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49e148a2f9
Co-authored-by: bytecraftii <bytecraftii@users.noreply.github.com> Co-authored-by: Wendong-Fan <w3ndong.fan@gmail.com>
803 lines
33 KiB
Python
803 lines
33 KiB
Python
# ========= Copyright 2025-2026 @ Eigent.ai All Rights Reserved. =========
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ========= Copyright 2025-2026 @ Eigent.ai All Rights Reserved. =========
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import asyncio
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import logging
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from typing import Generator, List, Optional
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from app.component import code
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from app.exception.exception import UserException
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from app.service.task import (Action, ActionAssignTaskData, ActionEndData,
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ActionTaskStateData, ActionTimeoutData,
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get_camel_task, get_task_lock)
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from app.utils.agent import ListenChatAgent
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from app.utils.single_agent_worker import SingleAgentWorker
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from app.utils.telemetry.workforce_metrics import WorkforceMetricsCallback
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from camel.agents import ChatAgent
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from camel.societies.workforce.base import BaseNode
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from camel.societies.workforce.events import (TaskAssignedEvent,
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TaskCompletedEvent,
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TaskCreatedEvent,
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TaskFailedEvent,
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WorkerCreatedEvent)
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from camel.societies.workforce.prompts import TASK_DECOMPOSE_PROMPT
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from camel.societies.workforce.task_channel import TaskChannel
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from camel.societies.workforce.utils import (FailureHandlingConfig,
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TaskAssignResult)
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from camel.societies.workforce.workforce import DEFAULT_WORKER_POOL_SIZE
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from camel.societies.workforce.workforce import Workforce as BaseWorkforce
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from camel.societies.workforce.workforce import WorkforceState
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from camel.societies.workforce.workforce_metrics import WorkforceMetrics
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from camel.tasks.task import Task, TaskState, validate_task_content
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logger = logging.getLogger("workforce")
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class Workforce(BaseWorkforce):
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def __init__(
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self,
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api_task_id: str,
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description: str,
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children: List[BaseNode] | None = None,
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coordinator_agent: ChatAgent | None = None,
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task_agent: ChatAgent | None = None,
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new_worker_agent: ChatAgent | None = None,
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graceful_shutdown_timeout: float = 3,
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share_memory: bool = False,
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use_structured_output_handler: bool = True,
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) -> None:
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self.api_task_id = api_task_id
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logger.info("=" * 80)
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logger.info("🏭 [WF-LIFECYCLE] Workforce.__init__ STARTED",
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extra={"api_task_id": api_task_id})
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logger.info(f"[WF-LIFECYCLE] Workforce id will be: {id(self)}")
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logger.info(
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f"[WF-LIFECYCLE] Init params: graceful_shutdown_timeout="
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f"{graceful_shutdown_timeout}, share_memory={share_memory}")
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logger.info("=" * 80)
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super().__init__(
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description=description,
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children=children,
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coordinator_agent=coordinator_agent,
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task_agent=task_agent,
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new_worker_agent=new_worker_agent,
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graceful_shutdown_timeout=graceful_shutdown_timeout,
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share_memory=share_memory,
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use_structured_output_handler=use_structured_output_handler,
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task_timeout_seconds=3600, # 60 minutes
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failure_handling_config=FailureHandlingConfig(
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enabled_strategies=["retry", "replan"], ),
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)
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self.task_agent.stream_accumulate = True
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self.task_agent._stream_accumulate_explicit = True
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logger.info(
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f"[WF-LIFECYCLE] ✅ Workforce.__init__ COMPLETED, id={id(self)}")
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def eigent_make_sub_tasks(
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self,
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task: Task,
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coordinator_context: str = "",
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on_stream_batch=None,
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on_stream_text=None,
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):
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"""Split process_task method to eigent_make_sub_tasks
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and eigent_start method.
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Args:
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task: The main task to decompose
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coordinator_context: Optional context ONLY for coordinator
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agent during decomposition. This context will NOT
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be passed to subtasks or worker agents.
