### Enterprise Grade Documentation

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## AutoScaler Class from `swarms` Package

The `AutoScaler` class, part of the `swarms` package, provides a dynamic mechanism to handle agents depending on the workload. This document outlines how to use it, complete with import statements and examples.

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### Importing the AutoScaler Class

Before you can use the `AutoScaler` class, you must import it from the `swarms` package:

```python
from swarms import AutoScaler
```

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### Constructor: `AutoScaler.__init__()`

**Description**:  
Initializes the `AutoScaler` with a predefined number of agents and sets up configurations for scaling.

**Parameters**:
- `initial_agents (int)`: Initial number of agents. Default is 10.
- `scale_up_factor (int)`: Multiplicative factor to scale up the number of agents. Default is 2.
- `idle_threshold (float)`: Threshold below which agents are considered idle. Expressed as a ratio (0-1). Default is 0.2.
- `busy_threshold (float)`: Threshold above which agents are considered busy. Expressed as a ratio (0-1). Default is 0.7.

**Returns**:
- None

**Example Usage**:
```python
from swarms import AutoScaler

scaler = AutoScaler(initial_agents=5, scale_up_factor=3, idle_threshold=0.1, busy_threshold=0.8)
```

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### Method: `AutoScaler.add_task(task)`

**Description**:  
Enqueues the specified task into the task queue.

**Parameters**:
- `task`: The task to be added to the queue.

**Returns**:
- None

**Example Usage**:
```python
task_data = "Process dataset X"
scaler.add_task(task_data)
```

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### Method: `AutoScaler.scale_up()`

**Description**:  
Scales up the number of agents based on the specified scale-up factor.

**Parameters**:
- None

**Returns**:
- None

**Example Usage**:
```python
# Called internally but can be manually invoked if necessary
scaler.scale_up()
```

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### Method: `AutoScaler.scale_down()`

**Description**:  
Scales down the number of agents, ensuring a minimum is always present.

**Parameters**:
- None

**Returns**:
- None

**Example Usage**:
```python
# Called internally but can be manually invoked if necessary
scaler.scale_down()
```

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### Method: `AutoScaler.monitor_and_scale()`

**Description**:  
Continuously monitors the task queue and agent utilization to decide on scaling.

**Parameters**:
- None

**Returns**:
- None

**Example Usage**:
```python
# This method is internally used as a thread and does not require manual invocation in most scenarios.
```

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### Method: `AutoScaler.start()`

**Description**:  
Initiates the monitoring process and starts processing tasks from the queue.

**Parameters**:
- None

**Returns**:
- None

**Example Usage**:
```python
scaler.start()
```

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### Full Usage

```python
from swarms import AutoScaler

# Initialize the scaler
auto_scaler = AutoScaler(initial_agents=15, scale_up_factor=2, idle_threshold=0.2, busy_threshold=0.7)

# Start the monitoring and task processing
auto_scaler.start()

# Simulate the addition of tasks
for i in range(100):
    auto_scaler.add_task(f"Task {i}")
```

### Pass in Custom Agent
You can pass any agent class that adheres to the required interface (like having a run() method). If no class is passed, it defaults to using AutoBot. This makes the AutoScaler more flexible and able to handle a wider range of agent implementations.

```python
from swarms import AutoScaler

auto_scaler = AutoScaler(agent=YourCustomAgent)
auto_scaler.start()

for i in range(100):  # Adding tasks
    auto_scaler.add_task(f"Task {i}")

```


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**Notes**:
1. Adjust the thresholds and scaling factors as per your specific requirements and nature of the tasks.
2. The provided implementation is a baseline. Depending on your production environment, you may need additional features, error-handling, and optimizations.
3. Ensure that the `swarms` package and its dependencies are installed in your environment.

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