MajorityVoting Module Documentation

The MajorityVoting module provides a mechanism for performing majority voting among a group of agents. Majority voting is a decision rule that selects the option which has the majority of votes. This is particularly useful in systems where multiple agents provide responses to a query, and the most common response needs to be identified as the final output.

Architecture

graph TD
    A[MajorityVoting System] --> B[Initialize Agents]
    B --> C[Process Task]
    C --> D{Execution Mode}
    D --> E[Single Task]
    D --> F[Batch Tasks]
    D --> G[Concurrent Tasks]
    D --> H[Async Tasks]
    E --> I[Run Agents]
    F --> I
    G --> I
    H --> I
    I --> J[Collect Responses]
    J --> K[Consensus Analysis]
    K --> L{Consensus Agent?}
    L -->|Yes| M[Use Consensus Agent]
    L -->|No| N[Use Last Agent]
    M --> O[Final Output]
    N --> O
    O --> P[Save Conversation]

Key Concepts

• Majority Voting: A method to determine the most common response from a set of answers.
• Agents: Entities (e.g., models, algorithms) that provide responses to tasks or queries.
• Output Parser: A function that processes the responses from the agents before performing the majority voting.
• Consensus Agent: An optional agent that analyzes the responses from all agents to determine the final consensus.
• Conversation History: A record of all agent interactions and responses during the voting process.

Class Definition: MajorityVoting

Parameters

Parameter	Type	Description
name	str	Name of the majority voting system. Default is "MajorityVoting".
description	str	Description of the system. Default is "A majority voting system for agents".
agents	List[Agent]	A list of agents to be used in the majority voting system.
output_parser	Callable	Function to parse agent outputs. Default is majority_voting function.
consensus_agent	Agent	Optional agent for analyzing consensus among responses.
autosave	bool	Whether to autosave conversations. Default is False.
verbose	bool	Whether to enable verbose logging. Default is False.
max_loops	int	Maximum number of voting loops. Default is 1.

Methods

run(task: str, correct_answer: str, *args, **kwargs) -> List[Any]

Runs the majority voting system for a single task.

Parameters: - task (str): The task to be performed by the agents - correct_answer (str): The correct answer for evaluation - *args, **kwargs: Additional arguments

Returns: - List[Any]: The conversation history as a string, including the majority vote

batch_run(tasks: List[str], *args, **kwargs) -> List[Any]

Runs multiple tasks in sequence.

Parameters: - tasks (List[str]): List of tasks to be performed - *args, **kwargs: Additional arguments

Returns: - List[Any]: List of majority votes for each task

run_concurrently(tasks: List[str], *args, **kwargs) -> List[Any]

Runs multiple tasks concurrently using thread pooling.

Parameters: - tasks (List[str]): List of tasks to be performed - *args, **kwargs: Additional arguments

Returns: - List[Any]: List of majority votes for each task

run_async(tasks: List[str], *args, **kwargs) -> List[Any]

Runs multiple tasks asynchronously using asyncio.

Parameters: - tasks (List[str]): List of tasks to be performed - *args, **kwargs: Additional arguments

Returns: - List[Any]: List of majority votes for each task

Usage Examples

Example 1: Basic Single Task Execution with Modern LLMs

from swarms import Agent, MajorityVoting

# Initialize multiple agents with different specialties
agents = [
    Agent(
        agent_name="Financial-Analysis-Agent",
        agent_description="Personal finance advisor focused on market analysis",
        system_prompt="You are a financial advisor specializing in market analysis and investment opportunities.",
        max_loops=1,
        model_name="gpt-4o"
    ),
    Agent(
        agent_name="Risk-Assessment-Agent", 
        agent_description="Risk analysis and portfolio management expert",
        system_prompt="You are a risk assessment expert focused on evaluating investment risks and portfolio diversification.",
        max_loops=1,
        model_name="gpt-4o"
    ),
    Agent(
        agent_name="Tech-Investment-Agent",
        agent_description="Technology sector investment specialist",
        system_prompt="You are a technology investment specialist focused on AI, emerging tech, and growth opportunities.",
        max_loops=1,
        model_name="gpt-4o"
    )
]


consensus_agent = Agent(
    agent_name="Consensus-Agent",
    agent_description="Consensus agent focused on analyzing investment advice",
    system_prompt="You are a consensus agent focused on analyzing investment advice and providing a final answer.",
    max_loops=1,
    model_name="gpt-4o"
)

# Create majority voting system
majority_voting = MajorityVoting(
    name="Investment-Advisory-System",
    description="Multi-agent system for investment advice",
    agents=agents,
    verbose=True,
    consensus_agent=consensus_agent
)

# Run the analysis with majority voting
result = majority_voting.run(
    task="Create a table of super high growth opportunities for AI. I have $40k to invest in ETFs, index funds, and more. Please create a table in markdown.",
    correct_answer=""  # Optional evaluation metric
)

print(result)

Batch Execution

from swarms import Agent, MajorityVoting

# Initialize multiple agents with different specialties
agents = [
    Agent(
        agent_name="Financial-Analysis-Agent",
        agent_description="Personal finance advisor focused on market analysis",
        system_prompt="You are a financial advisor specializing in market analysis and investment opportunities.",
        max_loops=1,
        model_name="gpt-4o"
    ),
    Agent(
        agent_name="Risk-Assessment-Agent", 
        agent_description="Risk analysis and portfolio management expert",
        system_prompt="You are a risk assessment expert focused on evaluating investment risks and portfolio diversification.",
        max_loops=1,
        model_name="gpt-4o"
    ),
    Agent(
        agent_name="Tech-Investment-Agent",
        agent_description="Technology sector investment specialist",
        system_prompt="You are a technology investment specialist focused on AI, emerging tech, and growth opportunities.",
        max_loops=1,
        model_name="gpt-4o"
    )
]


consensus_agent = Agent(
    agent_name="Consensus-Agent",
    agent_description="Consensus agent focused on analyzing investment advice",
    system_prompt="You are a consensus agent focused on analyzing investment advice and providing a final answer.",
    max_loops=1,
    model_name="gpt-4o"
)

# Create majority voting system
majority_voting = MajorityVoting(
    name="Investment-Advisory-System",
    description="Multi-agent system for investment advice",
    agents=agents,
    verbose=True,
    consensus_agent=consensus_agent
)

# Run the analysis with majority voting
result = majority_voting.batch_run(
    task="Create a table of super high growth opportunities for AI. I have $40k to invest in ETFs, index funds, and more. Please create a table in markdown.",
    correct_answer=""  # Optional evaluation metric
)

print(result)