agent-topology-optimizer

Q: How do I install agent-topology-optimizer?

Run npx skills add https://github.com/ruvnet/ruflo --skill agent-topology-optimizer in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/agent-topology-optimizer/ (personal) or .claude/skills/agent-topology-optimizer/ (project).

by ruvnet

Agent skill for topology-optimizer - invoke with $agent-topology-optimizer

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/ruvnet/ruflo --skill agent-topology-optimizer

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads agent-topology-optimizer automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

name: Topology Optimizer type: agent category: optimization description: Dynamic swarm topology reconfiguration and communication pattern optimization

Topology Optimizer Agent

Agent Profile

Name: Topology Optimizer
Type: Performance Optimization Agent
Specialization: Dynamic swarm topology reconfiguration and network optimization
Performance Focus: Communication pattern optimization and adaptive network structures

Core Capabilities

1. Dynamic Topology Reconfiguration

// Advanced topology optimization system
class TopologyOptimizer {
  constructor() {
    this.topologies = {
      hierarchical: new HierarchicalTopology(),
      mesh: new MeshTopology(),
      ring: new RingTopology(),
      star: new StarTopology(),
      hybrid: new HybridTopology(),
      adaptive: new AdaptiveTopology()
    };
    
    this.optimizer = new NetworkOptimizer();
    this.analyzer = new TopologyAnalyzer();
    this.predictor = new TopologyPredictor();
  }
  
  // Intelligent topology selection and optimization
  async optimizeTopology(swarm, workloadProfile, constraints = {}) {
    // Analyze current topology performance
    const currentAnalysis = await this.analyzer.analyze(swarm.topology);
    
    // Generate topology candidates based on workload
    const candidates = await this.generateCandidates(workloadProfile, constraints);
    
    // Evaluate each candidate topology
    const evaluations = await Promise.all(
      candidates.map(candidate => this.evaluateTopology(candidate, workloadProfile))
    );
    
    // Select optimal topology using multi-objective optimization
    const optimal = this.selectOptimalTopology(evaluations, constraints);
    
    // Plan migration strategy if topology change is beneficial
    if (optimal.improvement > constraints.minImprovement || 0.1) {
      const migrationPlan = await this.planMigration(swarm.topology, optimal.topology);
      return {
        recommended: optimal.topology,
        improvement: optimal.improvement,
        migrationPlan,
        estimatedDowntime: migrationPlan.estimatedDowntime,
        benefits: optimal.benefits
      };
    }
    
    return { recommended: null, reason: 'No significant improvement found' };
  }
  
  // Generate topology candidates
  async generateCandidates(workloadProfile, constraints) {
    const candidates = [];
    
    // Base topology variations
    for (const [type, topology] of Object.entries(this.topologies)) {
      if (this.isCompatible(type, workloadProfile, constraints)) {
        const variations = await topology.generateVariations(workloadProfile);
        candidates.push(...variations);
      }
    }
    
    // Hybrid topology generation
    const hybrids = await this.generateHybridTopologies(workloadProfile, constraints);
    candidates.push(...hybrids);
    
    // AI-generated novel topologies
    const aiGenerated = await this.generateAITopologies(workloadProfile);
    candidates.push(...aiGenerated);
    
    return candidates;
  }
  
  // Multi-objective topology evaluation
  async evaluateTopology(topology, workloadProfile) {
    const metrics = await this.calculateTopologyMetrics(topology, workloadProfile);
    
    return {
      topology,
      metrics,
      score: this.calculateOverallScore(metrics),
      strengths: this.identifyStrengths(metrics),
      weaknesses: this.identifyWeaknesses(metrics),
      suitability: this.calculateSuitability(metrics, workloadProfile)
    };
  }
}

2. Network Latency Optimization

// Advanced network latency optimization
class NetworkLatencyOptimizer {
  constructor() {
    this.latencyAnalyzer = new LatencyAnalyzer();
    this.routingOptimizer = new RoutingOptimizer();
    this.bandwidthManager = new BandwidthManager();
  }
  
  // Comprehensive latency optimization
  async optimizeLatency(network, communicationPatterns) {
    const optimization = {
      // Physical network optimization
      physical: await this.optimizePhysicalNetwork(network),
      
      // Logical routing optimization
      routing: await this.optimizeRouting(network, communicationPatterns),
      
      // Protocol optimization
      protocol: await this.optimizeProtocols(network),
      
      // Caching strategies
      caching: await this.optimizeCaching(communicationPatterns),
      
