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WorkforceState

class WorkforceState(Enum):
Workforce execution state for human intervention support.

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WorkforceSnapshot

class WorkforceSnapshot:
Snapshot of workforce state for resuming execution.

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init

def __init__(
    self,
    main_task: Optional[Task] = None,
    pending_tasks: Optional[Deque[Task]] = None,
    completed_tasks: Optional[List[Task]] = None,
    task_dependencies: Optional[Dict[str, List[str]]] = None,
    assignees: Optional[Dict[str, str]] = None,
    current_task_index: int = 0,
    description: str = ''
):

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Workforce

class Workforce(BaseNode):
A system where multiple worker nodes (agents) cooperate together to solve tasks. It can assign tasks to worker nodes and also take strategies such as create new worker, decompose tasks, etc. to handle situations when the task fails. The workforce uses three specialized ChatAgents internally:
  • Coordinator Agent: Assigns tasks to workers based on their capabilities
  • Task Planner Agent: Decomposes complex tasks and composes results
  • Dynamic Workers: Created at runtime when tasks fail repeatedly
Parameters:
  • description (str): Description of the workforce.
  • children (Optional[List[BaseNode]], optional): List of child nodes under this node. Each child node can be a worker node or another workforce node. (default: :obj:None)
  • coordinator_agent (Optional[ChatAgent], optional): A custom coordinator agent instance for task assignment and worker creation. If provided, the workforce will create a new agent using this agent’s model configuration but with the required system message and functionality. If None, a default agent will be created using DEFAULT model settings. (default: :obj:None)
  • task_agent (Optional[ChatAgent], optional): A custom task planning agent instance for task decomposition and composition. If provided, the workforce will create a new agent using this agent’s model configuration but with the required system message and tools (TaskPlanningToolkit). If None, a default agent will be created using DEFAULT model settings. (default: :obj:None)
  • new_worker_agent (Optional[ChatAgent], optional): A template agent for workers created dynamically at runtime when existing workers cannot handle failed tasks. If None, workers will be created with default settings including SearchToolkit, CodeExecutionToolkit, and ThinkingToolkit. (default: :obj:None)
  • graceful_shutdown_timeout (float, optional): The timeout in seconds for graceful shutdown when a task fails 3 times. During this period, the workforce remains active for debugging. Set to 0 for immediate shutdown. (default: :obj:15.0)
  • task_timeout_seconds (Optional[float], optional): The timeout in seconds for waiting for tasks to be returned by workers. If None, uses the global TASK_TIMEOUT_SECONDS value (600.0 seconds). Increase this value for tasks that require more processing time. (default: :obj:None)
  • share_memory (bool, optional): Whether to enable shared memory across SingleAgentWorker instances in the workforce. When enabled, all SingleAgentWorker instances, coordinator agent, and task planning agent will share their complete conversation history and function-calling trajectory, providing better context for task handoffs and continuity. Note: Currently only supports SingleAgentWorker instances; RolePlayingWorker and nested Workforce instances do not participate in memory sharing. (default: :obj:False)
  • use_structured_output_handler (bool, optional): Whether to use the structured output handler instead of native structured output. When enabled, the workforce will use prompts with structured output instructions and regex extraction to parse responses. This ensures compatibility with agents that don’t reliably support native structured output. When disabled, the workforce uses the native response_format parameter. (default: :obj:True)
  • callbacks (Optional[List[WorkforceCallback]], optional): A list of callback handlers to observe and record workforce lifecycle events and metrics (e.g., task creation/assignment/start/completion/ failure, worker creation/deletion, all-tasks-completed). All items must be instances of :class:WorkforceCallback, otherwise
  • a: class:ValueError is raised. If none of the provided callbacks implement :class:WorkforceMetrics, a built-in :class:WorkforceLogger (implements both callback and metrics) is added automatically. If at least one provided callback
  • implements: class:WorkforceMetrics, no default logger is added. (default: :obj:None)
Note: When custom coordinator_agent or task_agent are provided, the workforce will preserve the user’s system message and append the required workforce coordination or task planning instructions to it. This ensures both the user’s intent is preserved and proper workforce functionality is maintained. All other agent configurations (model, memory, tools, etc.) will also be preserved.

