Refactor UnifiedCoordinator to follow Single Responsibility Principle
- Create dedicated service classes for separated concerns: * AgentService: Agent management and health monitoring * WorkflowService: Workflow parsing and execution tracking * PerformanceService: Metrics and load balancing * BackgroundService: Background processes and cleanup * TaskService: Database persistence (already existed) - Refactor UnifiedCoordinator into UnifiedCoordinatorRefactored * Clean separation of responsibilities * Improved maintainability and testability * Dependency injection pattern for services * Clear service boundaries and interfaces - Maintain backward compatibility through re-exports - Update main.py to use refactored coordinator 🚀 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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anthonyrawlins
263
backend/app/services/workflow_service.py
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263
backend/app/services/workflow_service.py
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"""
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Workflow Management Service
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Handles workflow parsing, scheduling, dependency tracking, and execution management.
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"""
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import time
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import logging
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from typing import Dict, List, Optional, Any
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from dataclasses import dataclass, field
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from enum import Enum
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# Import shared types
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from .agent_service import AgentType
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logger = logging.getLogger(__name__)
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class TaskStatus(Enum):
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"""Task status tracking"""
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PENDING = "pending"
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IN_PROGRESS = "in_progress"
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COMPLETED = "completed"
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FAILED = "failed"
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@dataclass
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class Task:
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"""Unified task representation"""
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id: str
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type: AgentType
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priority: int = 3
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status: TaskStatus = TaskStatus.PENDING
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context: Dict[str, Any] = field(default_factory=dict)
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payload: Dict[str, Any] = field(default_factory=dict)
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assigned_agent: Optional[str] = None
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result: Optional[Dict] = None
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created_at: float = field(default_factory=time.time)
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completed_at: Optional[float] = None
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# Workflow support
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workflow_id: Optional[str] = None
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dependencies: List[str] = field(default_factory=list)
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def cache_key(self) -> str:
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"""Generate cache key for task result"""
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import hashlib
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import json
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payload_hash = hashlib.md5(json.dumps(self.payload, sort_keys=True).encode()).hexdigest()
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return f"task_result:{self.type.value}:{payload_hash}"
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@dataclass
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class WorkflowExecution:
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"""Represents a workflow execution instance"""
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workflow_id: str
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execution_id: str
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tasks: List[Task]
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created_at: float
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completed_at: Optional[float] = None
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status: str = "running"
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metadata: Dict[str, Any] = None
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def __post_init__(self):
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if self.metadata is None:
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self.metadata = {}
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class WorkflowService:
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"""Service for managing workflows and their execution"""
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def __init__(self):
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self.workflow_tasks: Dict[str, List[Task]] = {}
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self.workflow_executions: Dict[str, WorkflowExecution] = {}
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self._initialized = False
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def initialize(self):
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"""Initialize the workflow service"""
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if self._initialized:
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return
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self._initialized = True
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logger.info("✅ Workflow Service initialized successfully")
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async def submit_workflow(self, workflow: Dict[str, Any]) -> str:
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"""Submit a workflow for execution"""
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workflow_id = f"workflow_{int(time.time())}"
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execution_id = f"exec_{workflow_id}"
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tasks = self._parse_workflow_to_tasks(workflow, workflow_id)
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# Create workflow execution record
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execution = WorkflowExecution(
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workflow_id=workflow_id,
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execution_id=execution_id,
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tasks=tasks,
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created_at=time.time(),
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metadata=workflow.get('metadata', {})
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)
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self.workflow_tasks[workflow_id] = tasks
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self.workflow_executions[execution_id] = execution
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logger.info(f"🔄 Submitted workflow: {workflow_id} with {len(tasks)} tasks")
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return workflow_id
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def _parse_workflow_to_tasks(self, workflow: Dict[str, Any], workflow_id: str) -> List[Task]:
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"""Parse workflow definition into tasks"""
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tasks = []
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base_tasks = workflow.get('tasks', [])
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for i, task_def in enumerate(base_tasks):
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task_id = f"{workflow_id}_task_{i}"
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task_type = AgentType(task_def.get('type', 'general_ai'))
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task = Task(
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id=task_id,
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type=task_type,
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workflow_id=workflow_id,
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context=task_def.get('context', {}),
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payload=task_def.get('payload', {}),
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dependencies=task_def.get('dependencies', []),
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priority=task_def.get('priority', 3)
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)
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tasks.append(task)
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return tasks
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def get_ready_workflow_tasks(self, all_tasks: Dict[str, Task]) -> List[Task]:
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"""Get workflow tasks that are ready to execute (dependencies satisfied)"""
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ready_tasks = []
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for workflow_id, workflow_tasks in self.workflow_tasks.items():
