# Project Orchestration - API Specification

## Introduction

This API specification documents the programmatic interfaces for the Project Orchestration module of Codomyrmex. The module provides comprehensive project management, workflow orchestration, and task coordination capabilities through a set of Python classes and functions.

## Core Classes and Interfaces

### OrchestrationEngine

The main coordination engine that manages all orchestration operations.

#### Methods

##### `create_session(user_id: str = "system", **kwargs) -> str`

- **Description**: Creates a new orchestration session for context management.
- **Parameters**:
  - `user_id` (string): User identifier for the session
  - `mode` (string, optional): Execution mode ("sequential", "parallel", "resource_aware")
  - `max_parallel_tasks` (int, optional): Maximum concurrent tasks (default: 4)
  - `max_parallel_workflows` (int, optional): Maximum concurrent workflows (default: 2)
  - `timeout_seconds` (int, optional): Session timeout in seconds
  - `resource_requirements` (dict, optional): Required resources for the session
  - `metadata` (dict, optional): Additional session metadata
- **Returns**: Session ID (string)
- **Example**:
  ```python
  engine = OrchestrationEngine()
  session_id = engine.create_session(
      user_id="analyst",
      mode="resource_aware",
      max_parallel_tasks=8,
      resource_requirements={"cpu": {"cores": 4}, "memory": {"gb": 8}}
  )
  ```

##### `execute_workflow(workflow_name: str, session_id: Optional[str] = None, **params) -> Dict[str, Any]`

- **Description**: Executes a workflow with orchestration management.
- **Parameters**:
  - `workflow_name` (string): Name of the workflow to execute
  - `session_id` (Optional[str]): Session ID for context (creates new if not provided)
  - `**params`: Workflow-specific parameters passed to workflow steps
- **Returns**: Dictionary with execution results:
  ```python
  {
    "success": bool,
    "result": Any,  # WorkflowExecution result data
    "error": Optional[str],  # Error message if success is False
    "execution_time": Optional[float],  # Execution time in seconds
    "steps_executed": int  # Number of steps executed
  }
  ```
- **Raises**:
  - `ValueError`: If session not found or workflow execution fails
- **Example**:
  ```python
  result = engine.execute_workflow(
      "ai-analysis",
      code_path="./src",
      output_path="./analysis",
      include_visualization=True
  )
  if result['success']:
      print(f"Workflow completed: {result['steps_executed']} steps")
  else:
      print(f"Workflow failed: {result['error']}")
  ```

##### `execute_task(task: Union[Task, Dict[str, Any]], session_id: Optional[str] = None) -> Dict[str, Any]`

- **Description**: Execute a single task with orchestration.
- **Parameters**:
  - `task` (Union[Task, Dict[str, Any]]): Task object or task dictionary
  - `session_id` (Optional[str]): Session ID (creates new if not provided)
- **Returns**: Dictionary with execution results:
  ```python
  {
    "success": bool,
    "result": Optional[TaskResult],  # Task result as dictionary
    "task_id": str  # Task ID
  }
  ```

##### `execute_project_workflow(project_name: str, workflow_name: str, session_id: Optional[str] = None, **params) -> Dict[str, Any]`

- **Description**: Execute a workflow for a specific project.
- **Parameters**:
  - `project_name` (string): Project name
  - `workflow_name` (string): Workflow to execute
  - `session_id` (Optional[str]): Session ID (creates new if not provided)
  - `**params`: Workflow parameters
- **Returns**: Execution result dictionary

##### `execute_complex_workflow(workflow_definition: Dict[str, Any], session_id: Optional[str] = None) -> Dict[str, Any]`

- **Description**: Execute a complex workflow with multiple interdependent steps.
- **Parameters**:
  - `workflow_definition` (Dict[str, Any]): Workflow definition with steps, dependencies, and parallel_groups
  - `session_id` (Optional[str]): Session ID (creates new if not provided)
- **Returns**: Dictionary with execution results:
  ```python
  {
    "success": bool,
    "results": Dict[str, Any],  # Results for each step
    "execution_stats": Dict[str, Any],  # Task orchestrator execution statistics
    "error": Optional[str]  # Error message if failed
  }
  ```

