# Task Orchestration Documentation

## Overview
The task orchestration system in AgentMCP manages complex workflows involving multiple agents from different frameworks. It handles task dependencies, result forwarding, and ensures proper execution order while maintaining system stability.

## Core Components

### 1. Task Structure

#### Basic Task Format
```python
task = {
    "task_id": str,            # Unique task identifier
    "type": str,               # Task type (e.g., "research", "analysis")
    "description": str,        # Human-readable description
    "agent": str,              # Target agent name
    "url": str,                # Agent endpoint URL
    "depends_on": List[str],   # List of dependent task IDs
    "timeout": int,            # Execution timeout in seconds
    "priority": int            # Task priority (1-10)
}
```

#### Collaborative Task Format
```python
collaborative_task = {
    "task_id": str,
    "type": "collaborative_task",
    "description": str,
    "steps": List[Dict],       # Ordered list of subtasks
    "metadata": Dict           # Additional task information
}
```

### 2. Task Coordinator

#### Purpose
Manages task distribution, dependency resolution, and result aggregation.

#### Implementation
```python
class TaskCoordinator:
    def __init__(self):
        self.pending_tasks = {}
        self.completed_tasks = {}
        self.task_results = {}
        self.dependencies = {}
        
    async def submit_task(self, task: Dict[str, Any]):
        """
        Submit a new task for execution.
        
        Args:
            task: Task specification
        """
        
    async def process_result(self, result: Dict[str, Any]):
        """
        Process task result and trigger dependent tasks.
        
        Args:
            result: Task execution result
        """
```

### 3. Dependency Management

#### Dependency Graph
```python
class DependencyGraph:
    def __init__(self):
        self.graph = {}
        self.ready_tasks = set()
        
    def add_task(self, task_id: str, depends_on: List[str]):
        """Add task to dependency graph"""
        
    def mark_completed(self, task_id: str):
        """Mark task as completed and update ready tasks"""
        
    def get_ready_tasks(self) -> Set[str]:
        """Get tasks ready for execution"""
```

## Task Processing Flow

### 1. Task Submission
```python
async def submit_task(task: Dict[str, Any]):
    """
    1. Validate task format
    2. Register dependencies
    3. Add to pending tasks
    4. Start execution if no dependencies
    """
```

### 2. Dependency Resolution
```python
def check_dependencies(task_id: str) -> bool:
    """
    Check if all dependencies are satisfied.
    
    Returns:
        True if task can execute, False otherwise
    """
```

### 3. Task Execution
```python
async def execute_task(task: Dict[str, Any]):
    """
    1. Send task to agent
    2. Monitor execution
    3. Handle timeout
    4. Process result
    """
```

### 4. Result Processing
```python
async def process_result(result: Dict[str, Any]):
    """
    1. Store result
    2. Update task status
    3. Check dependent tasks
    4. Forward result if needed
    """
```

## Advanced Features

### 1. Task Prioritization
```python
class TaskPrioritizer:
    def prioritize(self, tasks: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """
        Prioritize tasks based on:
        - Explicit priority
        - Dependencies
        - Resource availability
        - Deadline
        """
```

### 2. Error Recovery
```python
async def handle_task_failure(task_id: str, error: Exception):
    """
    1. Log failure
    2. Attempt recovery
    3. Update dependent tasks
    4. Notify coordinator
    """
```

### 3. Progress Monitoring
```python
class TaskMonitor:
    def get_status(self, task_id: str) -> Dict[str, Any]:
        """Get detailed task status"""
        
    def get_progress(self) -> float:
        """Get overall progress percentage"""
```

## Integration Examples

### 1. Simple Task Chain
```python
# Define sequential tasks
tasks = [
    {
        "task_id": "research",
        "agent": "LangchainWorker",
        "description": "Research topic"
    },
    {
        "task_id": "analysis",
        "agent": "CrewAIWorker",
        "description": "Analyze findings",
        "depends_on": ["research"]
    }
]

# Submit tasks
await coordinator.submit_tasks(tasks)
```

### 2. Complex Workflow
```python
# Define parallel tasks with dependencies
workflow = {
    "task_id": "project",
    "type": "collaborative_task",
    "steps": [
        {
            "task_id": "data_collection",
            "agent": "LangchainWorker",
            "parallel": True
        },
        {
            "task_id": "preprocessing",
            "agent": "AutogenWorker",
            "depends_on": ["data_collection"]
        },
        {
            "task_id": "analysis",
            "agent": "CrewAIWorker",
            "depends_on": ["preprocessing"]
        }
    ]
}
```

## Best Practices

### 1. Task Design
```python
# Good task design
task = {
    "task_id": "unique_id",
    "description": "Clear, specific instruction",
    "timeout": 300,
    "retry_policy": {
        "max_attempts": 3,
        "backoff": "exponential"
    }
}
```

### 2. Dependency Management
```python
# Avoid circular dependencies
def validate_dependencies(tasks: List[Dict[str, Any]]):
    """
    Check for:
    1. Circular dependencies
    2. Missing dependencies
    3. Invalid task references
    """
```

### 3. Error Handling
```python
try:
    result = await coordinator.execute_task(task)
except TaskError as e:
    # Handle task-specific error
    await coordinator.handle_task_failure(task["task_id"], e)
except DependencyError as e:
    # Handle dependency resolution error
    await coordinator.resolve_dependency_conflict(e)
```

## Performance Optimization

### 1. Task Batching
```python
async def batch_tasks(tasks: List[Dict[str, Any]]):
    """
    Optimize task execution by:
    1. Grouping similar tasks
    2. Parallel execution
    3. Resource allocation
    """
```

### 2. Resource Management
```python
class ResourceManager:
    def allocate_resources(self, task: Dict[str, Any]):
        """
        Manage:
        1. Memory usage
        2. CPU allocation
        3. Network bandwidth
        """
```

### 3. Caching
```python
class ResultCache:
    def cache_result(self, task_id: str, result: Any):
        """Cache task result"""
        
    def get_cached_result(self, task_id: str) -> Optional[Any]:
        """Retrieve cached result"""
```

## Monitoring and Debugging

### 1. Logging
```python
class TaskLogger:
    def log_event(self, task_id: str, event: str):
        """Log task events"""
        
    def get_task_history(self, task_id: str) -> List[Dict]:
        """Get task execution history"""
```

### 2. Metrics
```python
class TaskMetrics:
    def track_metrics(self, task: Dict[str, Any]):
        """
        Track:
        1. Execution time
        2. Success rate
        3. Resource usage
        4. Dependency chains
        """
```

### 3. Visualization
```python
class TaskVisualizer:
    def visualize_workflow(self, tasks: List[Dict[str, Any]]):
        """Generate workflow visualization"""
        
    def show_dependencies(self, task_id: str):
        """Show task dependencies"""
```
