🐝 Swarms

Coordinate multiple AI agents for complex tasks

Swarm Strategies

⚡
Parallel
All agents run simultaneously
🔗
Sequential
Agents run one after another
🏆
Competitive
Best result wins

⚡ Parallel Strategy

All agents execute simultaneously for maximum speed:

🤖 Agent 1
🤖 Agent 2
🤖 Agent 3
⬇️ All run at once ⬇️
📊 Combined Results 
swarm DataProcessors {
    name: "Data Processing Swarm"
    agents: ["Parser", "Validator", "Transformer", "Loader"]
    strategy: parallel
}

agent Parser {
    task parse(data) {
        return "Parsed: " + data
    }
}

agent Validator {
    task validate(data) {
        return "Validated: " + data
    }
}

agent Transformer {
    task transform(data) {
        return "Transformed: " + data
    }
}

agent Loader {
    task load(data) {
        return "Loaded: " + data
    }
}

// All agents run in parallel!
let results = DataProcessors.execute({
    data: "raw_data",
    operation: "process"
})

print(results)
// {
//   Parser: "Parsed: raw_data",
//   Validator: "Validated: raw_data",
//   Transformer: "Transformed: raw_data",
//   Loader: "Loaded: raw_data"
// }

⚡ 4x Faster

Parallel execution completes in the time of the slowest agent, not the sum of all agents!

🔗 Sequential Strategy

Agents execute in order, each using the previous result:

🤖 Agent 1
Collects data
↓
🤖 Agent 2
Processes data
↓
🤖 Agent 3
Analyzes results
↓
📊 Final Report
swarm DataPipeline {
    name: "Data Analysis Pipeline"
    agents: ["Collector", "Processor", "Analyzer", "Reporter"]
    strategy: sequential
}

agent Collector {
    task collect(source) {
        return "Data from: " + source
    }
}

agent Processor {
    task process(data) {
        return "Processed: " + data
    }
}

agent Analyzer {
    task analyze(data) {
        return "Analysis: " + data
    }
}

agent Reporter {
    task report(analysis) {
        return "Report: " + analysis
    }
}

// Sequential execution: Collector → Processor → Analyzer → Reporter
let final_report = DataPipeline.execute({
    source: "database"
})

print(final_report)
// "Report: Analysis: Processed: Data from: database"

🔗 Pipeline Pattern

Perfect for data pipelines where each step depends on the previous one.

🏆 Competitive Strategy

Multiple agents compete, best result wins:

🤖 GPT-4
Score: 95
🤖 Claude
Score: 92
🤖 Llama
Score: 88
⬇️ Best wins ⬇️
🏆 GPT-4 Result Selected 
swarm AnswerCompetition {
    name: "Answer Competition"
    agents: ["GPT4Agent", "ClaudeAgent", "LlamaAgent"]
    strategy: competitive
    
    // Selection criteria
    selection: {
        method: "quality",  // or "speed", "cost"
        evaluator: "JudgeAgent"
    }
}

agent GPT4Agent {
    provider: "openai"
    model: "gpt-4"
    
    task answer(question) {
        return this.call(question)
    }
}

agent ClaudeAgent {
    provider: "anthropic"
    model: "claude-3-sonnet"
    
    task answer(question) {
        return this.call(question)
    }
}

agent LlamaAgent {
    provider: "ollama"
    model: "llama2"
    
    task answer(question) {
        return this.call(question)
    }
}

// All three agents answer, best one is selected
let best_answer = AnswerCompetition.execute({
    question: "What is artificial intelligence?"
})

print(best_answer)

🎯 Best Quality Guaranteed

Get the best possible answer by running multiple models and selecting the winner!

Real-World Example: E-Commerce

// Order processing pipeline (sequential)
swarm OrderPipeline {
    name: "Order Processing"
    agents: ["OrderValidator", "PaymentProcessor", "InventoryManager", "ShippingAgent"]
    strategy: sequential
}

// Product recommendation (parallel)
swarm RecommendationEngine {
    name: "Product Recommendations"
    agents: ["CollaborativeFilter", "ContentBased", "TrendingAnalyzer"]
    strategy: parallel
}

// Customer support (competitive)
swarm SupportTeam {
    name: "Customer Support"
    agents: ["GPT4Support", "ClaudeSupport"]
    strategy: competitive
    selection: {
        method: "quality"
    }
}

// Usage
async function process_order(order) {
    // Sequential: validate → pay → inventory → ship
    let result = await OrderPipeline.execute(order)
    
    // Parallel: get all recommendations at once
    let recommendations = await RecommendationEngine.execute({
        user_id: order.user_id
    })
    
    // Competitive: best support response
    let support = await SupportTeam.execute({
        question: "Order status?"
    })
    
    return { result, recommendations, support }
}

Strategy Comparison

Strategy Speed Use Case Cost
⚡ Parallel Fast Independent tasks Medium
🔗 Sequential Medium Dependent tasks Low
🏆 Competitive Medium Best quality needed High
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