##### `create_session(user_id: str = "system", **kwargs) -> str`

- **Description**: Creates a new orchestration session for context management.
- **Parameters**:
  - `user_id` (string): User identifier for the session. Default: "system"
  - `mode` (string, optional): Execution mode. One of: "sequential", "parallel", "priority", "resource_aware". Default: "resource_aware"
  - `max_parallel_tasks` (int, optional): Maximum concurrent tasks. Default: 4
  - `max_parallel_workflows` (int, optional): Maximum concurrent workflows. Default: 2
  - `timeout_seconds` (int, optional): Session timeout in seconds
  - `resource_requirements` (dict, optional): Required resources for the session
  - `metadata` (dict, optional): Additional session metadata
- **Returns**: Session ID (string)
- **Example**:
  ```python
  engine = OrchestrationEngine()
  session_id = engine.create_session(
      user_id="analyst",
      mode="resource_aware",
      max_parallel_tasks=8,
      resource_requirements={"cpu": {"cores": 4}, "memory": {"gb": 8}}
  )
  ```

##### `get_session(session_id: str) -> Optional[OrchestrationSession]`

- **Description**: Get a session by ID.
- **Parameters**:
  - `session_id` (string): Session ID
- **Returns**: OrchestrationSession object or None if not found

##### `close_session(session_id: str) -> bool`

- **Description**: Close an orchestration session and cleanup resources.
- **Parameters**:
  - `session_id` (string): Session ID to close
- **Returns**: bool - True if session was closed, False if not found

##### `register_event_handler(event: str, handler: Callable)`

- **Description**: Register an event handler for orchestration events.
- **Parameters**:
  - `event` (string): Event name (e.g., "workflow_completed", "session_closed")
  - `handler` (Callable): Handler function that accepts (event: str, data: dict) arguments

##### `get_metrics() -> Dict[str, Any]`

- **Description**: Get comprehensive metrics for all components.
- **Returns**: Dictionary with metrics including sessions, workflows, tasks, projects, and resources

##### `get_system_status() -> Dict[str, Any]`

- **Description**: Retrieves comprehensive system status and metrics.
- **Returns**: Dictionary with system status information
  ```python
  {
    "timestamp": str,
    "orchestration_engine": {
      "active_sessions": int,
      "event_handlers": dict
    },
    "workflow_manager": {
      "total_workflows": int,
      "running_workflows": int
    },
    "task_orchestrator": {
      "total_tasks": int,
      "pending": int,
      "running": int,
      "completed": int,
      "failed": int
    },
    "project_manager": dict,
    "resource_manager": dict,
    "performance": dict  # If available
  }
  ```

##### `health_check() -> Dict[str, Any]`

- **Description**: Performs comprehensive health check of all components.
- **Returns**: Dictionary with health status
  ```python
  {
    "overall_status": str,  # "healthy", "degraded", "unhealthy"
    "timestamp": str,
    "components": {
      "workflow_manager": {"status": str, "details": dict},
      "task_orchestrator": {"status": str, "details": dict},
      "project_manager": {"status": str, "details": dict},
      "resource_manager": {"status": str, "details": dict}
    },
    "issues": list
  }
  ```

---

### WorkflowManager

Manages workflow definitions and execution.

#### Methods

##### `create_workflow(name: str, steps: List[WorkflowStep], save: bool = True) -> bool`

- **Description**: Creates a new workflow with the specified steps.
- **Parameters**:
  - `name` (string): Unique workflow name. Must be a valid identifier.
  - `steps` (List[WorkflowStep]): List of workflow steps to execute in order. Dependencies between steps are resolved automatically.
  - `save` (bool, optional): Whether to persist the workflow to disk. Defaults to True.
- **Returns**: bool - True if workflow was created successfully, False otherwise.
- **Raises**:
  - `ValueError`: If workflow name is empty or invalid, or if workflow has no steps.
  - `OSError`: If workflow cannot be saved to disk (when save=True).
- **Example**:
  ```python
  from codomyrmex.logistics.orchestration.project import WorkflowManager, WorkflowStep
  
  manager = WorkflowManager()
  steps = [
      WorkflowStep(
          name="setup",
          module="environment_setup",
          action="check_environment"
      ),
      WorkflowStep(
          name="analyze",
          module="static_analysis",
          action="analyze_code_quality",
          parameters={"path": "."},
          dependencies=["setup"]
      )
  ]
  success = manager.create_workflow("custom-analysis", steps)
  ```