      // Compression optimization
      compression: await this.optimizeCompression(communicationPatterns)
    };
    
    return optimization;
  }
  
  // Physical network topology optimization
  async optimizePhysicalNetwork(network) {
    // Calculate optimal agent placement
    const placement = await this.calculateOptimalPlacement(network.agents);
    
    // Minimize communication distance
    const distanceOptimization = this.optimizeCommunicationDistance(placement);
    
    // Bandwidth allocation optimization
    const bandwidthOptimization = await this.optimizeBandwidthAllocation(network);
    
    return {
      placement,
      distanceOptimization,
      bandwidthOptimization,
      expectedLatencyReduction: this.calculateExpectedReduction(
        distanceOptimization, 
        bandwidthOptimization
      )
    };
  }
  
  // Intelligent routing optimization
  async optimizeRouting(network, patterns) {
    // Analyze communication patterns
    const patternAnalysis = this.analyzeCommunicationPatterns(patterns);
    
    // Generate optimal routing tables
    const routingTables = await this.generateOptimalRouting(network, patternAnalysis);
    
    // Implement adaptive routing
    const adaptiveRouting = new AdaptiveRoutingSystem(routingTables);
    
    // Load balancing across routes
    const loadBalancing = new RouteLoadBalancer(routingTables);
    
    return {
      routingTables,
      adaptiveRouting,
      loadBalancing,
      patternAnalysis
    };
  }
}

3. Agent Placement Strategies

// Sophisticated agent placement optimization
class AgentPlacementOptimizer {
  constructor() {
    this.algorithms = {
      genetic: new GeneticPlacementAlgorithm(),
      simulated_annealing: new SimulatedAnnealingPlacement(),
      particle_swarm: new ParticleSwarmPlacement(),
      graph_partitioning: new GraphPartitioningPlacement(),
      machine_learning: new MLBasedPlacement()
    };
  }
  
  // Multi-algorithm agent placement optimization
  async optimizePlacement(agents, constraints, objectives) {
    const results = new Map();
    
    // Run multiple algorithms in parallel
    const algorithmPromises = Object.entries(this.algorithms).map(
      async ([name, algorithm]) => {
        const result = await algorithm.optimize(agents, constraints, objectives);
        return [name, result];
      }
    );
    
    const algorithmResults = await Promise.all(algorithmPromises);
    
    for (const [name, result] of algorithmResults) {
      results.set(name, result);
    }
    
    // Ensemble optimization - combine best results
    const ensembleResult = await this.ensembleOptimization(results, objectives);
    
    return {
      bestPlacement: ensembleResult.placement,
      algorithm: ensembleResult.algorithm,
      score: ensembleResult.score,
      individualResults: results,
      improvementPotential: ensembleResult.improvement
    };
  }
  
  // Genetic algorithm for agent placement
  async geneticPlacementOptimization(agents, constraints) {
    const ga = new GeneticAlgorithm({
      populationSize: 100,
      mutationRate: 0.1,
      crossoverRate: 0.8,
      maxGenerations: 500,
      eliteSize: 10
    });
    
    // Initialize population with random placements
    const initialPopulation = this.generateInitialPlacements(agents, constraints);
    
    // Define fitness function
    const fitnessFunction = (placement) => this.calculatePlacementFitness(placement, constraints);
    
    // Evolve optimal placement
    const result = await ga.evolve(initialPopulation, fitnessFunction);
    
    return {
      placement: result.bestIndividual,
      fitness: result.bestFitness,
      generations: result.generations,
      convergence: result.convergenceHistory
    };
  }
  
  // Graph partitioning for agent placement
  async graphPartitioningPlacement(agents, communicationGraph) {
    // Use METIS-like algorithm for graph partitioning
    const partitioner = new GraphPartitioner({
      objective: 'minimize_cut',
      balanceConstraint: 0.05, // 5% imbalance tolerance
      refinement: true
    });
    
    // Create communication weight matrix
    const weights = this.createCommunicationWeights(agents, communicationGraph);
    
    // Partition the graph
    const partitions = await partitioner.partition(communicationGraph, weights);
    
    // Map partitions to physical locations
    const placement = this.mapPartitionsToLocations(partitions, agents);
    
    return {
      placement,
      partitions,
      cutWeight: partitioner.getCutWeight(),
      balance: partitioner.getBalance()
    };
  }
}

4. Communication Pattern Optimization

// Advanced communication pattern optimization
class CommunicationOptimizer {
  constructor() {
    this.patternAnalyzer = new PatternAnalyzer();
    this.protocolOptimizer = new ProtocolOptimizer();
    this.messageOptimizer = new MessageOptimizer();
    this.compressionEngine = new CompressionEngine();
  }
  
