swarms/docs/swarms/structs/taskqueue_swarm.md

TaskQueueSwarm Documentation

The TaskQueueSwarm class is designed to manage and execute tasks using multiple agents concurrently. This class allows for the orchestration of multiple agents processing tasks from a shared queue, facilitating complex workflows where tasks can be distributed and processed in parallel by different agents.

Attributes

Attribute	Type	Description
agents	List[Agent]	The list of agents in the swarm.
task_queue	queue.Queue	A queue to store tasks for processing.
lock	threading.Lock	A lock for thread synchronization.
autosave_on	bool	Whether to automatically save the swarm metadata.
save_file_path	str	The file path for saving swarm metadata.
workspace_dir	str	The directory path of the workspace.
return_metadata_on	bool	Whether to return the swarm metadata after running.
max_loops	int	The maximum number of loops to run the swarm.
metadata	SwarmRunMetadata	Metadata about the swarm run.

Methods

__init__(self, agents: List[Agent], name: str = "Task-Queue-Swarm", description: str = "A swarm that processes tasks from a queue using multiple agents on different threads.", autosave_on: bool = True, save_file_path: str = "swarm_run_metadata.json", workspace_dir: str = os.getenv("WORKSPACE_DIR"), return_metadata_on: bool = False, max_loops: int = 1, *args, **kwargs)

The constructor initializes the TaskQueueSwarm object.

• Parameters:
  • agents (List[Agent]): The list of agents in the swarm.
  • name (str, optional): The name of the swarm. Defaults to "Task-Queue-Swarm".
  • description (str, optional): The description of the swarm. Defaults to "A swarm that processes tasks from a queue using multiple agents on different threads.".
  • autosave_on (bool, optional): Whether to automatically save the swarm metadata. Defaults to True.
  • save_file_path (str, optional): The file path to save the swarm metadata. Defaults to "swarm_run_metadata.json".
  • workspace_dir (str, optional): The directory path of the workspace. Defaults to os.getenv("WORKSPACE_DIR").
  • return_metadata_on (bool, optional): Whether to return the swarm metadata after running. Defaults to False.
  • max_loops (int, optional): The maximum number of loops to run the swarm. Defaults to 1.
  • *args: Variable length argument list.
  • **kwargs: Arbitrary keyword arguments.

add_task(self, task: str)

Adds a task to the queue.

• Parameters:
  • task (str): The task to be added to the queue.

run(self)

Runs the swarm by having agents pick up tasks from the queue.

• Returns:

  • str: JSON string of the swarm run metadata if return_metadata_on is True.

• Usage Example:

  from swarms import Agent, TaskQueueSwarm
  from swarms_models import OpenAIChat
  
  # Initialize the language model
  llm = OpenAIChat()
  
  # Initialize agents
  agent1 = Agent(agent_name="Agent1", llm=llm)
  agent2 = Agent(agent_name="Agent2", llm=llm)
  
  # Create the TaskQueueSwarm
  swarm = TaskQueueSwarm(agents=[agent1, agent2], max_loops=5)
  
  # Add tasks to the swarm
  swarm.add_task("Analyze the latest market trends")
  swarm.add_task("Generate a summary report")
  
  # Run the swarm
  result = swarm.run()
  print(result)  # Prints the swarm run metadata
  

  This example initializes a TaskQueueSwarm with two agents, adds tasks to the queue, and runs the swarm.

save_json_to_file(self)

Saves the swarm run metadata to a JSON file.

export_metadata(self)

Exports the swarm run metadata as a JSON string.

• Returns:
  • str: JSON string of the swarm run metadata.

Additional Notes

• The TaskQueueSwarm uses threading to process tasks concurrently, which can significantly improve performance for I/O-bound tasks.
• The reliability_checks method ensures that the swarm is properly configured before running.
• The swarm automatically handles task distribution among agents and provides detailed metadata about the run.
• Error handling and logging are implemented to track the execution flow and capture any issues during task processing.