##### `async execute_workflow(name: str, parameters: Optional[Dict[str, Any]] = None, timeout: Optional[int] = None) -> WorkflowExecution`

- **Description**: Executes a workflow asynchronously with performance monitoring.
- **Parameters**:
  - `name` (string): Name of the workflow to execute.
  - `parameters` (Optional[Dict[str, Any]]): Global parameters to pass to workflow steps. These can be referenced in step parameters using {{parameter_name}} syntax.
  - `timeout` (Optional[int]): Maximum execution time in seconds. If None, no timeout.
- **Returns**: WorkflowExecution object containing status, results, errors, and metrics.
- **Raises**:
  - `ValueError`: If workflow name is not found.
  - `asyncio.TimeoutError`: If workflow execution exceeds the specified timeout.
- **Example**:
  ```python
  import asyncio
  from codomyrmex.logistics.orchestration.project import get_workflow_manager
  
  async def main():
      manager = get_workflow_manager()
      execution = await manager.execute_workflow(
          "ai-analysis",
          parameters={"project_path": "/path/to/project", "output_format": "json"}
      )
      
      if execution.status == WorkflowStatus.COMPLETED:
          print("Workflow completed successfully")
          for step_name, result in execution.results.items():
              print(f"Step {step_name}: {result}")
      else:
          print(f"Workflow failed: {execution.errors}")
  
  asyncio.run(main())
  ```

##### `list_workflows() -> Dict[str, Dict[str, Any]]`

- **Description**: List all available workflows with comprehensive metadata.
- **Returns**: Dictionary mapping workflow names to their metadata. Each workflow metadata includes:
  - `steps` (int): Number of steps in the workflow
  - `modules` (List[str]): Unique list of modules used by the workflow
  - `estimated_duration` (int): Estimated total execution time in seconds
  - `has_dependencies` (bool): Whether the workflow has step dependencies
  - `created_time` (Optional[str]): When the workflow was created (if available)
- **Example**:
  ```python
  workflows = manager.list_workflows()
  for name, info in workflows.items():
      print(f"Workflow: {name}")
      print(f"  Steps: {info['steps']}")
      print(f"  Modules: {', '.join(info['modules'])}")
      print(f"  Estimated Duration: {info['estimated_duration']}s")
  ```

##### `get_performance_summary(workflow_name: Optional[str] = None) -> Dict[str, Any]`

- **Description**: Get comprehensive performance summary for workflows.
- **Parameters**:
  - `workflow_name` (Optional[str]): Specific workflow name to analyze. If None, returns summary for all workflows.
- **Returns**: Performance summary containing:
  - `performance_stats` (Dict): Detailed performance metrics from monitor
  - `total_workflows_executed` (int): Total number of workflow executions
  - `successful_executions` (int): Number of successful executions
  - `failed_executions` (int): Number of failed executions
  - `average_execution_time` (float): Average execution time in seconds
  - `module_usage_stats` (Dict): Usage statistics by module
- **Note**: Requires performance monitoring to be enabled. Returns error message if monitoring is not available.

---

### TaskOrchestrator

Coordinates individual task execution with dependency management.

