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578 lines
25 KiB
Python
578 lines
25 KiB
Python
import asyncio
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from typing import Generator, List, Optional
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from camel.agents import ChatAgent
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from camel.societies.workforce.workforce import (
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Workforce as BaseWorkforce,
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WorkforceState,
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DEFAULT_WORKER_POOL_SIZE,
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)
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from camel.societies.workforce.utils import FailureHandlingConfig
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from camel.societies.workforce.task_channel import TaskChannel
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from camel.societies.workforce.base import BaseNode
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from camel.societies.workforce.utils import TaskAssignResult
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from camel.societies.workforce.workforce_metrics import WorkforceMetrics
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from camel.societies.workforce.events import WorkerCreatedEvent
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from camel.societies.workforce.prompts import TASK_DECOMPOSE_PROMPT
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from camel.tasks.task import Task, TaskState, validate_task_content
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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.utils.agent import ListenChatAgent
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from app.service.task import (
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Action,
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ActionAssignTaskData,
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ActionEndData,
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ActionTaskStateData,
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ActionTimeoutData,
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get_camel_task,
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get_task_lock,
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)
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from app.utils.single_agent_worker import SingleAgentWorker
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from utils import traceroot_wrapper as traceroot
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logger = traceroot.get_logger("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", 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(f"[WF-LIFECYCLE] Init params: graceful_shutdown_timeout={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=1800, # 30 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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)
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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(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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"""
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Split process_task method to eigent_make_sub_tasks 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 agent during decomposition.
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This context will NOT be passed to subtasks or worker agents.
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on_stream_batch: Optional callback for streaming batches signature (List[Task], bool)
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on_stream_text: Optional callback for raw streaming text chunks
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"""
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logger.debug("[DECOMPOSE] eigent_make_sub_tasks called", 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("[DECOMPOSE] Task rejected: Invalid or empty content", extra={
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"task_id": task.id,
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"content_preview": task.content[:50] + "..." 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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)
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)
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logger.info(f"[DECOMPOSE] Task decomposition completed", 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 {len(subtasks)} subtasks", extra={
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"api_task_id": self.api_task_id
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})
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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}", extra={
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"api_task_id": self.api_task_id,
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"error": str(e)
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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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)
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self.task_agent.reset()
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result = task.decompose(
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self.task_agent, decompose_prompt, stream_callback=stream_callback
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)
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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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)
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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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"""
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Override to support coordinator_context parameter.
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Handle task decomposition and validation, 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 (Workforce must not be running)
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coordinator_context: Optional context ONLY for coordinator during decomposition
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on_stream_batch: Optional callback for streaming 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, 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(
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f"[DECOMPOSE] Task {task.id} rejected: Invalid or empty content. "
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f"Content preview: '{task.content}'"
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)
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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 + "\n=== CURRENT TASK ===\n" + original_content
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task.content = task_with_context
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subtasks_result = self._decompose_task(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(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 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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if not subtasks:
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logger.warning(f"[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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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, 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 frontend communication.
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The CAMEL base class uses node_id for task assignment, but the frontend
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uses agent_id to identify agents. 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(child.worker, 'agent_id'):
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return child.worker.agent_id
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return 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 and its dependencies
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logger.debug(f"[WF] ASSIGN {item.task_id} -> {item.assignee_id} 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 tasks list during ASSIGN phase. This may indicate a task tree inconsistency."
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)
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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 an already assigned task
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# This prevents the frontend from showing "Reassigned" when a task is being retried
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# with the same or different 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={task_obj.assigned_worker_id}, "
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f"new assignee={item.assignee_id} (retry/replan scenario)"
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)
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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, but the frontend
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# 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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logger.error(
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f"[WF] ERROR: Could not find agent_id for node_id={item.assignee_id}. "
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f"Task {item.task_id} will not be properly tracked on frontend. "
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f"Available workers: {[c.node_id for c in self._children if hasattr(c, 'node_id')]}"
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)
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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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)
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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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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 when the task really starts to execute"""
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# When the dependency check is passed and the task is about to be published to the execution queue, send a 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: # 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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if agent_id is None:
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logger.error(
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f"[WF] ERROR: Could not find agent_id for node_id={assignee_id}. "
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f"Task {task.id} will not be properly tracked on frontend. "
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f"Available workers: {[c.node_id for c in self._children if hasattr(c, 'node_id')]}"
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)
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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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)
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# Call the parent class method to continue the normal task publishing process
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await super()._post_task(task, assignee_id)
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def add_single_agent_worker(
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self,
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description: str,
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worker: ListenChatAgent,
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pool_max_size: int = DEFAULT_WORKER_POOL_SIZE,
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enable_workflow_memory: bool = False,
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) -> BaseWorkforce:
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if self._state == WorkforceState.RUNNING:
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raise RuntimeError("Cannot add workers while workforce is running. Pause the workforce first.")
