
# Full license: https://www.apache.org/licenses/LICENSE-2.0
# For commercial use, contact: marcosomma.work@gmail.com
#
# Required attribution: OrKa by Marco Somma – https://github.com/marcosomma/orka-resoning

"""
🧠 **Agents Domain** - Intelligent Processing Units
================================================

This module defines the foundation for all OrKa agents - the cognitive building blocks
of your AI workflows. Agents are specialized processing units that transform inputs
into structured outputs while maintaining context and handling errors gracefully.

**Core Agent Philosophy:**
Think of agents as expert consultants in your workflow - each with specialized knowledge
and capabilities, working together to solve complex problems. They provide:

- 🎯 **Specialized Intelligence**: Each agent excels at specific tasks
- 🧠 **Context Awareness**: Maintains conversation and processing context
- 🔄 **Error Resilience**: Graceful failure handling with fallback strategies
- ⚡ **Performance**: Async execution with concurrency control
- 🔧 **Flexibility**: Support for both cloud LLMs and local models

**Agent Types:**
- **Classification Agents**: Route and categorize inputs intelligently
- **Answer Builders**: Synthesize complex information into coherent responses
- **Binary Agents**: Make precise true/false decisions
- **Memory Agents**: Store and retrieve contextual information
- **Tool Agents**: Integrate with external services and APIs

**Real-world Applications:**
- Customer service workflows with intelligent routing
- Content moderation with multi-stage validation
- Research assistants that combine search and synthesis
- Conversational AI with persistent memory
"""

import abc
import logging
import uuid
from datetime import datetime
from typing import Any, List, Optional, TypeVar, Union

from ..contracts import Context, Output, Registry
from ..utils.concurrency import ConcurrencyManager

logger = logging.getLogger(__name__)

T = TypeVar("T")


class BaseAgent:
    """
    🧠 **Foundation for all OrKa agents** - the cognitive building blocks of your workflows.

    **What makes agents special:**
    - **Intelligent Processing**: Transform raw inputs into structured, meaningful outputs
    - **Context Preservation**: Maintain conversation flow and processing history
    - **Async Performance**: Non-blocking execution with timeout and concurrency control
    - **Error Intelligence**: Graceful failure handling with detailed diagnostics
    - **Resource Management**: Automatic initialization and cleanup of dependencies

    **Agent Lifecycle:**
    1. **Initialization**: Set up resources and validate configuration
    2. **Execution**: Process inputs with full context awareness
    3. **Result Handling**: Structure outputs for downstream processing
    4. **Cleanup**: Release resources and maintain system health

    **Perfect for building:**
    - Multi-step reasoning workflows
    - Intelligent content processing pipelines
    - Context-aware conversational systems
    - Fault-tolerant distributed AI applications

    **Example Usage:**
    ```python
    # Create a specialized agent
    classifier = OpenAIClassificationAgent(
        agent_id="intent_classifier",
        options=["question", "request", "complaint"],
        prompt="Classify user intent: {{ input }}"
    )

    # Execute with context
    result = await classifier.run({
        "input": "How do I reset my password?",
        "user_id": "user123",
        "session_context": previous_interactions
    })
    ```
    """

    def __init__(
        self,
        agent_id: str,
        registry: Optional[Registry] = None,
        prompt: Optional[str] = None,
        queue: Optional[List[str]] = None,
        timeout: Optional[float] = 30.0,
        max_concurrency: int = 10,
        **kwargs,
    ):
        """
        Initialize the base agent with common properties.

        Args:
            agent_id (str): Unique identifier for the agent
            registry (Registry, optional): Resource registry for dependency injection
            prompt (str, optional): Prompt or instruction for the agent (legacy)
            queue (List[str], optional): Queue of agents or nodes (legacy)
            timeout (Optional[float]): Maximum execution time in seconds
            max_concurrency (int): Maximum number of concurrent executions
            **kwargs: Additional parameters specific to the agent type
        """
        self.agent_id = agent_id
        self.registry = registry
        self.timeout = timeout
        self.concurrency = ConcurrencyManager(max_concurrency=max_concurrency)
        self._initialized = False

        # Legacy attributes
        self.prompt = prompt
        self.queue = queue
        self.params = kwargs
        self.type = self.__class__.__name__.lower()

    async def initialize(self) -> None:
        """
        Initialize the agent and its resources.