#### Methods

##### `create_task(name: str, module: str, action: str, **kwargs) -> Task`

- **Description**: Creates and adds a new task.
- **Parameters**:
  - `name` (string): Task name
  - `module` (string): Codomyrmex module name
  - `action` (string): Module action/function
  - `parameters` (dict, optional): Action parameters
  - `dependencies` (list, optional): Task dependencies
  - `priority` (TaskPriority, optional): Task priority
  - `timeout` (int, optional): Timeout in seconds
  - `max_retries` (int, optional): Maximum retry attempts
  - `resources` (list, optional): Required resources
- **Returns**: Task object
- **Example**:
  ```python
  orchestrator = TaskOrchestrator()
  task = orchestrator.create_task(
      "analyze_code",
      "static_analysis",
      "analyze_code_quality",
      parameters={"path": "./src"},
      priority=TaskPriority.HIGH,
      timeout=300
  )
  ```

##### `add_task(task: Task) -> str`

- **Description**: Add a task to the orchestrator.
- **Parameters**:
  - `task` (Task): Task object to add
- **Returns**: Task ID (string)
- **Example**:
  ```python
  task = Task(
      name="analyze_code",
      module="static_analysis",
      action="analyze_code_quality",
      parameters={"path": "./src"}
  )
  task_id = orchestrator.add_task(task)
  ```

##### `execute_task(task: Task) -> TaskResult`

- **Description**: Executes a single task synchronously. This method is called internally by the execution loop.
- **Parameters**:
  - `task` (Task): Task object to execute
- **Returns**: TaskResult object with the following structure:
  ```python
  {
    "success": bool,
    "data": Any,  # Result data from the action function
    "error_message": Optional[str],  # Error message if failed
    "error_type": Optional[str],  # Error type/category
    "execution_time": float,  # Execution time in seconds
    "memory_usage": float,  # Memory usage (default: 0.0)
    "metadata": dict  # Additional metadata
  }
  ```
- **Raises**:
  - Various exceptions depending on module/action execution failures
- **Note**: This method is typically called by the internal execution loop. For external use, add tasks via `add_task()` and start execution.

##### `start_execution()`

- **Description**: Start the task execution engine. This starts a background thread that processes tasks.
- **Note**: Must be called before tasks will be executed automatically.

##### `stop_execution()`

- **Description**: Stop the task execution engine. Waits for the execution thread to complete (with 5 second timeout).

##### `wait_for_completion(timeout: Optional[float] = None) -> bool`

- **Description**: Wait for all tasks to complete.
- **Parameters**:
  - `timeout` (Optional[float]): Maximum time to wait in seconds. If None, waits indefinitely.
- **Returns**: bool - True if all tasks completed, False if timeout was reached.

##### `cancel_task(task_id: str) -> bool`

- **Description**: Cancel a task.
- **Parameters**:
  - `task_id` (string): ID of task to cancel
- **Returns**: bool - True if task was cancelled, False if task not found or already completed/failed/cancelled

##### `get_task(task_id: str) -> Optional[Task]`

- **Description**: Get a task by ID.
- **Parameters**:
  - `task_id` (string): Task ID
- **Returns**: Task object or None if not found

##### `get_task_result(task_id: str) -> Optional[TaskResult]`

- **Description**: Get the result of a completed task.
- **Parameters**:
  - `task_id` (string): Task ID
- **Returns**: TaskResult object or None if task not found or not completed

##### `list_tasks(status: Optional[TaskStatus] = None) -> List[Task]`

- **Description**: List tasks, optionally filtered by status.
- **Parameters**:
  - `status` (Optional[TaskStatus]): Filter by task status
- **Returns**: List of Task objects

##### `get_execution_stats() -> Dict[str, Any]`

- **Description**: Gets execution statistics.
- **Returns**: Statistics dictionary
  ```python
  {
    "total_tasks": int,
    "pending": int,
    "ready": int,
    "running": int,
    "completed": int,
    "failed": int,
    "cancelled": int,
    "total_execution_time": float,
    "average_execution_time": float
  }
  ```

---

### ProjectManager

High-level project lifecycle management.