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init

def __init__(
    self,
    description: str,
    children: Optional[List[BaseNode]] = None,
    coordinator_agent: Optional[ChatAgent] = None,
    task_agent: Optional[ChatAgent] = None,
    new_worker_agent: Optional[ChatAgent] = None,
    graceful_shutdown_timeout: float = 15.0,
    share_memory: bool = False,
    use_structured_output_handler: bool = True,
    task_timeout_seconds: Optional[float] = None,
    callbacks: Optional[List[WorkforceCallback]] = None
):

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_initialize_callbacks

def _initialize_callbacks(self, callbacks: Optional[List[WorkforceCallback]]):
Validate, register, and prime workforce callbacks.

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_notify_worker_created

def _notify_worker_created(self, worker_node: BaseNode):
Emit a worker-created event to all registered callbacks.

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_get_or_create_shared_context_utility

def _get_or_create_shared_context_utility(self, session_id: Optional[str] = None):
Get or create the shared context utility for workflow management. This method creates the context utility only when needed, avoiding unnecessary session folder creation during initialization. Parameters:
  • session_id (Optional[str]): Custom session ID to use. If None, auto-generates a timestamped session ID. (default: :obj:None)
Returns: ContextUtility: The shared context utility instance.

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_validate_agent_compatibility

def _validate_agent_compatibility(self, agent: ChatAgent, agent_context: str = 'agent'):
Validate that agent configuration is compatible with workforce settings. Parameters:
  • agent (ChatAgent): The agent to validate.
  • agent_context (str): Context description for error messages.

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_attach_pause_event_to_agent

def _attach_pause_event_to_agent(self, agent: ChatAgent):
Ensure the given ChatAgent shares this workforce’s pause_event. If the agent already has a different pause_event we overwrite it and emit a debug log (it is unlikely an agent needs multiple independent pause controls once managed by this workforce).

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_ensure_pause_event_in_kwargs

def _ensure_pause_event_in_kwargs(self, kwargs: Optional[Dict]):
Insert pause_event into kwargs dict for ChatAgent construction.

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repr

def __repr__(self):

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_collect_shared_memory

def _collect_shared_memory(self):
Returns: Dict[str, List]: A dictionary mapping agent types to their memory records. Contains entries for ‘coordinator’, ‘task_agent’, and ‘workers’.

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_share_memory_with_agents

def _share_memory_with_agents(self, shared_memory: Dict[str, List]):
Share collected memory with coordinator, task agent, and SingleAgentWorker instances. Parameters:
  • shared_memory (Dict[str, List]): Memory records collected from all agents to be shared.

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_sync_shared_memory

def _sync_shared_memory(self):
Synchronize memory across all agents by collecting and sharing.

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_update_dependencies_for_decomposition

def _update_dependencies_for_decomposition(self, original_task: Task, subtasks: List[Task]):
Update dependency tracking when a task is decomposed into subtasks. Tasks that depended on the original task should now depend on all subtasks. The last subtask inherits the original task’s dependencies.

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_increment_in_flight_tasks

def _increment_in_flight_tasks(self, task_id: str):
Safely increment the in-flight tasks counter with logging.

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_decrement_in_flight_tasks

def _decrement_in_flight_tasks(self, task_id: str, context: str = ''):
Safely decrement the in-flight tasks counter with safety checks.

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_cleanup_task_tracking

def _cleanup_task_tracking(self, task_id: str):
Clean up tracking data for a task to prevent memory leaks. Parameters:
  • task_id (str): The ID of the task to clean up.

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_decompose_task

def _decompose_task(self, task: Task):
Returns: Union[List[Task], Generator[List[Task], None, None]]: The subtasks or generator of subtasks.

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_analyze_task

def _analyze_task(self, task: Task):
Unified task analysis for both failures and quality evaluation. This method consolidates the logic for analyzing task failures and evaluating task quality, using the unified TASK_ANALYSIS_PROMPT. Parameters:
  • task (Task): The task to analyze
  • for_failure (bool): True for failure analysis, False for quality evaluation
  • error_message (Optional[str]): Error message, required when for_failure=True
Returns: TaskAnalysisResult: Unified analysis result with recovery strategy and optional quality metrics

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pause

def pause(self):
Pause the workforce execution. If the internal event-loop is already running we schedule the asynchronous pause coroutine onto it. When the loop has not yet been created (e.g. the caller presses the hot-key immediately after workforce start-up) we fall back to a synchronous state change so that no tasks will be scheduled until the loop is ready.

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resume

def resume(self):
Resume execution after a manual pause.

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stop_gracefully

def stop_gracefully(self):
Request workforce to finish current in-flight work then halt. Works both when the internal event-loop is alive and when it has not yet been started. In the latter case we simply mark the stop flag so that the loop (when it eventually starts) will exit immediately after initialisation.

