Aggregate Multi-Agent Responses¶

The aggregate function allows you to run multiple agents concurrently on the same task and then synthesize their responses using an intelligent aggregator agent. This is useful for getting diverse perspectives on a problem and then combining them into a comprehensive analysis.

Installation¶

You can get started by first installing swarms with the following command, or click here for more detailed installation instructions:

pip3 install -U swarms

Environment Variables¶

WORKSPACE_DIR=""
OPENAI_API_KEY=""
ANTHROPIC_API_KEY=""

How It Works¶

Concurrent Execution: All agents in the workers list run the same task simultaneously
Response Collection: Individual agent responses are collected into a conversation
Intelligent Aggregation: A specialized aggregator agent analyzes all responses and creates a comprehensive synthesis
Formatted Output: The final result is returned in the specified format

Code Example¶

from swarms.structs.agent import Agent
from swarms.structs.ma_blocks import aggregate


# Create specialized agents for different perspectives
agents = [
    Agent(
        agent_name="Sector-Financial-Analyst",
        agent_description="Senior financial analyst at BlackRock.",
        system_prompt="You are a financial analyst tasked with optimizing asset allocations for a $50B portfolio. Provide clear, quantitative recommendations for each sector.",
        max_loops=1,
        model_name="gpt-4o-mini",
        max_tokens=3000,
    ),
    Agent(
        agent_name="Sector-Risk-Analyst",
        agent_description="Expert risk management analyst.",
        system_prompt="You are a risk analyst responsible for advising on risk allocation within a $50B portfolio. Provide detailed insights on risk exposures for each sector.",
        max_loops=1,
        model_name="gpt-4o-mini",
        max_tokens=3000,
    ),
    Agent(
        agent_name="Tech-Sector-Analyst",
        agent_description="Technology sector analyst.",
        system_prompt="You are a tech sector analyst focused on capital and risk allocations. Provide data-backed insights for the tech sector.",
        max_loops=1,
        model_name="gpt-4o-mini",
        max_tokens=3000,
    ),
]

# Run the aggregate function
result = aggregate(
    workers=agents,
    task="What is the best sector to invest in?",
    type="all",  # Get complete conversation history
    aggregator_model_name="anthropic/claude-3-sonnet-20240229"
)

print(result)