#### Methods

##### `create_project(name: str, template_name: str = None, path: str = None, **kwargs) -> Project`

- **Description**: Creates a new project from a template.
- **Parameters**:
  - `name` (string): Project name
  - `template_name` (string, optional): Template to use
  - `path` (string, optional): Project directory path
  - `description` (string, optional): Project description
  - `author` (string, optional): Project author
  - `tags` (list, optional): Project tags
- **Returns**: Project object
- **Example**:
  ```python
  manager = ProjectManager()
  project = manager.create_project(
      "web-app-analysis",
      template_name="web_application",
      description="Analysis of web application code",
      author="Development Team"
  )
  ```

##### `execute_project_workflow(project_name: str, workflow_name: str, **params) -> Dict[str, Any]`

- **Description**: Executes a workflow for a specific project.
- **Parameters**:
  - `project_name` (string): Project name
  - `workflow_name` (string): Workflow to execute
  - `**params`: Workflow parameters
- **Returns**: Execution result dictionary

##### `get_project_status(name: str) -> Optional[Dict[str, Any]]`

- **Description**: Gets detailed project status.
- **Parameters**:
  - `name` (string): Project name
- **Returns**: Status dictionary
  ```python
  {
    "name": str,
    "status": str,  # "planning", "active", "completed", etc.
    "type": str,
    "version": str,
    "path": str,
    "workflows": int,
    "active_workflows": int,
    "required_modules": list,
    "created_at": str,
    "updated_at": str,
    "milestones": dict,
    "metrics": dict
  }
  ```

##### `list_templates() -> List[str]`

- **Description**: Lists available project templates.
- **Returns**: List of template names

---

### ResourceManager

System resource allocation and management.

#### Methods

##### `allocate_resources(user_id: str, requirements: Dict[str, Dict[str, Any]], timeout: Optional[int] = None) -> Optional[Dict[str, str]]`

- **Description**: Allocates resources to a user based on requirements.
- **Parameters**:
  - `user_id` (string): User/task identifier
  - `requirements` (dict): Resource requirements mapping resource types to required amounts. Example: `{"cpu": {"cores": 2}, "memory": {"gb": 4}}`
  - `timeout` (Optional[int]): Allocation timeout in seconds. If specified, creates expiration time for allocations.
- **Returns**: Dictionary mapping requirement keys to allocated resource IDs, or None if allocation failed
- **Raises**:
  - No exceptions raised, but returns None on failure
- **Note**: Automatically selects best-fit resources based on utilization. Rolls back all allocations if any resource cannot be allocated.
- **Example**:
  ```python
  manager = ResourceManager()
  allocation = manager.allocate_resources(
      "task_123",
      {
          "cpu": {"cores": 2},
          "memory": {"gb": 4},
          "disk": {"gb": 10}
      },
      timeout=60
  )
  ```

##### `deallocate_resources(user_id: str, allocation_ids: List[str] = None) -> bool`

- **Description**: Deallocates resources for a user.
- **Parameters**:
  - `user_id` (string): User identifier
  - `allocation_ids` (list, optional): Specific allocations to release
- **Returns**: True if successful

##### `get_resource_usage(resource_id: Optional[str] = None) -> Dict[str, Any]`

- **Description**: Gets resource usage statistics.
- **Parameters**:
  - `resource_id` (Optional[str]): Specific resource ID, or None for system-wide statistics
- **Returns**: Usage statistics dictionary. For specific resource:
  ```python
  {
    "resource_id": str,
    "name": str,
    "type": str,
    "status": str,
    "capacity": dict,
    "allocated": dict,
    "utilization": dict,  # Percentage utilization per capacity key
    "current_users": int,
    "total_allocations": int
  }
  ```
  For system-wide:
  ```python
  {
    "total_resources": int,
    "total_allocations": int,
    "resources_by_type": dict,  # Count by resource type
    "utilization_summary": dict  # Average utilization by type
  }
  ```

##### `list_resources(resource_type: Optional[ResourceType] = None, status: Optional[ResourceStatus] = None) -> List[Resource]`

- **Description**: List resources, optionally filtered by type and status.
- **Parameters**:
  - `resource_type` (Optional[ResourceType]): Filter by resource type
  - `status` (Optional[ResourceStatus]): Filter by resource status
- **Returns**: List of Resource objects

##### `add_resource(resource: Resource) -> bool`

- **Description**: Add a resource to the manager.
- **Parameters**:
  - `resource` (Resource): Resource object to add
- **Returns**: bool - True if added successfully, False if resource ID already exists

