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HeavySwarm Documentation

HeavySwarm is a sophisticated multi-agent orchestration system that decomposes complex tasks into specialized questions and executes them using four specialized agents: Research, Analysis, Alternatives, and Verification. The results are then synthesized into a comprehensive response.

Inspired by X.AI's Grok 4 heavy implementation, HeavySwarm provides robust task analysis through intelligent question generation, parallel execution, and comprehensive synthesis with real-time progress monitoring.

Architecture

System Design

The HeavySwarm follows a structured 5-phase workflow:

  1. Task Decomposition: Complex tasks are broken down into specialized questions
  2. Question Generation: AI-powered generation of role-specific questions
  3. Parallel Execution: Four specialized agents work concurrently
  4. Result Collection: Outputs are gathered and validated
  5. Synthesis: Integration into a comprehensive final response

Agent Specialization

  • Research Agent: Comprehensive information gathering and synthesis
  • Analysis Agent: Pattern recognition and statistical analysis
  • Alternatives Agent: Creative problem-solving and strategic options
  • Verification Agent: Validation, feasibility assessment, and quality assurance
  • Synthesis Agent: Multi-perspective integration and executive reporting

Architecture Diagram

graph TB
    subgraph "HeavySwarm Architecture"
        A[Input Task] --> B[Question Generation Agent]
        B --> C[Task Decomposition]

        C --> D[Research Agent]
        C --> E[Analysis Agent]
        C --> F[Alternatives Agent]
        C --> G[Verification Agent]

        D --> H[Parallel Execution Engine]
        E --> H
        F --> H
        G --> H

        H --> I[Result Collection]
        I --> J[Synthesis Agent]
        J --> K[Comprehensive Report]

        subgraph "Monitoring & Control"
            L[Rich Dashboard]
            M[Progress Tracking]
            N[Error Handling]
            O[Timeout Management]
        end

        H --> L
        H --> M
        H --> N
        H --> O
    end

    subgraph "Agent Specializations"
        D --> D1[Information Gathering<br/>Market Research<br/>Data Collection]
        E --> E1[Statistical Analysis<br/>Pattern Recognition<br/>Predictive Modeling]
        F --> F1[Creative Solutions<br/>Strategic Options<br/>Innovation Ideation]
        G --> G1[Fact Checking<br/>Feasibility Assessment<br/>Quality Assurance]
    end

    style A fill:#ff6b6b
    style K fill:#4ecdc4
    style H fill:#45b7d1
    style J fill:#96ceb4

Installation

pip install swarms

Quick Start

from swarms import HeavySwarm

# Initialize the swarm
swarm = HeavySwarm(
    name="MarketAnalysisSwarm",
    description="Financial market analysis swarm",
    question_agent_model_name="gpt-4o-mini",
    worker_model_name="gpt-4o-mini",
    show_dashboard=True,
    verbose=True
)

# Execute analysis
result = swarm.run("Analyze the current cryptocurrency market trends and investment opportunities")
print(result)

API Reference

HeavySwarm Class

Constructor Parameters

Parameter Type Default Description
name str "HeavySwarm" Identifier name for the swarm instance
description str "A swarm of agents..." Description of the swarm's purpose
agents List[Agent] None Pre-configured agent list (unused - agents created internally)
timeout int 300 Maximum execution time per agent in seconds
aggregation_strategy str "synthesis" Strategy for result aggregation
loops_per_agent int 1 Number of execution loops per agent
question_agent_model_name str "gpt-4o-mini" Model for question generation
worker_model_name str "gpt-4o-mini" Model for specialized worker agents
verbose bool False Enable detailed logging output
max_workers int int(os.cpu_count() * 0.9) Maximum concurrent workers
show_dashboard bool False Enable rich dashboard visualization
agent_prints_on bool False Enable individual agent output printing

Methods

run(task: str, img: str = None) -> str

Execute the complete HeavySwarm orchestration flow.

