MixtureOfAgents Examples¶

The MixtureOfAgents architecture combines multiple specialized agents with an aggregator agent to process complex tasks. This architecture is particularly effective for tasks requiring diverse expertise and consensus-building among different specialists.

Prerequisites¶

Python 3.7+
OpenAI API key or other supported LLM provider keys
Swarms library

Installation¶

pip3 install -U swarms

Environment Variables¶

WORKSPACE_DIR="agent_workspace"
OPENAI_API_KEY=""
ANTHROPIC_API_KEY=""
GROQ_API_KEY=""

Basic Usage¶

1. Initialize Specialized Agents¶

from swarms import Agent, MixtureOfAgents

# Initialize specialized agents
legal_expert = Agent(
    agent_name="Legal-Expert",
    system_prompt="""You are a legal expert specializing in contract law. Your responsibilities include:
    1. Analyzing legal documents and contracts
    2. Identifying potential legal risks
    3. Ensuring regulatory compliance
    4. Providing legal recommendations
    5. Drafting and reviewing legal documents""",
    model_name="gpt-4o",
    max_loops=1,
)

financial_expert = Agent(
    agent_name="Financial-Expert",
    system_prompt="""You are a financial expert specializing in business finance. Your tasks include:
    1. Analyzing financial implications
    2. Evaluating costs and benefits
    3. Assessing financial risks
    4. Providing financial projections
    5. Recommending financial strategies""",
    model_name="gpt-4o",
    max_loops=1,
)

business_expert = Agent(
    agent_name="Business-Expert",
    system_prompt="""You are a business strategy expert. Your focus areas include:
    1. Analyzing business models
    2. Evaluating market opportunities
    3. Assessing competitive advantages
    4. Providing strategic recommendations
    5. Planning business development""",
    model_name="gpt-4o",
    max_loops=1,
)

# Initialize aggregator agent
aggregator = Agent(
    agent_name="Decision-Aggregator",
    system_prompt="""You are a decision aggregator responsible for:
    1. Synthesizing input from multiple experts
    2. Resolving conflicting viewpoints
    3. Prioritizing recommendations
    4. Providing coherent final decisions
    5. Ensuring comprehensive coverage of all aspects""",
    model_name="gpt-4o",
    max_loops=1,
)

2. Create and Run MixtureOfAgents¶

# Create list of specialist agents
specialists = [legal_expert, financial_expert, business_expert]

# Initialize the mixture of agents
moa = MixtureOfAgents(
    agents=specialists,
    aggregator_agent=aggregator,
    layers=3,
)

# Run the analysis
result = moa.run(
    "Analyze the proposed merger between Company A and Company B, considering legal, financial, and business aspects."
)

Advanced Usage¶

1. Custom Configuration with System Prompts¶

# Initialize MixtureOfAgents with custom aggregator prompt
moa = MixtureOfAgents(
    agents=specialists,
    aggregator_agent=aggregator,
    aggregator_system_prompt="""As the decision aggregator, synthesize the analyses from all specialists into a coherent recommendation:
    1. Summarize key points from each specialist
    2. Identify areas of agreement and disagreement
    3. Weigh different perspectives
    4. Provide a balanced final recommendation
    5. Highlight key risks and opportunities""",
    layers=3,
)

result = moa.run("Evaluate the potential acquisition of StartupX")

2. Error Handling and Validation¶

try:
    moa = MixtureOfAgents(
        agents=specialists,
        aggregator_agent=aggregator,
        layers=3,
        verbose=True,
    )

    result = moa.run("Complex analysis task")

    # Validate and process results
    if result:
        print("Analysis complete:")
        print(result)
    else:
        print("Analysis failed to produce results")

except Exception as e:
    print(f"Error in analysis: {str(e)}")

Best Practices¶

Agent Selection and Configuration:
Choose specialists with complementary expertise
Configure appropriate system prompts
Set suitable model parameters
Aggregator Configuration:
Define clear aggregation criteria
Set appropriate weights for different opinions
Configure conflict resolution strategies
Layer Management:
Set appropriate number of layers
Monitor layer effectiveness
Adjust based on task complexity
Quality Control:
Implement validation checks
Monitor agent performance
Ensure comprehensive coverage

Example Implementation¶

Here's a complete example showing how to use MixtureOfAgents for a comprehensive business analysis:

import os
from swarms import Agent, MixtureOfAgents

# Initialize specialist agents
market_analyst = Agent(
    agent_name="Market-Analyst",
    system_prompt="""You are a market analysis specialist focusing on:
    1. Market size and growth
    2. Competitive landscape
    3. Customer segments
    4. Market trends
    5. Entry barriers""",
    model_name="gpt-4o",
    max_loops=1,
)

financial_analyst = Agent(
    agent_name="Financial-Analyst",
    system_prompt="""You are a financial analysis expert specializing in:
    1. Financial performance
    2. Valuation metrics
    3. Cash flow analysis
    4. Investment requirements
    5. ROI projections""",
    model_name="gpt-4o",
    max_loops=1,
)

risk_analyst = Agent(
    agent_name="Risk-Analyst",
    system_prompt="""You are a risk assessment specialist focusing on:
    1. Market risks
    2. Operational risks
    3. Financial risks
    4. Regulatory risks
    5. Strategic risks""",
    model_name="gpt-4o",
    max_loops=1,
)

# Initialize aggregator
aggregator = Agent(
    agent_name="Strategic-Aggregator",
    system_prompt="""You are a strategic decision aggregator responsible for:
    1. Synthesizing specialist analyses
    2. Identifying key insights
    3. Evaluating trade-offs
    4. Making recommendations
    5. Providing action plans""",
    model_name="gpt-4o",
    max_loops=1,
)

# Create and configure MixtureOfAgents
try:
    moa = MixtureOfAgents(
        agents=[market_analyst, financial_analyst, risk_analyst],
        aggregator_agent=aggregator,
        aggregator_system_prompt="""Synthesize the analyses from all specialists to provide:
        1. Comprehensive situation analysis
        2. Key opportunities and risks
        3. Strategic recommendations
        4. Implementation considerations
        5. Success metrics""",
        layers=3,
        verbose=True,
    )

    # Run the analysis
    result = moa.run(
        """Evaluate the business opportunity for expanding into the electric vehicle market:
        1. Market potential and competition
        2. Financial requirements and projections
        3. Risk assessment and mitigation strategies"""
    )

    # Process and display results
    print("\nComprehensive Analysis Results:")
    print("=" * 50)
    print(result)
    print("=" * 50)

except Exception as e:
    print(f"Error during analysis: {str(e)}")

This comprehensive guide demonstrates how to effectively use the MixtureOfAgents architecture for complex analysis tasks requiring multiple expert perspectives and consensus-building.