##### `get_user_allocations(user_id: str) -> List[Dict[str, Any]]`

- **Description**: Get all allocations for a specific user.
- **Parameters**:
  - `user_id` (string): User identifier
- **Returns**: List of allocation dictionaries with resource details

##### `health_check() -> Dict[str, Any]`

- **Description**: Perform health check on all resources.
- **Returns**: Dictionary with health status, resource health details, and issues list

---

## Data Models

### WorkflowExecution Class

Workflows are stored as lists of WorkflowStep objects. During execution, a WorkflowExecution object tracks the execution state:

```python
@dataclass
class WorkflowExecution:
    workflow_name: str  # Name of the workflow being executed
    status: WorkflowStatus = WorkflowStatus.PENDING  # Current execution status
    start_time: Optional[datetime] = None  # When execution began
    end_time: Optional[datetime] = None  # When execution completed/failed
    results: Dict[str, Any] = field(default_factory=dict)  # Results from each completed step
    errors: List[str] = field(default_factory=dict)  # List of error messages encountered
    performance_metrics: Dict[str, Any] = field(default_factory=dict)  # Performance data for each step
```

### WorkflowStep Class

```python
@dataclass
class WorkflowStep:
    name: str  # Unique identifier for this step within the workflow
    module: str  # Codomyrmex module name (e.g., 'static_analysis')
    action: str  # Specific action/function to call within the module
    parameters: Dict[str, Any] = field(default_factory=dict)  # Parameters to pass to the action function
    dependencies: List[str] = field(default_factory=list)  # List of step names that must complete before this step
    timeout: Optional[int] = None  # Maximum execution time in seconds (None for no limit)
    retry_count: int = 0  # Current number of retry attempts (internal use)
    max_retries: int = 3  # Maximum number of retry attempts before marking as failed
```

### Task Class

```python
@dataclass
class Task:
    id: str = field(default_factory=lambda: str(uuid.uuid4()))  # Unique task ID
    name: str = ""  # Task name (auto-generated if not provided)
    description: str = ""  # Task description
    module: str = ""  # Codomyrmex module name
    action: str = ""  # Module action/function name
    parameters: Dict[str, Any] = field(default_factory=dict)  # Action parameters
    
    # Dependencies and scheduling
    dependencies: List[str] = field(default_factory=list)  # Task IDs this depends on
    priority: TaskPriority = TaskPriority.NORMAL
    resources: List[TaskResource] = field(default_factory=list)  # Required resources
    
    # Execution control
    timeout: Optional[int] = None  # Timeout in seconds
    max_retries: int = 3  # Maximum retry attempts
    retry_delay: float = 1.0  # Seconds between retries
    
    # Status tracking
    status: TaskStatus = TaskStatus.PENDING
    retry_count: int = 0
    created_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
    started_at: Optional[datetime] = None
    completed_at: Optional[datetime] = None
    result: Optional[TaskResult] = None
    
    # Metadata
    tags: List[str] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
```

### Project Class

```python
@dataclass
class Project:
    name: str
    description: str = ""
    type: ProjectType = ProjectType.CUSTOM
    path: str = ""
    status: ProjectStatus = ProjectStatus.PLANNING
    workflows: List[str] = field(default_factory=list)
    config: Dict[str, Any] = field(default_factory=dict)
    # ... additional fields
```

### Resource Class

```python
@dataclass
class Resource:
    id: str  # Unique resource identifier
    name: str  # Human-readable resource name
    type: ResourceType  # Resource type
    description: str = ""  # Resource description
    status: ResourceStatus = ResourceStatus.AVAILABLE  # Current availability status
    capacity: Dict[str, Any] = field(default_factory=dict)  # Resource capacity (e.g., {"cores": 8, "gb": 16})
    allocated: Dict[str, Any] = field(default_factory=dict)  # Currently allocated amounts
    limits: ResourceLimits = field(default_factory=ResourceLimits)  # Usage limits and quotas
    total_allocations: int = 0  # Total number of allocations made
    total_usage_time: float = 0.0  # Total usage time
    current_users: Set[str] = field(default_factory=set)  # Set of current user IDs
    metadata: Dict[str, Any] = field(default_factory=dict)  # Additional metadata
    tags: List[str] = field(default_factory=list)  # Resource tags
    created_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
    updated_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
```