        This method is called automatically before the first execution and
        should be overridden by derived classes to set up any required resources.
        """
        if self._initialized:
            return
        self._initialized = True

    async def run(self, ctx: Union[Context, Any]) -> Union[Output, Any]:
        """
        Run the agent with the given context.

        This method handles the execution workflow including:
        - Lazy initialization of the agent
        - Adding trace information to the context
        - Managing concurrency and timeouts
        - Standardizing error handling and result formatting

        Args:
            ctx: The execution context containing input and metadata.
                Can be a Context object for modern agents or any input for legacy agents.

        Returns:
            Output or Any: Standardized output for modern agents or direct result for legacy agents
        """
        if not self._initialized:
            await self.initialize()

        # Check if this is a legacy call pattern
        if hasattr(self, "_is_legacy_agent") and self._is_legacy_agent():
            # Call the legacy implementation
            if hasattr(self, "run") and not isinstance(self.run, type(BaseAgent.run)):
                return self.run(ctx)
            # Default to calling _run_legacy for compatibility
            return await self._run_legacy(ctx)

        # Modern agent pattern - process the context
        if not isinstance(ctx, dict):
            ctx = {"input": ctx}

        # Add trace information if not present
        if "trace_id" not in ctx:
            ctx["trace_id"] = str(uuid.uuid4())
        if "timestamp" not in ctx:
            ctx["timestamp"] = datetime.now()

        try:
            # Use concurrency manager to run the agent
            result = await self.concurrency.run_with_timeout(
                self._run_impl,
                self.timeout,
                ctx,
            )
            return Output(
                result=result,
                status="success",
                error=None,
                metadata={"agent_id": self.agent_id},
            )
        except Exception as e:
            logger.error(f"Agent {self.agent_id} failed: {e!s}")
            return Output(
                result=None,
                status="error",
                error=str(e),
                metadata={"agent_id": self.agent_id},
            )

    async def _run_impl(self, ctx: Context) -> Any:
        """
        Implementation of the agent's run logic.

        This method must be implemented by all derived agent classes to
        provide the specific execution logic for that agent type.

        Args:
            ctx (Context): The execution context containing input and metadata

        Returns:
            Any: The result of the agent's processing

        Raises:
            NotImplementedError: If not implemented by a subclass
        """
        raise NotImplementedError("Subclasses must implement _run_impl")

    async def _run_legacy(self, input_data: Any) -> Any:
        """
        Legacy implementation that modern async classes should override
        if they need to support the legacy sync interface.

        Args:
            input_data: The input data to process

        Returns:
            Any: The result of processing the input data

        Raises:
            NotImplementedError: If not implemented by a subclass that needs legacy support
        """
        raise NotImplementedError(
            "Legacy agents must implement _run_legacy or override run",
        )

    async def cleanup(self) -> None:
        """
        Clean up agent resources.

        This method should be called when the agent is no longer needed to
        release any resources it may be holding, such as network connections,
        file handles, or memory.
        """
        await self.concurrency.shutdown()

    def __repr__(self):
        """
        Return a string representation of the agent.

        Returns:
            str: String representation showing agent class, ID, and queue.
        """
        return f"<{self.__class__.__name__} id={self.agent_id} queue={self.queue}>"


# Legacy abstract base class for backward compatibility
class LegacyBaseAgent(abc.ABC, BaseAgent):
    """
    Abstract base class for legacy agents in the OrKa framework.
    Provides compatibility with the older synchronous agent pattern.

    New agent implementations should use BaseAgent directly with async methods.
    This class exists only for backward compatibility.
    """

    def __init__(self, agent_id, prompt, queue, **kwargs):
        """
        Initialize the legacy base agent.

        Args:
            agent_id (str): Unique identifier for the agent.
            prompt (str): Prompt or instruction for the agent.
            queue (list): Queue of agents or nodes to be processed.
            **kwargs: Additional parameters specific to the agent type.
        """
        super().__init__(agent_id=agent_id, prompt=prompt, queue=queue, **kwargs)

    def _is_legacy_agent(self):
        """Identify this as a legacy agent for the unified run method"""
        return True

    @abc.abstractmethod
    def run(self, input_data):
        """
        Abstract method to run the agent's reasoning process.
        Must be implemented by all concrete agent classes.

        Args:
            input_data: Input data for the agent to process.

        Returns:
            The result of the agent's processing.

        Raises:
            NotImplementedError: If not implemented by a subclass.
        """