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skip_gracefully

def skip_gracefully(self):
Request workforce to skip current pending tasks and move to next main task from the queue. If no main tasks exist, acts like stop_gracefully. This method clears the current pending subtasks and moves to the next main task in the queue if available. Works both when the internal event-loop is alive and when it has not yet been started.

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save_snapshot

def save_snapshot(self, description: str = ''):
Save current state as a snapshot.

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list_snapshots

def list_snapshots(self):
List all available snapshots.

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get_pending_tasks

def get_pending_tasks(self):
Get current pending tasks for human review.

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get_completed_tasks

def get_completed_tasks(self):
Get completed tasks.

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modify_task_content

def modify_task_content(self, task_id: str, new_content: str):
Modify the content of a pending task.

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add_task

def add_task(
    self,
    content: str,
    task_id: Optional[str] = None,
    additional_info: Optional[Dict[str, Any]] = None,
    as_subtask: bool = False,
    insert_position: int = -1
):
Add a new task to the workforce. By default, this method adds a main task that will be decomposed into subtasks. Set as_subtask=True to add a task directly to the pending subtask queue without decomposition. Parameters:
  • content (str): The content of the task.
  • task_id (Optional[str], optional): Optional ID for the task. If not provided, a unique ID will be generated.
  • additional_info (Optional[Dict[str, Any]], optional): Optional additional metadata for the task.
  • as_subtask (bool, optional): If True, adds the task directly to the pending subtask queue. If False, adds as a main task that will be decomposed. Defaults to False.
  • insert_position (int, optional): Position to insert the task in the pending queue. Only applies when as_subtask=True. Defaults to -1 (append to end).
Returns: Task: The created task object.

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add_main_task

def add_main_task(
    self,
    content: str,
    task_id: Optional[str] = None,
    additional_info: Optional[Dict[str, Any]] = None
):
Add a new main task that will be decomposed into subtasks. This is an alias for :meth:add_task with as_subtask=False. Parameters:
  • content (str): The content of the main task.
  • task_id (Optional[str], optional): Optional ID for the task.
  • additional_info (Optional[Dict[str, Any]], optional): Optional additional metadata.
Returns: Task: The created main task object.

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add_subtask

def add_subtask(
    self,
    content: str,
    task_id: Optional[str] = None,
    additional_info: Optional[Dict[str, Any]] = None,
    insert_position: int = -1
):
Add a new subtask to the current pending queue. This is an alias for :meth:add_task with as_subtask=True. Parameters:
  • content (str): The content of the subtask.
  • task_id (Optional[str], optional): Optional ID for the task.
  • additional_info (Optional[Dict[str, Any]], optional): Optional additional metadata.
  • insert_position (int, optional): Position to insert the task. Defaults to -1 (append to end).
Returns: Task: The created subtask object.

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remove_task

def remove_task(self, task_id: str):
Remove a task from the pending queue or main task queue. Parameters:
  • task_id (str): The ID of the task to remove.
Returns: bool: True if task was found and removed, False otherwise.

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reorder_tasks

def reorder_tasks(self, task_ids: List[str]):
Reorder pending tasks according to the provided task IDs list.

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resume_from_task

def resume_from_task(self, task_id: str):
Resume execution from a specific task.

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restore_from_snapshot

def restore_from_snapshot(self, snapshot_index: int):
Restore workforce state from a snapshot.

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get_workforce_status

def get_workforce_status(self):
Get current workforce status for human review.

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process_task

def process_task(self, task: Task):
Synchronous wrapper for process_task that handles async operations internally. Parameters:
  • task (Task): The task to be processed.
Returns: Task: The updated task.

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_process_task_with_intervention

def _process_task_with_intervention(self, task: Task):
Process task with human intervention support. This creates and manages its own event loop to allow for pausing/resuming functionality. Parameters:
  • task (Task): The task to be processed.
Returns: Task: The updated task.

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continue_from_pause

def continue_from_pause(self):
Returns: Optional[Task]: The completed task if execution finishes, None if still running/paused.