### TaskResource Class

```python
@dataclass
class TaskResource:
    type: ResourceType  # Type of resource required
    identifier: str  # Resource ID/name
    mode: str = "read"  # Access mode: "read", "write", or "exclusive"
    timeout: Optional[int] = None  # Resource timeout
```

### ResourceAllocation Class

```python
@dataclass
class ResourceAllocation:
    id: str  # Allocation ID
    resource_id: str  # Resource identifier
    user_id: str  # Task ID or user ID
    allocated: Dict[str, Any]  # Allocated amounts
    allocated_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
    expires_at: Optional[datetime] = None  # Expiration time if timeout specified
    metadata: Dict[str, Any] = field(default_factory=dict)
```

## Enumerations

### WorkflowStatus
- `PENDING`: Workflow is pending execution
- `RUNNING`: Workflow is currently executing
- `COMPLETED`: Workflow completed successfully
- `FAILED`: Workflow execution failed
- `CANCELLED`: Workflow was cancelled

### TaskStatus
- `PENDING`: Task is waiting to be executed
- `READY`: Task is ready (dependencies satisfied)
- `RUNNING`: Task is currently executing
- `COMPLETED`: Task completed successfully
- `FAILED`: Task execution failed
- `CANCELLED`: Task was cancelled
- `SKIPPED`: Task was skipped

### TaskPriority
- `LOW`: Low priority task
- `NORMAL`: Normal priority task
- `HIGH`: High priority task
- `CRITICAL`: Critical priority task

### ProjectStatus
- `PLANNING`: Project is in planning phase
- `ACTIVE`: Project is actively being worked on
- `PAUSED`: Project work is paused
- `COMPLETED`: Project is completed
- `ARCHIVED`: Project is archived
- `FAILED`: Project failed

### ResourceType
- `CPU`: CPU cores
- `MEMORY`: System memory
- `DISK`: Disk storage
- `NETWORK`: Network bandwidth
- `FILE`: File access
- `DATABASE`: Database connection
- `EXTERNAL_API`: External API quota
- `GPU`: GPU resources
- `QUEUE`: Queue resources
- `LOCK`: Lock resources
- `SEMAPHORE`: Semaphore resources

### ResourceStatus
- `AVAILABLE`: Resource is available for allocation
- `IN_USE`: Resource is currently in use
- `RESERVED`: Resource is reserved
- `MAINTENANCE`: Resource is in maintenance mode
- `UNAVAILABLE`: Resource is unavailable

### OrchestrationMode
- `SEQUENTIAL`: Execute workflows/tasks one after another
- `PARALLEL`: Execute workflows/tasks in parallel when possible
- `PRIORITY`: Execute based on priority ordering
- `RESOURCE_AWARE`: Execute based on resource availability

### SessionStatus
- `PENDING`: Session is pending
- `ACTIVE`: Session is active
- `COMPLETED`: Session completed successfully
- `CANCELLED`: Session was cancelled
- `FAILED`: Session failed

## Error Handling

### Exception Classes

#### `OrchestrationError`
Base exception for orchestration operations.

#### `WorkflowExecutionError`
Raised when workflow execution fails.

#### `TaskExecutionError`
Raised when task execution fails.

#### `ResourceAllocationError`
Raised when resource allocation fails.

#### `ProjectManagementError`
Raised when project operations fail.

### Error Response Format

```python
{
    "success": False,
    "error": str,           # Error message
    "error_type": str,      # Error type/category
    "error_code": str,      # Specific error code
    "details": dict,        # Additional error details
    "suggestions": list     # Suggested remediation steps
}
```