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on_stream_batch: Optional callback for streaming
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batches signature (List[Task], bool)
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on_stream_text: Optional callback for raw
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streaming text chunks
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"""
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logger.debug("[DECOMPOSE] eigent_make_sub_tasks called",
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extra={
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"api_task_id": self.api_task_id,
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"task_id": task.id
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})
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if not validate_task_content(task.content, task.id):
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task.state = TaskState.FAILED
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task.result = "Task failed: Invalid or empty content provided"
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logger.warning(
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"[DECOMPOSE] Task rejected: Invalid or empty content",
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extra={
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"task_id":
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task.id,
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"content_preview":
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task.content[:50] +
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"..." if len(task.content) > 50 else task.content
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})
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raise UserException(code.error, task.result)
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self.reset()
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self._task = task
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self.set_channel(TaskChannel())
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self._state = WorkforceState.RUNNING
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task.state = TaskState.OPEN
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subtasks = asyncio.run(
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self.handle_decompose_append_task(
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task,
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reset=False,
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coordinator_context=coordinator_context,
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on_stream_batch=on_stream_batch,
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on_stream_text=on_stream_text))
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logger.info("[DECOMPOSE] Task decomposition completed",
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extra={
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"api_task_id": self.api_task_id,
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"task_id": task.id,
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"subtasks_count": len(subtasks)
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})
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return subtasks
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async def eigent_start(self, subtasks: list[Task]):
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"""start the workforce"""
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logger.debug((f"[WF-LIFECYCLE] eigent_start called with "
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f"{len(subtasks)} subtasks"),
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extra={"api_task_id": self.api_task_id})
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# Clear existing pending tasks to use the user-edited task list
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# (tasks may have been added during decomposition before user edits)
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self._pending_tasks.clear()
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self._pending_tasks.extendleft(reversed(subtasks))
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self.save_snapshot("Initial task decomposition")
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try:
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await self.start()
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except Exception as e:
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logger.error(f"[WF-LIFECYCLE] Error in workforce execution: {e}",
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extra={
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"api_task_id": self.api_task_id,
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"error": str(e)
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},
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exc_info=True)
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self._state = WorkforceState.STOPPED
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raise
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finally:
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if self._state != WorkforceState.STOPPED:
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self._state = WorkforceState.IDLE
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def _decompose_task(self, task: Task, stream_callback=None):
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"""Decompose task with optional streaming text callback."""
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decompose_prompt = str(
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TASK_DECOMPOSE_PROMPT.format(
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content=task.content,
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child_nodes_info=self._get_child_nodes_info(),
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additional_info=task.additional_info,
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))
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self.task_agent.reset()
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result = task.decompose(self.task_agent,
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decompose_prompt,
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stream_callback=stream_callback)
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if isinstance(result, Generator):
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def streaming_with_dependencies():
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all_subtasks = []
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for new_tasks in result:
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all_subtasks.extend(new_tasks)
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if new_tasks:
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self._update_dependencies_for_decomposition(
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task, all_subtasks)
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yield new_tasks
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return streaming_with_dependencies()
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else:
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subtasks = result
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if subtasks:
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self._update_dependencies_for_decomposition(task, subtasks)
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return subtasks
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async def handle_decompose_append_task(
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self,
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task: Task,
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reset: bool = True,
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coordinator_context: str = "",
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on_stream_batch=None,
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on_stream_text=None,
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) -> List[Task]:
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"""Override to support coordinator_context parameter.
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Handle task decomposition and validation,
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then append to pending tasks.
