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# 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.
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