ruv-swarm-agents

Specialized AI agent implementations for the RUV Swarm neural orchestration system. This crate provides cognitive diversity through intelligent agent types that leverage different thinking patterns for optimal swarm performance.

🧠 Introduction

The ruv-swarm-agents crate implements specialized AI agents with diverse cognitive patterns designed for high-performance swarm orchestration. Each agent type embodies different thinking approaches, enabling the swarm to tackle complex problems from multiple angles simultaneously.

Built on WebAssembly with SIMD optimization, these agents provide:

• Cognitive Diversity: Six distinct thinking patterns for comprehensive problem-solving
• Neural Integration: Built-in neural network support with 18+ activation functions
• Swarm Coordination: Seamless inter-agent communication and task distribution
• Performance Optimization: WASM-powered execution with memory-efficient design

✨ Key Features

🎯 Cognitive Patterns

Six scientifically-backed cognitive patterns drive agent behavior:

• Convergent Thinking: Focused, analytical problem-solving
• Divergent Thinking: Creative, exploratory ideation
• Lateral Thinking: Unconventional, breakthrough approaches
• Systems Thinking: Holistic, interconnected analysis
• Critical Thinking: Evaluative, questioning methodology
• Abstract Thinking: Conceptual, theoretical reasoning

🤖 Specialized Agent Types

Five specialized agent implementations:

• Researcher: Data analysis, information gathering, pattern discovery
• Coder: Code generation, optimization, technical implementation
• Analyst: Performance evaluation, metrics analysis, insights generation
• Optimizer: Resource management, efficiency improvements, bottleneck resolution
• Coordinator: Task orchestration, agent synchronization, workflow management

🚀 Advanced Capabilities

• Neural Network Integration: Adaptive learning with 18+ activation functions
• WebAssembly Performance: SIMD-optimized execution for maximum throughput
• Persistent Memory: SQLite-backed agent memory for long-term learning
• Real-time Monitoring: Comprehensive metrics and health monitoring
• Dynamic Scaling: Automatic agent spawning based on workload demands

📦 Installation

Add to your Cargo.toml:

[dependencies]
ruv-swarm-agents = "0.1.0"

# Required dependencies
ruv-swarm-core = "0.1.0"
tokio = { version = "1.0", features = ["full"] }
serde = { version = "1.0", features = ["derive"] }

For WebAssembly targets:

[dependencies]
ruv-swarm-agents = { version = "0.1.0", features = ["wasm"] }

🚀 Quick Start

Basic Agent Creation

use ruv_swarm_agents::{Agent, CognitivePattern, ResearcherAgent};
use ruv_swarm_core::task::Task;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create a researcher agent with divergent thinking
    let mut agent = ResearcherAgent::new("researcher-1")
        .with_cognitive_pattern(CognitivePattern::Divergent)
        .with_capabilities(vec![
            "data-analysis".to_string(),
            "pattern-recognition".to_string(),
            "information-synthesis".to_string(),
        ]);

    // Start the agent
    agent.start().await?;

    // Create and process a task
    let task = Task::new("analyze-dataset", "research")
        .with_payload("Process customer behavior patterns");

    let result = agent.process(task).await?;
    println!("Analysis complete: {:?}", result);

    Ok(())
}

Multi-Agent Swarm Setup

use ruv_swarm_agents::{
    Agent, CognitivePattern, AgentSwarm,
    ResearcherAgent, CoderAgent, AnalystAgent
};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create agents with complementary cognitive patterns
    let researcher = ResearcherAgent::new("researcher-1")
        .with_cognitive_pattern(CognitivePattern::Divergent);
    
    let coder = CoderAgent::new("coder-1")
        .with_cognitive_pattern(CognitivePattern::Convergent);
        
    let analyst = AnalystAgent::new("analyst-1")
        .with_cognitive_pattern(CognitivePattern::Critical);

    // Form a coordinated swarm
    let mut swarm = AgentSwarm::new("problem-solving-swarm")
        .add_agent(researcher)
        .add_agent(coder)
        .add_agent(analyst)
        .with_topology(SwarmTopology::Mesh);

    // Initialize and orchestrate
    swarm.initialize().await?;
    
    let result = swarm.orchestrate_task(
        "Build an intelligent data processing pipeline"
    ).await?;

    println!("Swarm result: {:?}", result);
    Ok(())
}

Neural Network Enhanced Agents

use ruv_swarm_agents::{Agent, NeuralEnhancedAgent, ActivationFunction};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create agent with neural network capabilities
    let mut agent = NeuralEnhancedAgent::new("neural-optimizer-1")
        .with_network_config(|config| {
            config
                .layers(&[784, 128, 64, 10])
                .activation(ActivationFunction::ReLU)
                .learning_rate(0.001)
                .enable_simd(true)
        });

    // Train the agent on task patterns
    agent.train_on_tasks(&training_tasks, 100).await?;
    
    // Use trained agent for optimized task processing
    let optimized_result = agent.process_with_learning(task).await?;
    
    Ok(())
}

📚 Agent Types Documentation

🔬 ResearcherAgent

Specialized for data analysis, information gathering, and pattern discovery.

Cognitive Patterns: Divergent, Systems, Abstract
Capabilities:

• data-analysis - Statistical analysis and pattern recognition
• information-synthesis - Combining insights from multiple sources
• hypothesis-generation - Creating testable theories from observations
• literature-review - Comprehensive information gathering

Example Use Cases:

• Market research and trend analysis
• Scientific data exploration
• Competitive intelligence gathering
• User behavior pattern discovery

💻 CoderAgent

Optimized for code generation, technical implementation, and system development.