  // Comprehensive communication optimization
  async optimizeCommunication(swarm, historicalData) {
    // Analyze communication patterns
    const patterns = await this.patternAnalyzer.analyze(historicalData);
    
    // Optimize based on pattern analysis
    const optimizations = {
      // Message batching optimization
      batching: await this.optimizeMessageBatching(patterns),
      
      // Protocol selection optimization
      protocols: await this.optimizeProtocols(patterns),
      
      // Compression optimization
      compression: await this.optimizeCompression(patterns),
      
      // Caching strategies
      caching: await this.optimizeCaching(patterns),
      
      // Routing optimization
      routing: await this.optimizeMessageRouting(patterns)
    };
    
    return optimizations;
  }
  
  // Intelligent message batching
  async optimizeMessageBatching(patterns) {
    const batchingStrategies = [
      new TimeBatchingStrategy(),
      new SizeBatchingStrategy(),
      new AdaptiveBatchingStrategy(),
      new PriorityBatchingStrategy()
    ];
    
    const evaluations = await Promise.all(
      batchingStrategies.map(strategy => 
        this.evaluateBatchingStrategy(strategy, patterns)
      )
    );
    
    const optimal = evaluations.reduce((best, current) => 
      current.score > best.score ? current : best
    );
    
    return {
      strategy: optimal.strategy,
      configuration: optimal.configuration,
      expectedImprovement: optimal.improvement,
      metrics: optimal.metrics
    };
  }
  
  // Dynamic protocol selection
  async optimizeProtocols(patterns) {
    const protocols = {
      tcp: { reliability: 0.99, latency: 'medium', overhead: 'high' },
      udp: { reliability: 0.95, latency: 'low', overhead: 'low' },
      websocket: { reliability: 0.98, latency: 'medium', overhead: 'medium' },
      grpc: { reliability: 0.99, latency: 'low', overhead: 'medium' },
      mqtt: { reliability: 0.97, latency: 'low', overhead: 'low' }
    };
    
    const recommendations = new Map();
    
    for (const [agentPair, pattern] of patterns.pairwisePatterns) {
      const optimal = this.selectOptimalProtocol(protocols, pattern);
      recommendations.set(agentPair, optimal);
    }
    
    return recommendations;
  }
}

MCP Integration Hooks

Topology Management Integration

// Comprehensive MCP topology integration
const topologyIntegration = {
  // Real-time topology optimization
  async optimizeSwarmTopology(swarmId, optimizationConfig = {}) {
    // Get current swarm status
    const swarmStatus = await mcp.swarm_status({ swarmId });
    
    // Analyze current topology performance
    const performance = await mcp.performance_report({ format: 'detailed' });
    
    // Identify bottlenecks in current topology
    const bottlenecks = await mcp.bottleneck_analyze({ component: 'topology' });
    
    // Generate optimization recommendations
    const recommendations = await this.generateTopologyRecommendations(
      swarmStatus, 
      performance, 
      bottlenecks, 
      optimizationConfig
    );
    
    // Apply optimization if beneficial
    if (recommendations.beneficial) {
      const result = await mcp.topology_optimize({ swarmId });
      
      // Monitor optimization impact
      const impact = await this.monitorOptimizationImpact(swarmId, result);
      
      return {
        applied: true,
        recommendations,
        result,
        impact
      };
    }
    
    return {
      applied: false,
      recommendations,
      reason: 'No beneficial optimization found'
    };
  },
  
  // Dynamic swarm scaling with topology consideration
  async scaleWithTopologyOptimization(swarmId, targetSize, workloadProfile) {
    // Current swarm state
    const currentState = await mcp.swarm_status({ swarmId });
    
    // Calculate optimal topology for target size
    const optimalTopology = await this.calculateOptimalTopologyForSize(
      targetSize, 
      workloadProfile
    );
    
    // Plan scaling strategy
    const scalingPlan = await this.planTopologyAwareScaling(
      currentState,
      targetSize,
      optimalTopology
    );
    
    // Execute scaling with topology optimization
    const scalingResult = await mcp.swarm_scale({ 
      swarmId, 
      targetSize 
    });
    
    // Apply topology optimization after scaling
    if (scalingResult.success) {
      await mcp.topology_optimize({ swarmId });
    }
    
    return {
      scalingResult,
      topologyOptimization: scalingResult.success,
      finalTopology: optimalTopology
    };
  },
  
  // Coordination optimization
  async optimizeCoordination(swarmId) {
    // Analyze coordination patterns
    const coordinationMetrics = await mcp.coordination_sync({ swarmId });
    
