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`	`"gpt-5.4"`	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`	Stored on the instance. `run()` does not pass it to the aggregator, which always uses the module’s default majority voting prompt
`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
`**kwargs`			Extra `Agent` arguments applied to every sample’s reasoning agent

Methods

Method	Description	Returns
`run(task, img=None, answer=None, args, *kwargs)`	Generates `num_samples` responses concurrently and aggregates them. Extra arguments are forwarded to each sample agent’s `run()`	The conversation formatted by `output_type`, or `None` when evaluation mode rejects the samples
`check_responses_for_answer(responses, answer)`	Checks if `answer` appears as a substring of any response	`bool`
`batched_run(tasks, args, *kwargs)`	Calls `run` on each task in order	`list` of `run` results

aggregation_agent(responses, prompt?, model_name?) is a module-level helper function (not a method on SelfConsistencyAgent) used internally to synthesize the final answer via majority voting. Import it separately as from swarms.agents.consistency_agent import aggregation_agent if you need it directly. run() calls it with its defaults, so the aggregator always runs on "gpt-5.4", whatever model_name you set.

Examples

Basic Usage

from swarms 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 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")
if result is not None:
    print("Validation passed:", result)
else:
    print("Validation failed - expected answer not found")

Random Models for Diversity

from swarms 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 import SelfConsistencyAgent

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

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 the responses concurrently. Each sample gets its own freshly built reasoning Agent, so no sample sees another sample’s answer and the draws stay independent. Responses are collected in the order they finish. When eval=True and you pass answer, run() checks whether answer appears in any response. If none contains it, run() returns None and skips aggregation. Without answer, the check is skipped. run() records the task, the list of responses, and the aggregated answer (as "Majority Voting Agent") in agent.conversation. That conversation is not cleared between calls, so a second run() or batched_run() returns the earlier tasks’ messages too. Create a new SelfConsistencyAgent, or read only the last message, when you need one task’s result on its own.

Output Formats

output_type takes any value listed in output types. Common choices:

"dict": The conversation as a list of message dicts
"final": Only the aggregated answer, as a string
"str": The conversation as a string
"json": The conversation as a JSON string

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
Final Answer Only: Set output_type="final" to get just the aggregated answer
Batch Processing: Use batched_run for multiple related tasks, and remember that results accumulate in one conversation

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