Reasoning Agent Router

The ReasoningAgentRouter enables dynamic selection and execution of different reasoning strategies based on task requirements. It provides a flexible interface to work with multiple reasoning approaches.

Architecture

Parameters

Parameter	Type	Default	Description
`agent_name`	`str`	`"reasoning_agent"`	Name identifier for the agent
`description`	`str`	`"A reasoning agent..."`	Description of the agent’s capabilities
`model_name`	`str`	`"gpt-5.4"`	The underlying language model to use
`system_prompt`	`str`	`"You are a helpful..."`	System prompt for the agent
`max_loops`	`int`	`1`	Maximum number of reasoning loops
`swarm_type`	`agent_types`	`"reasoning-duo"`	Type of reasoning swarm to use
`num_samples`	`int`	`1`	Number of samples for self-consistency
`output_type`	`OutputType`	`"dict-all-except-first"`	Format of the output
`num_knowledge_items`	`int`	`6`	Number of knowledge items for GKP agent
`memory_capacity`	`int`	`6`	Memory capacity for agents that support it
`eval`	`bool`	`False`	Enable evaluation mode for self-consistency
`random_models_on`	`bool`	`False`	Enable random model selection for diversity
`majority_voting_prompt`	`Optional[str]`	`None`	Custom prompt for majority voting
`reasoning_model_name`	`Optional[str]`	`"gpt-4o"`	Model for reasoning in ReasoningDuo

Available Agent Types

The following values are supported for the swarm_type parameter:

"reasoning-duo" or "reasoning-agent"
"self-consistency" or "consistency-agent"
"ire" or "ire-agent" — _create_ire_agent passes max_loops=self.num_samples when building the underlying agent, so the router’s own max_loops is ignored for this type; num_samples controls the number of IRE iterations instead.
"ReflexionAgent" — run() returns the raw list produced by ReflexionAgent.run() directly; it bypasses history_output_formatter, so output_type has no effect for this swarm type (every other type is formatted according to output_type).
"GKPAgent"
"AgentJudge"

Methods

Method	Description
`select_swarm()`	Selects and initializes the appropriate reasoning swarm
`run(task, args, *kwargs)`	Executes the selected swarm’s reasoning process
`batched_run(tasks, args, *kwargs)`	Executes the reasoning process on a batch of tasks (each task processed via `run`)

Examples

Basic Usage

from swarms import ReasoningAgentRouter

router = ReasoningAgentRouter(
    agent_name="reasoning-agent",
    model_name="claude-sonnet-4-6",
    swarm_type="self-consistency",
    num_samples=3,
)

result = router.run("What is the best approach to solve this problem?")

Self-Consistency with Evaluation

router = ReasoningAgentRouter(
    swarm_type="self-consistency",
    num_samples=5,
    model_name="claude-sonnet-4-6",
    eval=True,
    random_models_on=True
)

result = router.run("What is 2 + 2?")

ReasoningDuo with Image Support

router = ReasoningAgentRouter(
    swarm_type="reasoning-duo",
    model_name="claude-sonnet-4-6",
    reasoning_model_name="claude-sonnet-4-6",
    max_loops=2
)

result = router.run(
    "Analyze this image and explain the patterns you see",
    img="data_visualization.png"
)

GKP Agent

router = ReasoningAgentRouter(
    swarm_type="GKPAgent",
    model_name="claude-sonnet-4-6",
    num_knowledge_items=6
)

result = router.run("What are the implications of quantum entanglement?")

Reflexion Agent

router = ReasoningAgentRouter(
    swarm_type="ReflexionAgent",
    max_loops=3,
    model_name="claude-sonnet-4-6"
)

result = router.run("Explain quantum computing to a beginner.")

Choosing the Right Type

Scenario	Recommended Type	Why
Tasks requiring high reliability	`self-consistency`	Multiple validation paths, consensus building
Complex tasks needing analysis + action	`reasoning-duo`	Separates reasoning from execution
Problems requiring iterative refinement	`ire`	Designed for progressive improvement
Tasks requiring introspection	`ReflexionAgent`	Self-reflection and learning from experience
Knowledge-intensive tasks	`GKPAgent`	Generates knowledge before reasoning
Quality control	`AgentJudge`	Specialized evaluation capabilities

Best Practices

Swarm Type Selection: Match the reasoning strategy to your task requirements
Performance: Adjust max_loops and num_samples based on task complexity
Self-Consistency: Use 3-5 samples for most tasks, 7+ for critical decisions
Multi-modal: Use vision-capable models when processing images
ReasoningDuo: Set different models for reasoning vs execution via reasoning_model_name

Getting Started

Core Concepts

Agent Development

Reasoning Agents

Multi-Agent Architectures

Deployment & Operations

Integrations

CLI Reference

Changelog

Reasoning Agent Router

Architecture

Parameters

Available Agent Types

Methods

Examples

Basic Usage

Self-Consistency with Evaluation

ReasoningDuo with Image Support

GKP Agent

Reflexion Agent

Choosing the Right Type

Best Practices

​Architecture

​Parameters

​Available Agent Types

​Methods

​Examples

​Basic Usage

​Self-Consistency with Evaluation

​ReasoningDuo with Image Support

​GKP Agent

​Reflexion Agent

​Choosing the Right Type

​Best Practices

Architecture

Parameters

Available Agent Types

Methods

Examples

Basic Usage

Self-Consistency with Evaluation

ReasoningDuo with Image Support

GKP Agent

Reflexion Agent

Choosing the Right Type

Best Practices