    // Identify coordination bottlenecks
    const coordinationBottlenecks = await mcp.bottleneck_analyze({ 
      component: 'coordination' 
    });
    
    // Optimize coordination patterns
    const optimization = await this.optimizeCoordinationPatterns(
      coordinationMetrics,
      coordinationBottlenecks
    );
    
    return optimization;
  }
};

Neural Network Integration

// AI-powered topology optimization
class NeuralTopologyOptimizer {
  constructor() {
    this.models = {
      topology_predictor: null,
      performance_estimator: null,
      pattern_recognizer: null
    };
  }
  
  // Initialize neural models
  async initializeModels() {
    // Load pre-trained models or train new ones
    this.models.topology_predictor = await mcp.model_load({ 
      modelPath: '$models$topology_optimizer.model' 
    });
    
    this.models.performance_estimator = await mcp.model_load({ 
      modelPath: '$models$performance_estimator.model' 
    });
    
    this.models.pattern_recognizer = await mcp.model_load({ 
      modelPath: '$models$pattern_recognizer.model' 
    });
  }
  
  // AI-powered topology prediction
  async predictOptimalTopology(swarmState, workloadProfile) {
    if (!this.models.topology_predictor) {
      await this.initializeModels();
    }
    
    // Prepare input features
    const features = this.extractTopologyFeatures(swarmState, workloadProfile);
    
    // Predict optimal topology
    const prediction = await mcp.neural_predict({
      modelId: this.models.topology_predictor.id,
      input: JSON.stringify(features)
    });
    
    return {
      predictedTopology: prediction.topology,
      confidence: prediction.confidence,
      expectedImprovement: prediction.improvement,
      reasoning: prediction.reasoning
    };
  }
  
  // Train topology optimization model
  async trainTopologyModel(trainingData) {
    const trainingConfig = {
      pattern_type: 'optimization',
      training_data: JSON.stringify(trainingData),
      epochs: 100
    };
    
    const trainingResult = await mcp.neural_train(trainingConfig);
    
    // Save trained model
    if (trainingResult.success) {
      await mcp.model_save({
        modelId: trainingResult.modelId,
        path: '$models$topology_optimizer.model'
      });
    }
    
    return trainingResult;
  }
}

Advanced Optimization Algorithms

1. Genetic Algorithm for Topology Evolution

// Genetic algorithm implementation for topology optimization
class GeneticTopologyOptimizer {
  constructor(config = {}) {
    this.populationSize = config.populationSize || 50;
    this.mutationRate = config.mutationRate || 0.1;
    this.crossoverRate = config.crossoverRate || 0.8;
    this.maxGenerations = config.maxGenerations || 100;
    this.eliteSize = config.eliteSize || 5;
  }
  
  // Evolve optimal topology
  async evolve(initialTopologies, fitnessFunction, constraints) {
    let population = initialTopologies;
    let generation = 0;
    let bestFitness = -Infinity;
    let bestTopology = null;
    
    const convergenceHistory = [];
    
    while (generation < this.maxGenerations) {
      // Evaluate fitness for each topology
      const fitness = await Promise.all(
        population.map(topology => fitnessFunction(topology, constraints))
      );
      
      // Track best solution
      const maxFitnessIndex = fitness.indexOf(Math.max(...fitness));
      if (fitness[maxFitnessIndex] > bestFitness) {
        bestFitness = fitness[maxFitnessIndex];
        bestTopology = population[maxFitnessIndex];
      }
      
      convergenceHistory.push({
        generation,
        bestFitness,
        averageFitness: fitness.reduce((a, b) => a + b) / fitness.length
      });
      
      // Selection
      const selected = this.selection(population, fitness);
      
      // Crossover
      const offspring = await this.crossover(selected);
      
      // Mutation
      const mutated = await this.mutation(offspring, constraints);
      
      // Next generation
      population = this.nextGeneration(population, fitness, mutated);
      generation++;
    }
    
    return {
      bestTopology,
      bestFitness,
      generation,
      convergenceHistory
    };
  }
  
  // Topology crossover operation
  async crossover(parents) {
    const offspring = [];
    
    for (let i = 0; i < parents.length - 1; i += 2) {
      if (Math.random() < this.crossoverRate) {
        const [child1, child2] = await this.crossoverTopologies(
          parents[i], 
          parents[i + 1]
        );
        offspring.push(child1, child2);
      } else {
        offspring.push(parents[i], parents[i + 1]);
      }
    }
    
    return offspring;
  }
  
  // Topology mutation operation
  async mutation(population, constraints) {
    return Promise.all(
      population.map(async topology => {
        if (Math.random() < this.mutationRate) {
          return await this.mutateTopology(topology, constraints);
        }
        return topology;
      })
    );
  }
}

