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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 | "claude-sonnet-4-6" | 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] | "claude-3-5-sonnet-20240620" | 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""ReflexionAgent""GKPAgent""AgentJudge"
Methods
| Method | Description |
|---|
select_swarm() | Selects and initializes the appropriate reasoning swarm |
run(task, img?, **kwargs) | Executes the selected swarm’s reasoning process |
batched_run(tasks, imgs?, **kwargs) | Executes the reasoning process on a batch of tasks |
Examples
Basic Usage
from swarms.agents.reasoning_agent_router 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-3-5-sonnet-20240620",
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