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_start_child_node_when_paused

def _start_child_node_when_paused(self, start_coroutine: Coroutine):
Helper to start a child node when workforce is paused. Parameters:
  • start_coroutine: The coroutine to start (e.g., worker_node.start())

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add_single_agent_worker

def add_single_agent_worker(
    self,
    description: str,
    worker: ChatAgent,
    pool_max_size: int = DEFAULT_WORKER_POOL_SIZE,
    enable_workflow_memory: bool = False
):
Add a worker node to the workforce that uses a single agent. Can be called when workforce is paused to dynamically add workers. Parameters:
  • description (str): Description of the worker node.
  • worker (ChatAgent): The agent to be added.
  • pool_max_size (int): Maximum size of the agent pool. (default: :obj:10)
  • enable_workflow_memory (bool): Whether to enable workflow memory accumulation. Set to True if you plan to call save_workflow_memories(). (default: :obj:False)
Returns: Workforce: The workforce node itself.

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add_role_playing_worker

def add_role_playing_worker(
    self,
    description: str,
    assistant_role_name: str,
    user_role_name: str,
    assistant_agent_kwargs: Optional[Dict] = None,
    user_agent_kwargs: Optional[Dict] = None,
    summarize_agent_kwargs: Optional[Dict] = None,
    chat_turn_limit: int = 3
):
Add a worker node to the workforce that uses RolePlaying system. Can be called when workforce is paused to dynamically add workers. Parameters:
  • description (str): Description of the node.
  • assistant_role_name (str): The role name of the assistant agent.
  • user_role_name (str): The role name of the user agent.
  • assistant_agent_kwargs (Optional[Dict]): The keyword arguments to initialize the assistant agent in the role playing, like the model name, etc. (default: :obj:None)
  • user_agent_kwargs (Optional[Dict]): The keyword arguments to initialize the user agent in the role playing, like the model name, etc. (default: :obj:None)
  • summarize_agent_kwargs (Optional[Dict]): The keyword arguments to initialize the summarize agent, like the model name, etc. (default: :obj:None)
  • chat_turn_limit (int): The maximum number of chat turns in the role playing. (default: :obj:3)
Returns: Workforce: The workforce node itself.

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add_workforce

def add_workforce(self, workforce: Workforce):
Add a workforce node to the workforce. Can be called when workforce is paused to dynamically add workers. Parameters:
  • workforce (Workforce): The workforce node to be added.
Returns: Workforce: The workforce node itself.

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reset

def reset(self):
Reset the workforce and all the child nodes under it. Can only be called when the workforce is not running.

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save_workflow_memories

def save_workflow_memories(self, session_id: Optional[str] = None):
Save workflow memories for all SingleAgentWorker instances in the workforce. .. deprecated:: 0.2.80 This synchronous method processes workers sequentially, which can be slow for multiple agents. Use :meth:save_workflow_memories_async instead for parallel processing and significantly better performance. This method iterates through all child workers and triggers workflow saving for SingleAgentWorker instances using their save_workflow_memories() method. Other worker types are skipped. Parameters:
  • session_id (Optional[str]): Custom session ID to use for saving workflows. If None, auto-generates a timestamped session ID. Useful for organizing workflows by project or context. (default: :obj:None)
Returns: Dict[str, str]: Dictionary mapping worker node IDs to save results. Values are either file paths (success) or error messages (failure). Note: For better performance with multiple workers, use the async version:: results = await workforce.save_workflow_memories_async() See Also: :meth:save_workflow_memories_async: Async version with parallel processing for significantly better performance.

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load_workflow_memories

def load_workflow_memories(
    self,
    session_id: Optional[str] = None,
    worker_max_workflows: int = 3,
    coordinator_max_workflows: int = 5,
    task_agent_max_workflows: int = 3
):
Load workflow memories for all SingleAgentWorker instances in the workforce. This method iterates through all child workers and loads relevant workflow files for SingleAgentWorker instances using their load_workflow_memories() method. Workers match files based on their description names. Parameters:
  • session_id (Optional[str]): Specific workforce session ID to load from. If None, searches across all sessions. (default: :obj:None)
  • worker_max_workflows (int): Maximum number of workflow files to load per worker agent. (default: :obj:3)
  • coordinator_max_workflows (int): Maximum number of workflow files to load for the coordinator agent. (default: :obj:5)
  • task_agent_max_workflows (int): Maximum number of workflow files to load for the task planning agent. (default: :obj:3)
Returns: Dict[str, bool]: Dictionary mapping worker node IDs to load success status. True indicates successful loading, False indicates failure.

