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112 lines
4.3 KiB
Python
112 lines
4.3 KiB
Python
from typing import Any, Dict, Optional, Tuple, Type
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from letta.log import get_logger
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from letta.orm.enums import ToolType
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from letta.schemas.agent import AgentState
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from letta.schemas.sandbox_config import SandboxConfig, SandboxRunResult
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from letta.schemas.tool import Tool
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from letta.schemas.user import User
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from letta.services.tool_executor.tool_executor import (
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ExternalComposioToolExecutor,
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ExternalMCPToolExecutor,
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LettaCoreToolExecutor,
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LettaMemoryToolExecutor,
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LettaMultiAgentToolExecutor,
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SandboxToolExecutor,
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ToolExecutor,
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)
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from letta.tracing import trace_method
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from letta.utils import get_friendly_error_msg
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class ToolExecutorFactory:
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"""Factory for creating appropriate tool executors based on tool type."""
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_executor_map: Dict[ToolType, Type[ToolExecutor]] = {
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ToolType.LETTA_CORE: LettaCoreToolExecutor,
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ToolType.LETTA_MULTI_AGENT_CORE: LettaMultiAgentToolExecutor,
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ToolType.LETTA_MEMORY_CORE: LettaMemoryToolExecutor,
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ToolType.EXTERNAL_COMPOSIO: ExternalComposioToolExecutor,
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ToolType.EXTERNAL_MCP: ExternalMCPToolExecutor,
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}
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@classmethod
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def get_executor(cls, tool_type: ToolType) -> ToolExecutor:
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"""Get the appropriate executor for the given tool type."""
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executor_class = cls._executor_map.get(tool_type)
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if executor_class:
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return executor_class()
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# Default to sandbox executor for unknown types
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return SandboxToolExecutor()
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class ToolExecutionManager:
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"""Manager class for tool execution operations."""
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def __init__(
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self,
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agent_state: AgentState,
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actor: User,
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sandbox_config: Optional[SandboxConfig] = None,
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sandbox_env_vars: Optional[Dict[str, Any]] = None,
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):
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self.agent_state = agent_state
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self.logger = get_logger(__name__)
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self.actor = actor
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self.sandbox_config = sandbox_config
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self.sandbox_env_vars = sandbox_env_vars
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def execute_tool(self, function_name: str, function_args: dict, tool: Tool) -> Tuple[Any, Optional[SandboxRunResult]]:
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"""
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Execute a tool and persist any state changes.
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Args:
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function_name: Name of the function to execute
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function_args: Arguments to pass to the function
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tool: Tool object containing metadata about the tool
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Returns:
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Tuple containing the function response and sandbox run result (if applicable)
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"""
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try:
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# Get the appropriate executor for this tool type
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executor = ToolExecutorFactory.get_executor(tool.tool_type)
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# Execute the tool
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return executor.execute(
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function_name, function_args, self.agent_state, tool, self.actor, self.sandbox_config, self.sandbox_env_vars
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)
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except Exception as e:
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self.logger.error(f"Error executing tool {function_name}: {str(e)}")
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error_message = get_friendly_error_msg(function_name=function_name, exception_name=type(e).__name__, exception_message=str(e))
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return error_message, SandboxRunResult(status="error")
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@trace_method
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async def execute_tool_async(self, function_name: str, function_args: dict, tool: Tool) -> Tuple[Any, Optional[SandboxRunResult]]:
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"""
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Execute a tool asynchronously and persist any state changes.
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"""
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try:
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# Get the appropriate executor for this tool type
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# TODO: Extend this async model to composio
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if tool.tool_type == ToolType.CUSTOM:
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executor = SandboxToolExecutor()
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result_tuple = await executor.execute(function_name, function_args, self.agent_state, tool, self.actor)
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else:
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executor = ToolExecutorFactory.get_executor(tool.tool_type)
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result_tuple = executor.execute(function_name, function_args, self.agent_state, tool, self.actor)
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return result_tuple
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except Exception as e:
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self.logger.error(f"Error executing tool {function_name}: {str(e)}")
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error_message = get_friendly_error_msg(
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function_name=function_name,
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exception_name=type(e).__name__,
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exception_message=str(e),
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)
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return error_message, SandboxRunResult(status="error")
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