ReasoningDuo¶

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 enables more robust and reliable outputs by separating the reasoning process from the final response generation.

Class Overview¶

Constructor 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]	["gpt-4o-mini", "gpt-4o"]	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¶

from swarms.agents.reasoning_duo import ReasoningDuo

# Initialize the ReasoningDuo
duo = ReasoningDuo(
    model_name="reasoning-agent-01",
    model_names=["gpt-4o-mini", "gpt-4o"]
)

# Run a single task
result = duo.run("Explain the concept of gravitational waves")

# Run multiple tasks
tasks = [
    "Calculate compound interest for $1000 over 5 years",
    "Explain quantum entanglement"
]
results = duo.batched_run(tasks)

Examples¶

1. Mathematical Analysis¶

duo = ReasoningDuo()

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

solution = duo.run(math_task)

2. Physics Problem¶

# Quantum mechanics problem
physics_task = """
Calculate the wavelength of an electron with kinetic energy of 50 eV 
using the de Broglie relationship.
"""

result = duo.run(physics_task)

3. Financial Analysis¶

# Complex 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)

Advanced Usage¶

Customizing Agent Behavior¶

You can customize both agents by modifying their initialization parameters:

duo = ReasoningDuo(
    model_name="custom-reasoning-agent",
    description="Specialized financial analysis agent",
    model_names=["gpt-4o-mini", "gpt-4o"],
    system_prompt="You are a financial expert AI assistant..."
)

Batch Processing with Progress Tracking¶

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

# Process multiple tasks with logging
results = duo.batched_run(tasks)

Implementation Details¶

The ReasoningDuo uses a two-stage process:

Reasoning Stage: The reasoning agent analyzes the task and develops a structured approach
Execution Stage: The main agent uses the reasoning output to generate the final response

Internal Architecture¶

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

Best Practices¶

Task Formulation
Be specific and clear in task descriptions
Include relevant context and constraints
Break complex problems into smaller subtasks
Performance Optimization
Use batched_run for multiple related tasks
Monitor agent outputs for consistency
Adjust model parameters based on task complexity

Error Handling¶

The ReasoningDuo includes built-in logging using the loguru library:

from loguru import logger

# Logs are automatically generated for each task
logger.info("Task processing started")

Limitations¶

Processing time may vary based on task complexity
Model response quality depends on input clarity
Resource usage scales with batch size