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Args:
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task: The task to be processed
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reset: Should trigger workforce reset
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(Workforce must not be running)
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coordinator_context: Optional context ONLY for
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coordinator during decomposition
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on_stream_batch: Optional callback for streaming
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batches signature (List[Task], bool)
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on_stream_text: Optional callback for raw streaming text chunks
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Returns:
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List[Task]: The decomposed subtasks or the original task
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"""
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logger.debug(f"[DECOMPOSE] handle_decompose_append_task called, "
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f"task_id={task.id}, reset={reset}")
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if not validate_task_content(task.content, task.id):
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task.state = TaskState.FAILED
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task.result = "Task failed: Invalid or empty content provided"
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logger.warning(f"[DECOMPOSE] Task {task.id} rejected: "
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f"Invalid or empty content. "
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f"Content preview: '{task.content}'")
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return [task]
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if reset and self._state != WorkforceState.RUNNING:
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self.reset()
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self._task = task
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task.state = TaskState.FAILED
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if coordinator_context:
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original_content = task.content
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task_with_context = (coordinator_context +
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"\n=== CURRENT TASK ===\n" + original_content)
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task.content = task_with_context
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subtasks_result = self._decompose_task(
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task, stream_callback=on_stream_text)
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task.content = original_content
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else:
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subtasks_result = self._decompose_task(
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task, stream_callback=on_stream_text)
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if isinstance(subtasks_result, Generator):
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subtasks = []
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for new_tasks in subtasks_result:
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subtasks.extend(new_tasks)
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if on_stream_batch:
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try:
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on_stream_batch(new_tasks, False)
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except Exception as e:
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logger.warning(f"Streaming callback failed: {e}")
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# After consuming the generator, check task.subtasks
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# for final result as fallback
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if not subtasks and task.subtasks:
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subtasks = task.subtasks
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else:
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subtasks = subtasks_result
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if subtasks:
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self._pending_tasks.extendleft(reversed(subtasks))
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# Log task created events
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metrics_callbacks = [
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cb for cb in self._callbacks
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if isinstance(cb, WorkforceMetrics)
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]
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if metrics_callbacks:
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for subtask in subtasks:
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event = TaskCreatedEvent(
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task_id=subtask.id,
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description=subtask.content,
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parent_task_id=task.id if task else None,
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task_type=None,
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)
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metrics_callbacks[0].log_task_created(event)
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if not subtasks:
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logger.warning(
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"[DECOMPOSE] No subtasks returned, creating fallback task")
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fallback_task = Task(
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content=task.content,
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id=f"{task.id}.1",
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parent=task,
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)
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task.subtasks = [fallback_task]
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subtasks = [fallback_task]
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# Log fallback task created event
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metrics_callbacks = [
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cb for cb in self._callbacks
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if isinstance(cb, WorkforceMetrics)
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]
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if metrics_callbacks:
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event = TaskCreatedEvent(
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task_id=fallback_task.id,
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description=fallback_task.content,
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parent_task_id=task.id if task else None,
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task_type=None,
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)
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metrics_callbacks[0].log_task_created(event)
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if on_stream_batch:
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try:
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on_stream_batch(subtasks, True)
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except Exception as e:
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logger.warning(f"Final streaming callback failed: {e}")
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logger.debug(f"[DECOMPOSE] handle_decompose_append_task completed, "
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f"returned {len(subtasks)} subtasks")
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return subtasks
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def _get_agent_id_from_node_id(self, node_id: str) -> str | None:
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"""Map worker node_id to the actual agent_id for
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frontend communication.
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The CAMEL base class uses node_id for task assignment,
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but the frontend uses agent_id to identify agents.
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This method provides the mapping.
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"""
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for child in self._children:
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if hasattr(child, 'node_id') and child.node_id == node_id:
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if hasattr(child, 'worker') and hasattr(
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child.worker, 'agent_id'):
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return child.worker.agent_id
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return None
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def _extract_model_type(self, agent: ChatAgent) -> Optional[str]:
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"""Extract model type from agent's model_backend.
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Handles both ModelManager (multiple models) and single model cases.
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Args:
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agent: The chat agent to extract model type from
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Returns:
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Model type as string, or None if not found
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"""
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if not hasattr(agent, 'model_backend') or not agent.model_backend:
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return None
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model_obj = agent.model_backend
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# Handle ModelManager case (multiple models)
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if hasattr(model_obj, 'models') and model_obj.models:
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first_model = model_obj.models[0] if model_obj.models else None
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if first_model:
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mt = getattr(first_model, 'model_type', None)
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return str(
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mt.value if hasattr(mt, 'value') else mt) if mt else None
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# Handle single model case
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mt = getattr(model_obj, 'model_type', None)
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return str(mt.value if hasattr(mt, 'value') else mt) if mt else None
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async def _find_assignee(self, tasks: List[Task]) -> TaskAssignResult:
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# Task assignment phase: send "waiting for execution" notification
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# to the frontend, and send "start execution" notification when the
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# task actually begins execution
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assigned = await super()._find_assignee(tasks)
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task_lock = get_task_lock(self.api_task_id)
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for item in assigned.assignments:
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# DEBUG ▶ Task has been assigned to which worker
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# and its dependencies
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logger.debug(f"[WF] ASSIGN {item.task_id} -> {item.assignee_id} "
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f"deps={item.dependencies}")
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# The main task itself does not need notification
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if self._task and item.task_id == self._task.id:
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continue
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# Find task content
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task_obj = get_camel_task(item.task_id, tasks)
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if task_obj is None:
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logger.warning(
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f"[WF] WARN: Task {item.task_id} not found in "
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f"tasks list during ASSIGN phase. This may indicate "
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f"a task tree inconsistency.")
