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:**
  ```python
  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.