Reasoning Duo

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
`id`	`str`	random `uuid4()`	Unique identifier for the duo
`agent_name`	`str`	`"reasoning-agent-01"`	Name identifier shared by both internal agents
`agent_description`	`str`	`"A highly intelligent..."`	Description passed to both internal agents
`model_name`	`str`	`"gpt-5.4"`	Stored on the instance; not currently used to configure either internal agent
`description`	`str`	`"A highly intelligent..."`	Description of the reasoning duo’s capabilities
`model_names`	`list[str]`	`["gpt-5.4", "gpt-5.4"]`	Model names for `[reasoning_agent, main_agent]` (index 1 configures the main agent)
`system_prompt`	`str`	`"You are a helpful..."`	System prompt for the main agent
`output_type`	`OutputType`	`"dict-all-except-first"`	Format of the value returned by `run()`
`reasoning_model_name`	`Optional[str]`	`"gpt-4o"`	Model used by the reasoning agent; if `None`, falls back to `model_names[0]`
`max_loops`	`int`	`1`	Number of reasoning/main-agent loop iterations in `run()`

Methods

Method	Parameters	Returns	Description
`step`	`task: str, img: Optional[str] = None`	`None`	Runs one reasoning-agent → main-agent cycle and appends to the conversation
`run`	`task: str, img: Optional[str] = None`	Formatted per `output_type` (e.g. `dict` by default)	Processes a task through both agents for `max_loops` iterations
`batched_run`	`tasks: List[str], imgs: Optional[List[str]] = None`	`list`	Processes multiple tasks sequentially

Quick Start

from swarms import ReasoningDuo

duo = ReasoningDuo(
    agent_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

duo = ReasoningDuo()

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

solution = duo.run(math_task)

Financial Analysis

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

duo = ReasoningDuo(
    agent_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

tasks = [
    "Analyze market trends for tech stocks",
    "Calculate risk metrics for a portfolio",
    "Forecast revenue growth"
]

results = duo.batched_run(tasks)

Best Practices

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

Getting Started

Core Concepts

Agent Development

Reasoning Agents

Multi-Agent Architectures

Deployment & Operations

Integrations

CLI Reference

Changelog

Architecture

Parameters

Methods

Quick Start

Examples

Mathematical Analysis

Financial Analysis

Customizing Agent Behavior

Batch Processing

Best Practices

​Architecture

​Parameters

​Methods

​Quick Start

​Examples

​Mathematical Analysis

​Financial Analysis

​Customizing Agent Behavior

​Batch Processing

​Best Practices

Architecture

Parameters

Methods

Quick Start

Examples

Mathematical Analysis

Financial Analysis

Customizing Agent Behavior

Batch Processing

Best Practices