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content = ""
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else:
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content = task_obj.content
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# Skip sending notification if this is a retry/replan for
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# an already assigned task
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# This prevents the frontend from showing "Reassigned"
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# when a task is being retried with the same or different
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# worker due to failure recovery
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if task_obj and task_obj.assigned_worker_id:
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logger.debug(
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f"[WF] ASSIGN Skip notification for task {item.task_id}: "
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f"already has assigned_worker_id="
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f"{task_obj.assigned_worker_id}, "
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f"new assignee={item.assignee_id} (retry/replan scenario)")
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continue
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# Map node_id to agent_id for frontend communication
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# The CAMEL base class returns node_id as assignee_id,
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# but the frontend uses agent_id to identify agents
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agent_id = self._get_agent_id_from_node_id(item.assignee_id)
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if agent_id is None:
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workers = [
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c.node_id for c in self._children if hasattr(c, 'node_id')
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]
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logger.error(
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f"[WF] ERROR: Could not find agent_id for "
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f"node_id={item.assignee_id}. Task {item.task_id} "
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f"will not be properly tracked on frontend. "
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f"Available workers: {workers}")
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continue # Skip sending notification for unmapped worker
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# Asynchronously send waiting notification
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task = asyncio.create_task(
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task_lock.put_queue(
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ActionAssignTaskData(
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action=Action.assign_task,
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data={
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"assignee_id": agent_id,
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"task_id": item.task_id,
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"content": content,
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"state": "waiting", # Mark as waiting state
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"failure_count": 0,
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},
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)))
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# Track the task for cleanup
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task_lock.add_background_task(task)
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metrics_callbacks = [
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cb for cb in self._callbacks
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if isinstance(cb, WorkforceMetrics)
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]
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if metrics_callbacks:
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event = TaskAssignedEvent(
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task_id=item.task_id,
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worker_id=agent_id,
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dependencies=item.dependencies,
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)
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metrics_callbacks[0].log_task_assigned(event)
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return assigned
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async def _post_task(self, task: Task, assignee_id: str) -> None:
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# DEBUG ▶ Dependencies are met, the task really starts to execute
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logger.debug(f"[WF] POST {task.id} -> {assignee_id}")
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"""Override the _post_task method to notify the frontend
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when the task really starts to execute
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"""
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# When the dependency check is passed and the task is
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# about to be published to the execution queue, send a
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# notification to the frontend
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task_lock = get_task_lock(self.api_task_id)
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if self._task and task.id != self._task.id:
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# Skip the main task itself
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# Map node_id to agent_id for frontend communication
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agent_id = self._get_agent_id_from_node_id(assignee_id)
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workers = [
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c.node_id for c in self._children if hasattr(c, 'node_id')
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]
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if agent_id is None:
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logger.error(f"[WF] ERROR: Could not find agent_id "
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f"for node_id={assignee_id}. "
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f"Task {task.id} will not be properly "
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f"tracked on frontend. "
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f"Available workers: "
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f"{workers}")
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else:
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await task_lock.put_queue(
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ActionAssignTaskData(
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action=Action.assign_task,
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data={
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"assignee_id": agent_id,
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"task_id": task.id,
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"content": task.content,
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"state": "running", # running state
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"failure_count": task.failure_count,
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},
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))
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# Call the parent class method to continue the
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# normal task publishing process
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|
await super()._post_task(task, assignee_id)
|
|
|
|
def add_single_agent_worker(
|
|
self,
|
|
description: str,
|
|
worker: ListenChatAgent,
|
|
pool_max_size: int = DEFAULT_WORKER_POOL_SIZE,
|
|
enable_workflow_memory: bool = False,
|
|
) -> BaseWorkforce:
|
|
if self._state == WorkforceState.RUNNING:
|
|
raise RuntimeError(
|
|
"Cannot add workers while workforce is running. "
|
|
"Pause the workforce first.")
