chore: officially migrate to submodule (#4502)
* remove apps/core and apps/fern * fix precommit * add submodule updates in workflows * submodule * remove core tests * update core revision * Add submodules: true to all GitHub workflows - Ensure all workflows can access git submodules - Add submodules support to deployment, test, and CI workflows - Fix YAML syntax issues in workflow files 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> * remove core-lint * upgrade core with latest main of oss --------- Co-authored-by: Claude <noreply@anthropic.com>
This commit is contained in:
Kian Jones
@@ -1,162 +0,0 @@
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import asyncio
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import traceback
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from typing import Any, Dict, Optional, Type
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from letta.constants import FUNCTION_RETURN_VALUE_TRUNCATED
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from letta.helpers.datetime_helpers import AsyncTimer
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from letta.log import get_logger
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from letta.otel.context import get_ctx_attributes
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from letta.otel.metric_registry import MetricRegistry
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from letta.otel.tracing import trace_method
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from letta.schemas.agent import AgentState
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from letta.schemas.enums import ToolType
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from letta.schemas.sandbox_config import SandboxConfig
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from letta.schemas.tool import Tool
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from letta.schemas.tool_execution_result import ToolExecutionResult
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from letta.schemas.user import User
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from letta.services.agent_manager import AgentManager
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from letta.services.block_manager import BlockManager
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from letta.services.job_manager import JobManager
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from letta.services.message_manager import MessageManager
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from letta.services.passage_manager import PassageManager
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from letta.services.tool_executor.builtin_tool_executor import LettaBuiltinToolExecutor
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from letta.services.tool_executor.composio_tool_executor import ExternalComposioToolExecutor
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from letta.services.tool_executor.core_tool_executor import LettaCoreToolExecutor
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from letta.services.tool_executor.files_tool_executor import LettaFileToolExecutor
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from letta.services.tool_executor.mcp_tool_executor import ExternalMCPToolExecutor
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from letta.services.tool_executor.multi_agent_tool_executor import LettaMultiAgentToolExecutor
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from letta.services.tool_executor.sandbox_tool_executor import SandboxToolExecutor
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from letta.services.tool_executor.tool_executor_base import ToolExecutor
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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_MEMORY_CORE: LettaCoreToolExecutor,
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ToolType.LETTA_SLEEPTIME_CORE: LettaCoreToolExecutor,
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ToolType.LETTA_MULTI_AGENT_CORE: LettaMultiAgentToolExecutor,
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ToolType.LETTA_BUILTIN: LettaBuiltinToolExecutor,
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ToolType.LETTA_FILES_CORE: LettaFileToolExecutor,
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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(
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cls,
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tool_type: ToolType,
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message_manager: MessageManager,
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agent_manager: AgentManager,
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block_manager: BlockManager,
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job_manager: JobManager,
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passage_manager: PassageManager,
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actor: User,
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) -> 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, SandboxToolExecutor)
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return executor_class(
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message_manager=message_manager,
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agent_manager=agent_manager,
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block_manager=block_manager,
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job_manager=job_manager,
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passage_manager=passage_manager,
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actor=actor,
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)
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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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message_manager: MessageManager,
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agent_manager: AgentManager,
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block_manager: BlockManager,
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job_manager: JobManager,
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passage_manager: PassageManager,
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actor: User,
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agent_state: Optional[AgentState] = None,
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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.message_manager = message_manager
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self.agent_manager = agent_manager
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self.block_manager = block_manager
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self.job_manager = job_manager
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self.passage_manager = passage_manager
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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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@trace_method
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async def execute_tool_async(
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self, function_name: str, function_args: dict, tool: Tool, step_id: str | None = None
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) -> ToolExecutionResult:
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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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status = "error" # set as default for tracking purposes
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try:
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executor = ToolExecutorFactory.get_executor(
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tool.tool_type,
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message_manager=self.message_manager,
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agent_manager=self.agent_manager,
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block_manager=self.block_manager,
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job_manager=self.job_manager,
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passage_manager=self.passage_manager,
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actor=self.actor,
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)
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def _metrics_callback(exec_time_ms: int, exc):
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return MetricRegistry().tool_execution_time_ms_histogram.record(
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exec_time_ms, dict(get_ctx_attributes(), **{"tool.name": tool.name})
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)
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async with AsyncTimer(callback_func=_metrics_callback):
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result = await executor.execute(
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function_name, function_args, tool, self.actor, self.agent_state, self.sandbox_config, self.sandbox_env_vars
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)
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status = result.status
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# trim result
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return_str = str(result.func_return)
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if len(return_str) > tool.return_char_limit:
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# TODO: okay that this become a string?
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result.func_return = FUNCTION_RETURN_VALUE_TRUNCATED(return_str, len(return_str), tool.return_char_limit)
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return result
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except asyncio.CancelledError as e:
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self.logger.error(f"Aysnc cancellation 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 ToolExecutionResult(
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status="error",
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func_return=error_message,
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stderr=[traceback.format_exc()],
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)
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except Exception as e:
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status = "error"
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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 ToolExecutionResult(
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status="error",
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func_return=error_message,
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stderr=[traceback.format_exc()],
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)
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finally:
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metric_attrs = {"tool.name": tool.name, "tool.execution_success": status == "success"}
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if status == "error" and step_id:
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metric_attrs["step.id"] = step_id
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MetricRegistry().tool_execution_counter.add(1, dict(get_ctx_attributes(), **metric_attrs))
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