> ## Documentation Index
> Fetch the complete documentation index at: https://docs.swarms.world/llms.txt
> Use this file to discover all available pages before exploring further.

# Reasoning Duo

> Dual-agent collaborative system that separates reasoning and execution phases for more robust and reliable outputs

The `ReasoningDuo` class implements a dual-agent reasoning system that combines a reasoning agent and a main agent to provide well-thought-out responses to complex tasks. This architecture separates the reasoning process from the final response generation.

## Architecture

```
Task Input → Reasoning Agent → Structured Analysis → Main Agent → Final Output
```

## Parameters

| Parameter       | Type        | Default                            | Description                                       |
| --------------- | ----------- | ---------------------------------- | ------------------------------------------------- |
| `model_name`    | `str`       | `"reasoning-agent-01"`             | Name identifier for the reasoning agent           |
| `description`   | `str`       | `"A highly intelligent..."`        | Description of the reasoning agent's capabilities |
| `model_names`   | `list[str]` | `["claude-sonnet-4-6", "gpt-5.4"]` | Model names for reasoning and main agents         |
| `system_prompt` | `str`       | `"You are a helpful..."`           | System prompt for the main agent                  |

### Methods

| Method        | Parameters         | Returns     | Description                                 |
| ------------- | ------------------ | ----------- | ------------------------------------------- |
| `run`         | `task: str`        | `str`       | Processes a single task through both agents |
| `batched_run` | `tasks: List[str]` | `List[str]` | Processes multiple tasks sequentially       |

## Quick Start

```python theme={null}
from swarms.agents.reasoning_duo import ReasoningDuo

duo = ReasoningDuo(
    model_name="reasoning-agent-01",
    model_names=["claude-sonnet-4-6", "gpt-5.4"]
)

result = duo.run("Explain the concept of gravitational waves")
```

## Examples

### Mathematical Analysis

```python theme={null}
duo = ReasoningDuo()

math_task = """
Solve the following differential equation:
dy/dx + 2y = x^2, y(0) = 1
"""

solution = duo.run(math_task)
```

### Financial Analysis

```python theme={null}
finance_task = """
Calculate the Net Present Value (NPV) of a project with:
- Initial investment: $100,000
- Annual cash flows: $25,000 for 5 years
- Discount rate: 8%
"""

analysis = duo.run(finance_task)
```

### Customizing Agent Behavior

```python theme={null}
duo = ReasoningDuo(
    model_name="custom-reasoning-agent",
    description="Specialized financial analysis agent",
    model_names=["claude-sonnet-4-6", "gpt-5.4"],
    system_prompt="You are a financial expert AI assistant..."
)
```

### Batch Processing

```python theme={null}
tasks = [
    "Analyze market trends for tech stocks",
    "Calculate risk metrics for a portfolio",
    "Forecast revenue growth"
]

results = duo.batched_run(tasks)
```

## Best Practices

1. **Task Formulation**: Be specific and clear in task descriptions. Include relevant context and constraints.
2. **Performance Optimization**: Use `batched_run` for multiple related tasks. Monitor agent outputs for consistency.
3. **Model Selection**: Adjust model parameters based on task complexity.