|
|
|
|
# Validate worker agent compatibility
|
|
self._validate_agent_compatibility(worker, "Worker agent")
|
|
|
|
# Ensure the worker agent shares this workforce's pause control
|
|
self._attach_pause_event_to_agent(worker)
|
|
|
|
worker_node = SingleAgentWorker(
|
|
description=description,
|
|
worker=worker,
|
|
pool_max_size=pool_max_size,
|
|
use_structured_output_handler=self.use_structured_output_handler,
|
|
context_utility=None,
|
|
enable_workflow_memory=enable_workflow_memory,
|
|
)
|
|
self._children.append(worker_node)
|
|
|
|
# If we have a channel set up, set it for the new worker
|
|
if hasattr(self, "_channel") and self._channel is not None:
|
|
worker_node.set_channel(self._channel)
|
|
|
|
# If workforce is paused, start the worker's listening task
|
|
self._start_child_node_when_paused(worker_node.start())
|
|
|
|
# Use proper CAMEL pattern for metrics logging
|
|
metrics_callbacks = [
|
|
cb for cb in self._callbacks if isinstance(cb, WorkforceMetrics)
|
|
]
|
|
if metrics_callbacks:
|
|
# Collect agent metadata for telemetry
|
|
agent_class_name = getattr(worker, 'agent_name',
|
|
worker.__class__.__name__)
|
|
model_type = self._extract_model_type(worker)
|
|
|
|
# Log worker created event
|
|
event = WorkerCreatedEvent(
|
|
worker_id=worker_node.node_id,
|
|
worker_type="SingleAgentWorker",
|
|
role=worker_node.description,
|
|
)
|
|
|
|
# Call log_worker_created for all callbacks
|
|
for cb in self._callbacks:
|
|
if isinstance(cb, WorkforceMetricsCallback):
|
|
cb.log_worker_created(
|
|
event,
|
|
agent_class=agent_class_name,
|
|
model_type=model_type,
|
|
)
|
|
else:
|
|
cb.log_worker_created(event)
|
|
|
|
return self
|
|
|
|
async def _handle_completed_task(self, task: Task) -> None:
|
|
# DEBUG ▶ Task completed
|
|
logger.debug(f"[WF] DONE {task.id}")
|
|
task_lock = get_task_lock(self.api_task_id)
|
|
|
|
# Log task completion with result details
|
|
is_main_task = self._task and task.id == self._task.id
|
|
task_type = "MAIN TASK" if is_main_task else "SUB-TASK"
|
|
logger.info(f"[TASK-RESULT] {task_type} COMPLETED: {task.id}")
|
|
logger.info(f"[TASK-RESULT] Content: {task.content[:200]}..." if len(
|
|
task.content) > 200 else f"[TASK-RESULT] Content: {task.content}")
|
|
logger.info(f"[TASK-RESULT] Result: {task.result[:500]}..."
|
|
if task.result and len(str(task.result)) > 500 else
|
|
f"[TASK-RESULT] Result: {task.result}")
|
|
|
|
task_data = {
|
|
"task_id": task.id,
|
|
"content": task.content,
|
|
"state": task.state,
|
|
"result": task.result or "",
|
|
"failure_count": task.failure_count,
|
|
}
|
|
|
|
await task_lock.put_queue(ActionTaskStateData(data=task_data))
|
|
|
|
# Log task completion to metrics
|
|
metrics_callbacks = [
|
|
cb for cb in self._callbacks if isinstance(cb, WorkforceMetrics)
|
|
]
|
|
if metrics_callbacks:
|
|
# worker_id is required and cannot be None
|
|
worker_id = getattr(task, 'assigned_worker_id', None) or 'unknown'
|
|
event = TaskCompletedEvent(
|
|
task_id=task.id,
|
|
worker_id=worker_id,
|
|
)
|
|
metrics_callbacks[0].log_task_completed(event)