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# Validate worker agent compatibility
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self._validate_agent_compatibility(worker, "Worker agent")
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# Ensure the worker agent shares this workforce's pause control
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self._attach_pause_event_to_agent(worker)
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worker_node = SingleAgentWorker(
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description=description,
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worker=worker,
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pool_max_size=pool_max_size,
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use_structured_output_handler=self.use_structured_output_handler,
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context_utility=None,
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enable_workflow_memory=enable_workflow_memory,
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)
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self._children.append(worker_node)
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# If we have a channel set up, set it for the new worker
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if hasattr(self, "_channel") and self._channel is not None:
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worker_node.set_channel(self._channel)
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# If workforce is paused, start the worker's listening task
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self._start_child_node_when_paused(worker_node.start())
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# Use proper CAMEL pattern for metrics logging
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metrics_callbacks = [cb for cb in self._callbacks if isinstance(cb, WorkforceMetrics)]
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if metrics_callbacks:
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event = WorkerCreatedEvent(
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worker_id=worker_node.node_id,
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worker_type="SingleAgentWorker",
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role=worker_node.description,
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)
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metrics_callbacks[0].log_worker_created(event)
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return self
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async def _handle_completed_task(self, task: Task) -> None:
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# DEBUG ▶ Task completed
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logger.debug(f"[WF] DONE {task.id}")
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task_lock = get_task_lock(self.api_task_id)
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# Log task completion with result details
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is_main_task = self._task and task.id == self._task.id
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task_type = "MAIN TASK" if is_main_task else "SUB-TASK"
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logger.info(f"[TASK-RESULT] {task_type} COMPLETED: {task.id}")
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logger.info(f"[TASK-RESULT] Content: {task.content[:200]}..." if len(task.content) > 200 else f"[TASK-RESULT] Content: {task.content}")
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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}")
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task_data = {
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"task_id": task.id,
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"content": task.content,
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"state": task.state,
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"result": task.result or "",
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"failure_count": task.failure_count,
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}
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await task_lock.put_queue(
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ActionTaskStateData(
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data=task_data
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)
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)
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return await super()._handle_completed_task(task)
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async def _handle_failed_task(self, task: Task) -> bool:
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# DEBUG ▶ Task failed
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logger.debug(f"[WF] FAIL {task.id} retry={task.failure_count}")
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result = await super()._handle_failed_task(task)
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error_message = ""
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# Use proper CAMEL pattern for metrics logging
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metrics_callbacks = [cb for cb in self._callbacks if isinstance(cb, WorkforceMetrics)]
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if metrics_callbacks and hasattr(metrics_callbacks[0], "log_entries"):
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for entry in reversed(metrics_callbacks[0].log_entries):
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if entry.get("event_type") == "task_failed" and entry.get("task_id") == task.id:
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error_message = entry.get("error_message")
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break
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task_lock = get_task_lock(self.api_task_id)
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await task_lock.put_queue(
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ActionTaskStateData(
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data={
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"task_id": task.id,
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"content": task.content,
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"state": task.state,
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"failure_count": task.failure_count,
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"result": str(error_message),
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}
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)
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)
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return result
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async def _get_returned_task(self) -> Optional[Task]:
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r"""Override to handle timeout and send notification to frontend.
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Get the task that's published by this node and just get returned
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from the assignee. Includes timeout handling to prevent indefinite
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waiting.
