Self-Consistency Agent

The SelfConsistencyAgent generates multiple independent responses to a given task and aggregates them into a single, consistent final answer. It leverages concurrent processing and employs a majority voting mechanism to ensure reliability. Based on the research paper: Self-Consistency Improves Chain of Thought Reasoning in Language Models (Wang et al., 2022).

Class: `SelfConsistencyAgent`

Parameters

Parameter	Type	Default	Description
`name`	`str`	`"Self-Consistency-Agent"`	Name of the agent
`description`	`str`	`"An agent that uses self consistency..."`	Description of the agent’s purpose
`system_prompt`	`str`	`CONSISTENCY_SYSTEM_PROMPT`	System prompt for the reasoning agent
`model_name`	`str`	Required	The underlying language model to use
`num_samples`	`int`	`5`	Number of independent responses to generate
`max_loops`	`int`	`1`	Maximum number of reasoning loops per sample
`majority_voting_prompt`	`Optional[str]`	`majority_voting_prompt`	Custom prompt for majority voting aggregation
`eval`	`bool`	`False`	Enable evaluation mode for answer validation
`output_type`	`OutputType`	`"dict"`	Format of the output
`random_models_on`	`bool`	`False`	Enable random model selection for diversity

Methods

Method	Description	Returns
`run(task, img?, answer?)`	Generates multiple responses and aggregates them	`Union[str, Dict[str, Any]]`
`aggregation_agent(responses, prompt?, model_name?)`	Aggregates responses via majority voting	`str`
`check_responses_for_answer(responses, answer)`	Checks if an answer appears in any response	`bool`
`batched_run(tasks)`	Run the agent on multiple tasks in batch	`List[Union[str, Dict[str, Any]]]`

Examples

Basic Usage

from swarms.agents.consistency_agent import SelfConsistencyAgent

agent = SelfConsistencyAgent(
    name="Math-Reasoning-Agent",
    model_name="claude-sonnet-4-6",
    max_loops=1,
    num_samples=5
)

task = "What is the 40th prime number?"
final_answer = agent.run(task)
print("Final aggregated answer:", final_answer)

Evaluation Mode

from swarms.agents.consistency_agent import SelfConsistencyAgent

agent = SelfConsistencyAgent(
    name="Validation-Agent",
    model_name="claude-sonnet-4-6",
    num_samples=3,
    eval=True
)

result = agent.run("What is 2 + 2?", answer="4", eval=True)
if result is not None:
    print("Validation passed:", result)
else:
    print("Validation failed - expected answer not found")

Random Models for Diversity

from swarms.agents.consistency_agent import SelfConsistencyAgent

agent = SelfConsistencyAgent(
    name="Diverse-Reasoning-Agent",
    model_name="claude-sonnet-4-6",
    num_samples=5,
    random_models_on=True
)

result = agent.run("What are the benefits of renewable energy?")
print("Diverse reasoning result:", result)

Batch Processing

from swarms.agents.consistency_agent import SelfConsistencyAgent

agent = SelfConsistencyAgent(
    name="Batch-Processing-Agent",
    model_name="claude-sonnet-4-6",
    num_samples=3
)

tasks = [
    "What is the capital of France?",
    "What is 15 * 23?",
    "Explain photosynthesis in simple terms."
]

results = agent.batched_run(tasks)
for i, result in enumerate(results):
    print(f"Task {i+1} result: {result}")

How It Works

Generates Multiple Independent Responses: Creates several reasoning paths for the same problem
Analyzes Consistency: Examines agreement among different reasoning approaches
Aggregates Results: Uses majority voting or consensus building
Produces Reliable Output: Delivers a final answer reflecting the most reliable consensus

The agent uses ThreadPoolExecutor to generate multiple responses concurrently, improving performance while maintaining independence between reasoning paths.

Output Formats

"dict": Dictionary format with conversation history
"str": Simple string output
"list": List format
"json": JSON formatted output

Best Practices

Sample Size: Use 3-7 samples for most tasks; increase for critical decisions
Model Selection: Choose models with strong reasoning capabilities
Evaluation Mode: Enable for tasks with known correct answers
Custom Prompts: Tailor majority voting prompts for specific domains
Batch Processing: Use batched_run for multiple related tasks

Getting Started

Core Concepts

Agent Development

Reasoning Agents

Multi-Agent Architectures

Deployment & Operations

Integrations

CLI Reference

Changelog

Self-Consistency Agent

Class: `SelfConsistencyAgent`

Parameters

Methods

Examples

Basic Usage

Evaluation Mode

Random Models for Diversity

Batch Processing

How It Works

Output Formats

Best Practices

​Class: SelfConsistencyAgent

​Parameters

​Methods

​Examples

​Basic Usage

​Evaluation Mode

​Random Models for Diversity

​Batch Processing

​How It Works

​Output Formats

​Best Practices

Class: `SelfConsistencyAgent`

Parameters

Methods

Examples

Basic Usage

Evaluation Mode

Random Models for Diversity

Batch Processing

How It Works

Output Formats

Best Practices