|
|
|
|
# IMPORTANT: Sync this subtask's result back to parent.subtasks BEFORE calling super()
|
|
# This fixes the issue where parent.subtasks[i].result is None because CAMEL's
|
|
# _handle_completed_task stores results in _completed_tasks but doesn't sync
|
|
# them back to the parent.subtasks list.
|
|
# We only sync the current task (not all subtasks) for efficiency - O(n) vs O(n²).
|
|
parent = task.parent
|
|
if parent and parent.subtasks:
|
|
for sub in parent.subtasks:
|
|
if sub.id == task.id:
|
|
sub.result = task.result
|
|
sub.state = task.state
|
|
logger.debug(f"[SYNC] Synced subtask {task.id} result to parent.subtasks")
|
|
break
|
|
|
|
# Call parent implementation
|
|
await super()._handle_completed_task(task)
|
|
|
|
async def _handle_failed_task(self, task: Task) -> bool:
|
|
# DEBUG ▶ Task failed
|
|
logger.debug(f"[WF] FAIL {task.id} retry={task.failure_count}")
|
|
|
|
result = await super()._handle_failed_task(task)
|
|
|
|
# Only send completion report to frontend when all
|
|
# retries are exhausted
|
|
max_retries = self.failure_handling_config.max_retries
|
|
if task.failure_count < max_retries:
|
|
return result
|
|
|
|
error_message = ""
|
|
# Use proper CAMEL pattern for metrics logging
|
|
metrics_callbacks = [
|
|
cb for cb in self._callbacks if isinstance(cb, WorkforceMetrics)
|
|
]
|
|
if metrics_callbacks and hasattr(metrics_callbacks[0], "log_entries"):
|
|
for entry in reversed(metrics_callbacks[0].log_entries):
|
|
if entry.get("event_type") == "task_failed" and entry.get(
|
|
"task_id") == task.id:
|
|
error_message = entry.get("error_message")
|
|
break
|
|
|
|
task_lock = get_task_lock(self.api_task_id)
|
|
await task_lock.put_queue(
|
|
ActionTaskStateData(
|
|
data={
|
|
"task_id": task.id,
|
|
"content": task.content,
|
|
"state": task.state,
|
|
"failure_count": task.failure_count,
|
|
"result": str(error_message),
|
|
}))
|
|
|
|
if metrics_callbacks:
|
|
|
|
event = TaskFailedEvent(task_id=task.id, )
|
|
# Add failure details if available
|
|
if hasattr(task, 'assigned_worker_id'):
|
|
event.worker_id = task.assigned_worker_id
|
|
event.error_message = error_message or str(task.result
|
|
or "Unknown error")
|
|
event.failure_count = task.failure_count
|
|
metrics_callbacks[0].log_task_failed(event)
|
|
|
|
return result
|
|
|
|
async def _get_returned_task(self) -> Optional[Task]:
|
|
r"""Override to handle timeout and send notification to frontend.
|
|
|
|
Get the task that's published by this node and just get returned
|
|
from the assignee. Includes timeout handling to prevent indefinite
|
|
waiting.
|
|
|
|
Raises:
|
|
asyncio.TimeoutError: If waiting for task exceeds timeout
|
|
"""
|
|
try:
|
|
return await asyncio.wait_for(
|
|
self._channel.get_returned_task_by_publisher(self.node_id),
|
|
timeout=self.task_timeout_seconds,
|
|
)
|
|
except asyncio.TimeoutError:
|
|
# Send timeout notification to frontend before re-raising
|
|
logger.warning(
|
|
f"⏰ [WF-TIMEOUT] Task timeout in workforce {self.node_id}. "
|
|
f"Timeout: {self.task_timeout_seconds}s, "
|
|
f"Pending tasks: {len(self._pending_tasks)}, "
|
|
f"In-flight tasks: {self._in_flight_tasks}")
|
|
|
|
# Try to notify frontend, but don't let
|
|
# notification failure mask the timeout
|
|
try:
|
|
task_lock = get_task_lock(self.api_task_id)
|
|
timeout_minutes = self.task_timeout_seconds // 60
|
|
await task_lock.put_queue(
|
|
ActionTimeoutData(
|
|
data={
|
|
"message":
|
|
(f"Task execution timeout: No response received "
|
|
f"for {timeout_minutes} minutes"),
|
|
"in_flight_tasks":