## Configuration

### Environment Variables
- `CODOMYRMEX_ORCHESTRATION_DIR`: Base directory for orchestration data
- `CODOMYRMEX_MAX_WORKERS`: Maximum number of worker threads
- `CODOMYRMEX_RESOURCE_CONFIG`: Path to resource configuration file

### Configuration File Format (JSON)
```json
{
  "max_workers": 4,
  "workflows_dir": "./workflows",
  "projects_dir": "./projects", 
  "templates_dir": "./templates",
  "resource_config": "./resources.json",
  "performance_monitoring": true,
  "session_timeout": 3600,
  "cleanup_interval": 300
}
```

## Integration Examples

### Basic Workflow Execution
```python
from codomyrmex.logistics.orchestration.project import OrchestrationEngine

engine = OrchestrationEngine()
result = engine.execute_workflow(
    "ai-analysis",
    code_path="./src",
    output_path="./reports",
    ai_provider="openai"
)

if result['success']:
    print(f"Analysis completed in {result['execution_time']} seconds")
    print(f"Results: {result['results']}")
else:
    print(f"Analysis failed: {result['error']}")
```

### Project-based Development
```python
from codomyrmex.logistics.orchestration.project import ProjectManager

pm = ProjectManager()

# Create project
project = pm.create_project(
    "chatbot-analysis",
    template_name="ai_analysis",
    description="AI analysis of chatbot conversations"
)

# Execute project workflow
result = pm.execute_project_workflow(
    "chatbot-analysis",
    "ai-analysis",
    data_path="./conversations",
    focus_areas=["sentiment", "intent", "quality"]
)

# Track milestone
pm.add_project_milestone(
    "chatbot-analysis",
    "initial_analysis_complete",
    {"quality_score": 8.5, "insights_generated": 23}
)
```

### Custom Task Orchestration
```python
from codomyrmex.logistics.orchestration.project import TaskOrchestrator, Task, TaskPriority, TaskResource, ResourceType

orchestrator = TaskOrchestrator(max_workers=4)
orchestrator.start_execution()

# Create dependent tasks
analysis_task = orchestrator.create_task(
    "analyze_code",
    "static_analysis",
    "analyze_code_quality",
    parameters={"path": "./src"},
    priority=TaskPriority.HIGH,
    resources=[
        TaskResource(type=ResourceType.CPU, identifier="system_cpu", mode="read")
    ]
)

visualization_task = orchestrator.create_task(
    "create_chart", 
    "data_visualization",
    "create_bar_chart",
    parameters={
        "data": {"metric": "complexity", "values": [3, 7, 2, 5]},
        "title": "Code Quality Metrics"
    },
    dependencies=[analysis_task.id],
    priority=TaskPriority.NORMAL
)

# Wait for completion
completed = orchestrator.wait_for_completion(timeout=300)

# Get results
if completed:
    analysis_result = orchestrator.get_task_result(analysis_task.id)
    if analysis_result and analysis_result.success:
        print(f"Analysis completed: {analysis_result.data}")
    
    stats = orchestrator.get_execution_stats()
    print(f"Completed {stats['completed']} tasks")
else:
    print("Task execution timed out")
```

## Performance Considerations

- **Resource Management**: Always specify resource requirements for better allocation
- **Parallel Execution**: Use parallel mode for independent tasks and workflows
- **Caching**: Results are cached based on parameters and dependencies
- **Monitoring**: Enable performance monitoring for production deployments
- **Cleanup**: Sessions and resources are automatically cleaned up

## Rate Limiting

- **Workflow Execution**: Limited by available workers and resources
- **Task Execution**: Limited by task orchestrator configuration
- **Resource Allocation**: Subject to system resource limits
- **API Calls**: External API calls respect provider rate limits

## Versioning

This API follows semantic versioning. Breaking changes to method signatures or return values will result in a major version update, while backward-compatible enhancements will result in minor version updates.

Current API Version: **1.0.0**

## Navigation Links

- **Parent**: [Project Overview](../README.md)
- **Module Index**: [All Agents](../../AGENTS.md)
- **Documentation**: [Reference Guides](../../../../../docs/README.md)
- **Home**: [Root README](../../../README.md)