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Raises:
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asyncio.TimeoutError: If waiting for task exceeds timeout
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"""
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try:
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return await asyncio.wait_for(
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self._channel.get_returned_task_by_publisher(self.node_id),
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timeout=self.task_timeout_seconds,
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)
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except asyncio.TimeoutError:
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# Send timeout notification to frontend before re-raising
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logger.warning(
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f"⏰ [WF-TIMEOUT] Task timeout in workforce {self.node_id}. "
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f"Timeout: {self.task_timeout_seconds}s, "
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f"Pending tasks: {len(self._pending_tasks)}, "
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f"In-flight tasks: {self._in_flight_tasks}"
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)
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# Try to notify frontend, but don't let notification failure mask the timeout
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try:
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task_lock = get_task_lock(self.api_task_id)
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timeout_minutes = self.task_timeout_seconds // 60
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await task_lock.put_queue(
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ActionTimeoutData(
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data={
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"message": f"Task execution timeout: No response received for {timeout_minutes} minutes",
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"in_flight_tasks": self._in_flight_tasks,
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"pending_tasks": len(self._pending_tasks),
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"timeout_seconds": self.task_timeout_seconds,
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}
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)
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)
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except Exception as notify_err:
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logger.error(f"Failed to send timeout notification: {notify_err}")
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raise
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except Exception as e:
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logger.error(
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f"Error getting returned task {e} in workforce {self.node_id}. "
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f"Current pending tasks: {len(self._pending_tasks)}, "
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f"In-flight tasks: {self._in_flight_tasks}"
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)
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raise
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def stop(self) -> None:
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logger.info("=" * 80)
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logger.info(f"⏹️ [WF-LIFECYCLE] stop() CALLED", extra={"api_task_id": self.api_task_id, "workforce_id": id(self)})
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logger.info(f"[WF-LIFECYCLE] Current state before stop: {self._state.name}, _running: {self._running}")
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logger.info("=" * 80)
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super().stop()
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logger.info(f"[WF-LIFECYCLE] super().stop() completed, new state: {self._state.name}")
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task_lock = get_task_lock(self.api_task_id)
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task = asyncio.create_task(task_lock.put_queue(ActionEndData()))
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task_lock.add_background_task(task)
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logger.info(f"[WF-LIFECYCLE] ✅ ActionEndData queued")
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def stop_gracefully(self) -> None:
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logger.info("=" * 80)
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logger.info(f"🛑 [WF-LIFECYCLE] stop_gracefully() CALLED", extra={"api_task_id": self.api_task_id, "workforce_id": id(self)})
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logger.info(f"[WF-LIFECYCLE] Current state before stop_gracefully: {self._state.name}, _running: {self._running}")
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logger.info("=" * 80)
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super().stop_gracefully()
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logger.info(f"[WF-LIFECYCLE] ✅ super().stop_gracefully() completed, new state: {self._state.name}, _running: {self._running}")
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def skip_gracefully(self) -> None:
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logger.info("=" * 80)
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logger.info(f"⏭️ [WF-LIFECYCLE] skip_gracefully() CALLED", extra={"api_task_id": self.api_task_id, "workforce_id": id(self)})
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logger.info(f"[WF-LIFECYCLE] Current state before skip_gracefully: {self._state.name}, _running: {self._running}")
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logger.info("=" * 80)
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super().skip_gracefully()
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logger.info(f"[WF-LIFECYCLE] ✅ super().skip_gracefully() completed, new state: {self._state.name}, _running: {self._running}")
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def pause(self) -> None:
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logger.info("=" * 80)
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logger.info(f"⏸️ [WF-LIFECYCLE] pause() CALLED", extra={"api_task_id": self.api_task_id, "workforce_id": id(self)})
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logger.info(f"[WF-LIFECYCLE] Current state before pause: {self._state.name}, _running: {self._running}")
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logger.info("=" * 80)
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super().pause()
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logger.info(f"[WF-LIFECYCLE] ✅ super().pause() completed, new state: {self._state.name}, _running: {self._running}")
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def resume(self) -> None:
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logger.info("=" * 80)
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logger.info(f"▶️ [WF-LIFECYCLE] resume() CALLED", extra={"api_task_id": self.api_task_id, "workforce_id": id(self)})
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logger.info(f"[WF-LIFECYCLE] Current state before resume: {self._state.name}, _running: {self._running}")
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logger.info("=" * 80)
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super().resume()
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logger.info(f"[WF-LIFECYCLE] ✅ super().resume() completed, new state: {self._state.name}, _running: {self._running}")
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async def cleanup(self) -> None:
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r"""Clean up resources when workforce is done"""
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try:
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# Clean up the task lock
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from app.service.task import delete_task_lock
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await delete_task_lock(self.api_task_id)
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except Exception as e:
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logger.error(f"Error cleaning up workforce resources: {e}")
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