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for task in workflow_tasks:
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if (task.status == TaskStatus.PENDING and
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self._dependencies_satisfied(task, all_tasks)):
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ready_tasks.append(task)
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return ready_tasks
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def _dependencies_satisfied(self, task: Task, all_tasks: Dict[str, Task]) -> bool:
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"""Check if task dependencies are satisfied"""
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for dep_id in task.dependencies:
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dep_task = all_tasks.get(dep_id)
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if not dep_task or dep_task.status != TaskStatus.COMPLETED:
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return False
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return True
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def handle_task_completion(self, task: Task):
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"""Handle completion of a workflow task"""
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if not task.workflow_id:
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return
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# Check if workflow is complete
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workflow_tasks = self.workflow_tasks.get(task.workflow_id, [])
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completed_tasks = [t for t in workflow_tasks if t.status == TaskStatus.COMPLETED]
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failed_tasks = [t for t in workflow_tasks if t.status == TaskStatus.FAILED]
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# Update workflow execution status
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for execution in self.workflow_executions.values():
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if execution.workflow_id == task.workflow_id:
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if len(failed_tasks) > 0:
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execution.status = "failed"
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execution.completed_at = time.time()
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logger.info(f"❌ Workflow {task.workflow_id} failed")
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elif len(completed_tasks) == len(workflow_tasks):
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execution.status = "completed"
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execution.completed_at = time.time()
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logger.info(f"🎉 Workflow {task.workflow_id} completed")
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break
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def get_workflow_status(self, workflow_id: str) -> Dict[str, Any]:
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"""Get workflow execution status"""
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workflow_tasks = self.workflow_tasks.get(workflow_id, [])
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if not workflow_tasks:
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return {"error": "Workflow not found"}
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status_counts = {}
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for status in TaskStatus:
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status_counts[status.value] = len([t for t in workflow_tasks if t.status == status])
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# Find execution record
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execution = None
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for exec_record in self.workflow_executions.values():
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if exec_record.workflow_id == workflow_id:
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execution = exec_record
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break
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return {
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"workflow_id": workflow_id,
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"execution_id": execution.execution_id if execution else None,
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"total_tasks": len(workflow_tasks),
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"status_breakdown": status_counts,
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"completed": status_counts.get("completed", 0) == len(workflow_tasks),
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"status": execution.status if execution else "unknown",
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"created_at": execution.created_at if execution else None,
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"completed_at": execution.completed_at if execution else None
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}
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def get_workflow_tasks(self, workflow_id: str) -> List[Task]:
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"""Get all tasks for a workflow"""
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return self.workflow_tasks.get(workflow_id, [])
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def get_all_workflows(self) -> Dict[str, List[Task]]:
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"""Get all workflows"""
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return self.workflow_tasks.copy()
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def get_workflow_executions(self, workflow_id: Optional[str] = None) -> List[Dict[str, Any]]:
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"""Get workflow execution history"""
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executions = []
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for execution in self.workflow_executions.values():
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if workflow_id is None or execution.workflow_id == workflow_id:
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executions.append({
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"workflow_id": execution.workflow_id,
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"execution_id": execution.execution_id,
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"status": execution.status,
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"task_count": len(execution.tasks),
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"created_at": execution.created_at,
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"completed_at": execution.completed_at,
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"metadata": execution.metadata
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})
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# Sort by creation time, newest first
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executions.sort(key=lambda x: x["created_at"], reverse=True)
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return executions
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def cleanup_completed_workflows(self, max_age_hours: int = 24):
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"""Clean up old completed workflow executions"""
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cutoff_time = time.time() - (max_age_hours * 3600)
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# Find completed executions older than cutoff
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to_remove = []
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for execution_id, execution in self.workflow_executions.items():
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if (execution.status in ["completed", "failed"] and
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execution.completed_at and
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execution.completed_at < cutoff_time):
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to_remove.append(execution_id)
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# Remove old executions and their associated workflow tasks
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removed_count = 0
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for execution_id in to_remove:
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execution = self.workflow_executions[execution_id]
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workflow_id = execution.workflow_id
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# Remove workflow tasks if this is the only execution for this workflow
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other_executions = [
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e for e in self.workflow_executions.values()
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if e.workflow_id == workflow_id and e.execution_id != execution_id
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]
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if not other_executions:
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self.workflow_tasks.pop(workflow_id, None)
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# Remove execution record
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del self.workflow_executions[execution_id]
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removed_count += 1
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if removed_count > 0:
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logger.info(f"🧹 Cleaned up {removed_count} old workflow executions")
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return removed_count
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