|
|
self._in_flight_tasks,
|
|
"pending_tasks":
|
|
len(self._pending_tasks),
|
|
"timeout_seconds":
|
|
self.task_timeout_seconds,
|
|
}))
|
|
except Exception as notify_err:
|
|
logger.error(
|
|
f"Failed to send timeout notification: {notify_err}")
|
|
raise
|
|
except Exception as e:
|
|
logger.error(f"Error getting returned task {e} in "
|
|
f"workforce {self.node_id}. "
|
|
f"Current pending tasks: {len(self._pending_tasks)}, "
|
|
f"In-flight tasks: {self._in_flight_tasks}")
|
|
raise
|
|
|
|
def stop(self) -> None:
|
|
logger.info("=" * 80)
|
|
logger.info("⏹️ [WF-LIFECYCLE] stop() CALLED",
|
|
extra={
|
|
"api_task_id": self.api_task_id,
|
|
"workforce_id": id(self)
|
|
})
|
|
logger.info(f"[WF-LIFECYCLE] Current state before stop: "
|
|
f"{self._state.name}, _running: {self._running}")
|
|
logger.info("=" * 80)
|
|
super().stop()
|
|
logger.info(f"[WF-LIFECYCLE] super().stop() completed, "
|
|
f"new state: {self._state.name}")
|
|
task_lock = get_task_lock(self.api_task_id)
|
|
task = asyncio.create_task(task_lock.put_queue(ActionEndData()))
|
|
task_lock.add_background_task(task)
|
|
logger.info("[WF-LIFECYCLE] ✅ ActionEndData queued")
|
|
|
|
def stop_gracefully(self) -> None:
|
|
logger.info("=" * 80)
|
|
logger.info("🛑 [WF-LIFECYCLE] stop_gracefully() CALLED",
|
|
extra={
|
|
"api_task_id": self.api_task_id,
|
|
"workforce_id": id(self)
|
|
})
|
|
logger.info(f"[WF-LIFECYCLE] Current state before stop_gracefully: "
|
|
f"{self._state.name}, _running: {self._running}")
|
|
logger.info("=" * 80)
|
|
super().stop_gracefully()
|
|
logger.info(
|
|
f"[WF-LIFECYCLE] ✅ super().stop_gracefully() completed, "
|
|
f"new state: {self._state.name}, _running: {self._running}")
|
|
|
|
def skip_gracefully(self) -> None:
|
|
logger.info("=" * 80)
|
|
logger.info("⏭️ [WF-LIFECYCLE] skip_gracefully() CALLED",
|
|
extra={
|
|
"api_task_id": self.api_task_id,
|
|
"workforce_id": id(self)
|
|
})
|
|
logger.info(f"[WF-LIFECYCLE] Current state before skip_gracefully: "
|
|
f"{self._state.name}, _running: {self._running}")
|
|
logger.info("=" * 80)
|
|
super().skip_gracefully()
|
|
logger.info(
|
|
f"[WF-LIFECYCLE] ✅ super().skip_gracefully() completed, "
|
|
f"new state: {self._state.name}, _running: {self._running}")
|
|
|
|
def pause(self) -> None:
|
|
logger.info("=" * 80)
|
|
logger.info("⏸️ [WF-LIFECYCLE] pause() CALLED",
|
|
extra={
|
|
"api_task_id": self.api_task_id,
|
|
"workforce_id": id(self)
|
|
})
|
|
logger.info(f"[WF-LIFECYCLE] Current state before pause: "
|
|
f"{self._state.name}, _running: {self._running}")
|
|
logger.info("=" * 80)
|
|
super().pause()
|
|
logger.info(
|
|
f"[WF-LIFECYCLE] ✅ super().pause() completed, "
|
|
f"new state: {self._state.name}, _running: {self._running}")
|
|
|
|
def resume(self) -> None:
|
|
logger.info("=" * 80)
|
|
logger.info("▶️ [WF-LIFECYCLE] resume() CALLED",
|
|
extra={
|
|
"api_task_id": self.api_task_id,
|
|
"workforce_id": id(self)
|
|
})
|
|
logger.info(f"[WF-LIFECYCLE] Current state before resume: "
|
|
f"{self._state.name}, _running: {self._running}")
|
|
logger.info("=" * 80)
|
|
super().resume()
|
|
logger.info(
|
|
f"[WF-LIFECYCLE] ✅ super().resume() completed, "
|
|
f"new state: {self._state.name}, _running: {self._running}")
|
|
|
|
async def cleanup(self) -> None:
|
|
r"""Clean up resources when workforce is done"""
|
|
try:
|
|
# Clean up the task lock
|
|
from app.service.task import delete_task_lock
|
|
|
|
await delete_task_lock(self.api_task_id)
|
|
except Exception as e:
|
|
logger.error(f"Error cleaning up workforce resources: {e}")
|