import traceback
from typing import Any, Dict, Optional, Type

from letta.log import get_logger
from letta.orm.enums import ToolType
from letta.schemas.agent import AgentState
from letta.schemas.sandbox_config import SandboxConfig
from letta.schemas.tool import Tool
from letta.schemas.tool_execution_result import ToolExecutionResult
from letta.schemas.user import User
from letta.services.tool_executor.tool_executor import (
    ExternalComposioToolExecutor,
    ExternalMCPToolExecutor,
    LettaCoreToolExecutor,
    LettaMultiAgentToolExecutor,
    SandboxToolExecutor,
    ToolExecutor,
)
from letta.tracing import trace_method
from letta.utils import get_friendly_error_msg


class ToolExecutorFactory:
    """Factory for creating appropriate tool executors based on tool type."""

    _executor_map: Dict[ToolType, Type[ToolExecutor]] = {
        ToolType.LETTA_CORE: LettaCoreToolExecutor,
        ToolType.LETTA_MEMORY_CORE: LettaCoreToolExecutor,
        ToolType.LETTA_SLEEPTIME_CORE: LettaCoreToolExecutor,
        ToolType.LETTA_MULTI_AGENT_CORE: LettaMultiAgentToolExecutor,
        ToolType.EXTERNAL_COMPOSIO: ExternalComposioToolExecutor,
        ToolType.EXTERNAL_MCP: ExternalMCPToolExecutor,
    }

    @classmethod
    def get_executor(cls, tool_type: ToolType) -> ToolExecutor:
        """Get the appropriate executor for the given tool type."""
        executor_class = cls._executor_map.get(tool_type, SandboxToolExecutor)
        return executor_class()


class ToolExecutionManager:
    """Manager class for tool execution operations."""

    def __init__(
        self,
        agent_state: AgentState,
        actor: User,
        sandbox_config: Optional[SandboxConfig] = None,
        sandbox_env_vars: Optional[Dict[str, Any]] = None,
    ):
        self.agent_state = agent_state
        self.logger = get_logger(__name__)
        self.actor = actor
        self.sandbox_config = sandbox_config
        self.sandbox_env_vars = sandbox_env_vars

    def execute_tool(self, function_name: str, function_args: dict, tool: Tool) -> ToolExecutionResult:
        """
        Execute a tool and persist any state changes.

        Args:
            function_name: Name of the function to execute
            function_args: Arguments to pass to the function
            tool: Tool object containing metadata about the tool

        Returns:
            Tuple containing the function response and sandbox run result (if applicable)
        """
        try:
            executor = ToolExecutorFactory.get_executor(tool.tool_type)
            return executor.execute(
                function_name,
                function_args,
                self.agent_state,
                tool,
                self.actor,
                self.sandbox_config,
                self.sandbox_env_vars,
            )

        except Exception as e:
            self.logger.error(f"Error executing tool {function_name}: {str(e)}")
            error_message = get_friendly_error_msg(
                function_name=function_name,
                exception_name=type(e).__name__,
                exception_message=str(e),
            )
            return ToolExecutionResult(
                status="error",
                func_return=error_message,
                stderr=[traceback.format_exc()],
            )

    @trace_method
    async def execute_tool_async(self, function_name: str, function_args: dict, tool: Tool) -> ToolExecutionResult:
        """
        Execute a tool asynchronously and persist any state changes.
        """
        try:
            executor = ToolExecutorFactory.get_executor(tool.tool_type)
            # TODO: Extend this async model to composio
            if isinstance(executor, (SandboxToolExecutor, ExternalComposioToolExecutor)):
                result = await executor.execute(function_name, function_args, self.agent_state, tool, self.actor)
            else:
                result = executor.execute(function_name, function_args, self.agent_state, tool, self.actor)
            return result

        except Exception as e:
            self.logger.error(f"Error executing tool {function_name}: {str(e)}")
            error_message = get_friendly_error_msg(
                function_name=function_name,
                exception_name=type(e).__name__,
                exception_message=str(e),
            )
            return ToolExecutionResult(
                status="error",
                func_return=error_message,
                stderr=[traceback.format_exc()],
            )