Parameters:

  • task (str): The main task to analyze and decompose

  • img (str, optional): Image input for visual analysis tasks

Returns: - str: Comprehensive final analysis from synthesis agent

Example: 

result = swarm.run("Develop a go-to-market strategy for a new SaaS product")

Real-World Applications

Financial Services

# Market Analysis
swarm = HeavySwarm(
    name="FinanceSwarm",
    worker_model_name="gpt-4o",
    show_dashboard=True
)

result = swarm.run("""
Analyze the impact of recent Federal Reserve policy changes on:
1. Bond markets and yield curves
2. Equity market valuations
3. Currency exchange rates
4. Provide investment recommendations for institutional portfolios
""")

Use-cases

Use Case Description
Portfolio optimization and risk assessment Optimize asset allocation and assess risks
Market trend analysis and forecasting Analyze and predict market movements
Regulatory compliance evaluation Evaluate adherence to financial regulations
Investment strategy development Develop and refine investment strategies
Credit risk analysis and modeling Analyze and model credit risk

Healthcare & Life Sciences

# Clinical Research Analysis
swarm = HeavySwarm(
    name="HealthcareSwarm",
    worker_model_name="gpt-4o",
    timeout=600,
    loops_per_agent=2
)

result = swarm.run("""
Evaluate the potential of AI-driven personalized medicine:
1. Current technological capabilities and limitations
2. Regulatory landscape and approval pathways
3. Market opportunities and competitive analysis
4. Implementation strategies for healthcare systems
""")

Use Cases:

Use Case Description
Drug discovery and development analysis Analyze and accelerate drug R&D processes
Clinical trial optimization Improve design and efficiency of trials
Healthcare policy evaluation Assess and inform healthcare policies
Medical device market analysis Evaluate trends and opportunities in devices
Patient outcome prediction modeling Predict and model patient health outcomes

Technology & Innovation

# Tech Strategy Analysis
swarm = HeavySwarm(
    name="TechSwarm",
    worker_model_name="gpt-4o",
    show_dashboard=True,
    verbose=True
)

result = swarm.run("""
Assess the strategic implications of quantum computing adoption:
1. Technical readiness and hardware developments
2. Industry applications and use cases
3. Competitive landscape and key players
4. Investment and implementation roadmap
""")

Use Cases:

Use Case Description
Technology roadmap development Plan and prioritize technology initiatives
Competitive intelligence gathering Analyze competitors and market trends
Innovation pipeline analysis Evaluate and manage innovation projects
Digital transformation strategy Develop and implement digital strategies
Emerging technology assessment Assess new and disruptive technologies

Manufacturing & Supply Chain

# Supply Chain Optimization
swarm = HeavySwarm(
    name="ManufacturingSwarm",
    worker_model_name="gpt-4o",
    max_workers=8
)

result = swarm.run("""
Optimize global supply chain resilience:
1. Risk assessment and vulnerability analysis
2. Alternative sourcing strategies
3. Technology integration opportunities
4. Cost-benefit analysis of proposed changes
""")

Use Cases:

Use Case Description
Supply chain risk management Identify and mitigate supply chain risks
Manufacturing process optimization Improve efficiency and productivity
Quality control system design Develop systems to ensure product quality
Sustainability impact assessment Evaluate environmental and social impacts
Logistics network optimization Enhance logistics and distribution networks

Advanced Configuration

Custom Agent Configuration

# High-performance configuration
swarm = HeavySwarm(
    name="HighPerformanceSwarm",
    question_agent_model_name="gpt-4o",
    worker_model_name="gpt-4o",
    timeout=900,
    loops_per_agent=3,
    max_workers=12,
    show_dashboard=True,
    verbose=True
)

Troubleshooting

Issue Solution
Agent Timeout Increase timeout parameter or reduce task complexity
Model Rate Limits Implement backoff strategies or use different models
Memory Usage Monitor system resources with large-scale operations
Dashboard Performance Disable dashboard for batch processing

Contributing

HeavySwarm is part of the Swarms ecosystem. Contributions are welcome for:

  • New agent specializations

  • Performance optimizations

  • Integration capabilities

  • Documentation improvements

Acknowledgments

  • Inspired by X.AI's Grok heavy implementation

  • Built on the Swarms framework

  • Utilizes Rich for dashboard visualization

  • Powered by advanced language models