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_load_management_agent_workflows

def _load_management_agent_workflows(
    self,
    coordinator_max_workflows: int,
    task_agent_max_workflows: int,
    session_id: Optional[str] = None
):
Load workflow summaries for coordinator and task planning agents. This method loads aggregated workflow summaries to help:
  • Coordinator agent: understand task assignment patterns and worker capabilities
  • Task agent: understand task decomposition patterns and successful strategies
Parameters:
  • coordinator_max_workflows (int): Maximum number of workflow files to load for the coordinator agent.
  • task_agent_max_workflows (int): Maximum number of workflow files to load for the task planning agent.
  • session_id (Optional[str]): Specific session ID to load from. If None, searches across all sessions.

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set_channel

def set_channel(self, channel: TaskChannel):
Set the channel for the node and all the child nodes under it.

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_get_child_nodes_info

def _get_child_nodes_info(self):
Get the information of all the child nodes under this node.

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_get_node_info

def _get_node_info(self, node):
Get descriptive information for a specific node type.

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_get_single_agent_toolkit_info

def _get_single_agent_toolkit_info(self, worker: 'SingleAgentWorker'):
Get formatted information for a SingleAgentWorker node.

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_group_tools_by_toolkit

def _group_tools_by_toolkit(self, tool_dict: dict):
Group tools by their parent toolkit class names.

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_get_valid_worker_ids

def _get_valid_worker_ids(self):
Returns: set: Set of valid worker IDs that can be assigned tasks.

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_call_coordinator_for_assignment

def _call_coordinator_for_assignment(self, tasks: List[Task], invalid_ids: Optional[List[str]] = None):
Call coordinator agent to assign tasks with optional validation feedback in the case of invalid worker IDs. Parameters:
  • tasks (List[Task]): Tasks to assign.
  • invalid_ids (List[str], optional): Invalid worker IDs from previous attempt (if any).
Returns: TaskAssignResult: Assignment result from coordinator.

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_validate_assignments

def _validate_assignments(self, assignments: List[TaskAssignment], valid_ids: Set[str]):
Validate task assignments against valid worker IDs. Parameters:
  • assignments (List[TaskAssignment]): Assignments to validate.
  • valid_ids (Set[str]): Set of valid worker IDs.
Returns: Tuple[List[TaskAssignment], List[TaskAssignment]]: (valid_assignments, invalid_assignments)

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_update_task_dependencies_from_assignments

def _update_task_dependencies_from_assignments(self, assignments: List[TaskAssignment], tasks: List[Task]):
Update Task.dependencies with actual Task objects based on assignments. Parameters:
  • assignments (List[TaskAssignment]): The task assignments containing dependency IDs.
  • tasks (List[Task]): The tasks that were assigned.

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get_workforce_log_tree

def get_workforce_log_tree(self):
Returns an ASCII tree representation of the task hierarchy and worker status.

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get_workforce_kpis

def get_workforce_kpis(self):
Returns a dictionary of key performance indicators.

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dump_workforce_logs

def dump_workforce_logs(self, file_path: str):
Dumps all collected logs to a JSON file. Parameters:
  • file_path (str): The path to the JSON file.

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_submit_coro_to_loop

def _submit_coro_to_loop(self, coro: 'Coroutine'):
Thread-safe submission of coroutine to the workforce loop.

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stop

def stop(self):
Stop all the child nodes under it. The node itself will be stopped by its parent node.

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clone

def clone(self, with_memory: bool = False):
Creates a new instance of Workforce with the same configuration. Parameters:
  • with_memory (bool, optional): Whether to copy the memory (conversation history) to the new instance. If True, the new instance will have the same conversation history. If False, the new instance will have a fresh memory. (default: :obj:False)
Returns: Workforce: A new instance of Workforce with the same configuration.

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to_mcp

def to_mcp(
    self,
    name: str = 'CAMEL-Workforce',
    description: str = 'A workforce system using the CAMEL AI framework for multi-agent collaboration.',
    dependencies: Optional[List[str]] = None,
    host: str = 'localhost',
    port: int = 8001
):
Expose this Workforce as an MCP server. Parameters:
  • name (str): Name of the MCP server. (default: :obj:CAMEL-Workforce)
  • description (str): Description of the workforce. If None, a generic description is used. (default: :obj:A workforce system using the CAMEL AI framework for multi-agent collaboration.)
  • dependencies (Optional[List[str]]): Additional dependencies for the MCP server. (default: :obj:None)
  • host (str): Host to bind to for HTTP transport. (default: :obj:localhost)
  • port (int): Port to bind to for HTTP transport. (default: :obj:8001)
Returns: FastMCP: An MCP server instance that can be run.
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