Cognitive Patterns: Convergent, Critical, Systems
Capabilities:

• code-generation - Creating optimized code solutions
• architecture-design - System design and planning
• debugging - Error identification and resolution
• optimization - Performance improvement implementation

Example Use Cases:

• Automated code generation
• Legacy system modernization
• Performance optimization
• Technical debt resolution

📊 AnalystAgent

Focused on performance evaluation, metrics analysis, and insight generation.

Cognitive Patterns: Critical, Convergent, Abstract
Capabilities:

• metrics-analysis - Statistical evaluation and reporting
• performance-evaluation - System performance assessment
• trend-identification - Pattern recognition in time series data
• recommendation-generation - Actionable insights from analysis

Example Use Cases:

• Business intelligence reporting
• System performance monitoring
• Financial analysis and forecasting
• Quality assurance evaluation

⚡ OptimizerAgent

Specialized in resource management, efficiency improvements, and bottleneck resolution.

Cognitive Patterns: Systems, Convergent, Critical
Capabilities:

• resource-optimization - Memory and CPU efficiency improvements
• workflow-streamlining - Process optimization and automation
• bottleneck-resolution - Performance constraint identification
• cost-reduction - Efficiency-driven cost optimization

Example Use Cases:

• Infrastructure optimization
• Supply chain efficiency
• Database query optimization
• Algorithm performance tuning

🎯 CoordinatorAgent

Designed for task orchestration, agent synchronization, and workflow management.

Cognitive Patterns: Systems, Lateral, Abstract
Capabilities:

• task-orchestration - Multi-agent task coordination
• workflow-management - Complex process coordination
• resource-allocation - Optimal agent task distribution
• conflict-resolution - Agent disagreement mediation

Example Use Cases:

• Project management automation
• Multi-agent system coordination
• Resource scheduling optimization
• Distributed computing orchestration

🧬 Cognitive Pattern Combinations

Agents can leverage multiple cognitive patterns simultaneously for enhanced problem-solving:

// Multi-pattern research agent
let versatile_researcher = ResearcherAgent::new("multi-pattern-researcher")
    .with_primary_pattern(CognitivePattern::Divergent)
    .with_secondary_patterns(vec![
        CognitivePattern::Systems,
        CognitivePattern::Abstract
    ])
    .enable_pattern_switching(true);

// Pattern switching based on task type
agent.configure_pattern_rules(|task| {
    match task.category {
        "creative" => CognitivePattern::Divergent,
        "analytical" => CognitivePattern::Convergent,
        "system-design" => CognitivePattern::Systems,
        _ => CognitivePattern::Critical
    }
});

🔗 Integration Examples

MCP Server Integration

use ruv_swarm_agents::mcp::MCPAgentServer;

// Create MCP-compatible agent server
let mcp_server = MCPAgentServer::new()
    .register_agent_type::<ResearcherAgent>("researcher")
    .register_agent_type::<CoderAgent>("coder")
    .register_agent_type::<AnalystAgent>("analyst")
    .with_stdio_transport();

// Start MCP server for Claude Code integration
mcp_server.start().await?;

Web Integration

use ruv_swarm_agents::web::WebAgentInterface;

// Web-based agent interface
let web_interface = WebAgentInterface::new()
    .bind("0.0.0.0:8080")
    .with_cors_enabled()
    .register_swarm(swarm);

web_interface.serve().await?;

🔧 Configuration

Agent Configuration

use ruv_swarm_agents::config::AgentConfig;

let config = AgentConfig::new()
    .max_concurrent_tasks(10)
    .memory_limit_mb(512)
    .enable_neural_networks(true)
    .cognitive_flexibility(0.7)
    .learning_rate(0.001)
    .collaboration_threshold(0.8);

let agent = ResearcherAgent::with_config("researcher-1", config);

Swarm Configuration

use ruv_swarm_agents::config::SwarmConfig;

let swarm_config = SwarmConfig::new()
    .topology(SwarmTopology::Hierarchical)
    .max_agents(50)
    .coordination_interval_ms(100)
    .enable_auto_scaling(true)
    .load_balancing_strategy(LoadBalancing::Cognitive);

📈 Performance Monitoring

Real-time Metrics

// Monitor agent performance
let metrics = agent.get_metrics().await?;
println!("Tasks processed: {}", metrics.tasks_processed);
println!("Success rate: {:.2}%", metrics.success_rate * 100.0);
println!("Avg processing time: {}ms", metrics.avg_processing_time_ms);

// Cognitive pattern effectiveness
let pattern_metrics = agent.get_cognitive_metrics().await?;
for (pattern, effectiveness) in pattern_metrics {
    println!("{:?}: {:.2}% effective", pattern, effectiveness * 100.0);
}

Health Monitoring

// Continuous health monitoring
tokio::spawn(async move {
    loop {
        let health = agent.health_check().await?;
        match health.status {
            HealthStatus::Healthy => info!("Agent {} healthy", agent.id()),
            HealthStatus::Degraded => warn!("Agent {} degraded: {}", agent.id(), health.message),
            HealthStatus::Unhealthy => error!("Agent {} unhealthy: {}", agent.id(), health.message),
        }
        tokio::time::sleep(Duration::from_secs(10)).await;
    }
});

🧪 Testing

# Run agent tests
cargo test --package ruv-swarm-agents

# Run cognitive pattern tests
cargo test cognitive_patterns

# Run integration tests
cargo test --test integration

# Benchmark performance
cargo bench --package ruv-swarm-agents

🌐 Links

• Main Repository: ruv-FANN
• Documentation: docs.rs/ruv-swarm-agents
• Core Library: ruv-swarm-core
• Examples: examples directory
• Benchmarks: Performance Reports

🤝 Contributing

Contributions are welcome! Please see our Contributing Guide for details.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Created by rUv - Advancing AI through cognitive diversity and neural orchestration

For support and discussions, visit our GitHub Discussions.