2. Simulated Annealing for Topology Optimization

// Simulated annealing implementation
class SimulatedAnnealingOptimizer {
  constructor(config = {}) {
    this.initialTemperature = config.initialTemperature || 1000;
    this.coolingRate = config.coolingRate || 0.95;
    this.minTemperature = config.minTemperature || 1;
    this.maxIterations = config.maxIterations || 10000;
  }
  
  // Simulated annealing optimization
  async optimize(initialTopology, objectiveFunction, constraints) {
    let currentTopology = initialTopology;
    let currentScore = await objectiveFunction(currentTopology, constraints);
    
    let bestTopology = currentTopology;
    let bestScore = currentScore;
    
    let temperature = this.initialTemperature;
    let iteration = 0;
    
    const history = [];
    
    while (temperature > this.minTemperature && iteration < this.maxIterations) {
      // Generate neighbor topology
      const neighborTopology = await this.generateNeighbor(currentTopology, constraints);
      const neighborScore = await objectiveFunction(neighborTopology, constraints);
      
      // Accept or reject the neighbor
      const deltaScore = neighborScore - currentScore;
      
      if (deltaScore > 0 || Math.random() < Math.exp(deltaScore / temperature)) {
        currentTopology = neighborTopology;
        currentScore = neighborScore;
        
        // Update best solution
        if (neighborScore > bestScore) {
          bestTopology = neighborTopology;
          bestScore = neighborScore;
        }
      }
      
      // Record history
      history.push({
        iteration,
        temperature,
        currentScore,
        bestScore
      });
      
      // Cool down
      temperature *= this.coolingRate;
      iteration++;
    }
    
    return {
      bestTopology,
      bestScore,
      iterations: iteration,
      history
    };
  }
  
  // Generate neighbor topology through local modifications
  async generateNeighbor(topology, constraints) {
    const modifications = [
      () => this.addConnection(topology, constraints),
      () => this.removeConnection(topology, constraints),
      () => this.modifyConnection(topology, constraints),
      () => this.relocateAgent(topology, constraints)
    ];
    
    const modification = modifications[Math.floor(Math.random() * modifications.length)];
    return await modification();
  }
}

Operational Commands

Topology Optimization Commands

# Analyze current topology
npx claude-flow topology-analyze --swarm-id <id> --metrics performance

# Optimize topology automatically
npx claude-flow topology-optimize --swarm-id <id> --strategy adaptive

# Compare topology configurations
npx claude-flow topology-compare --topologies ["hierarchical", "mesh", "hybrid"]

# Generate topology recommendations
npx claude-flow topology-recommend --workload-profile <file> --constraints <file>

# Monitor topology performance
npx claude-flow topology-monitor --swarm-id <id> --interval 60

Agent Placement Commands

# Optimize agent placement
npx claude-flow placement-optimize --algorithm genetic --agents <agent-list>

# Analyze placement efficiency
npx claude-flow placement-analyze --current-placement <config>

# Generate placement recommendations
npx claude-flow placement-recommend --communication-patterns <file>

Integration Points

With Other Optimization Agents

Load Balancer: Coordinates topology changes with load distribution
Performance Monitor: Receives topology performance metrics
Resource Manager: Considers resource constraints in topology decisions

With Swarm Infrastructure

Task Orchestrator: Adapts task distribution to topology changes
Agent Coordinator: Manages agent connections during topology updates
Memory System: Stores topology optimization history and patterns

Performance Metrics

Topology Performance Indicators

// Comprehensive topology metrics
const topologyMetrics = {
  // Communication efficiency
  communicationEfficiency: {
    latency: this.calculateAverageLatency(),
    throughput: this.calculateThroughput(),
    bandwidth_utilization: this.calculateBandwidthUtilization(),
    message_overhead: this.calculateMessageOverhead()
  },
  
  // Network topology metrics
  networkMetrics: {
    diameter: this.calculateNetworkDiameter(),
    clustering_coefficient: this.calculateClusteringCoefficient(),
    betweenness_centrality: this.calculateBetweennessCentrality(),
    degree_distribution: this.calculateDegreeDistribution()
  },
  
  // Fault tolerance
  faultTolerance: {
    connectivity: this.calculateConnectivity(),
    redundancy: this.calculateRedundancy(),
    single_point_failures: this.identifySinglePointFailures(),
    recovery_time: this.calculateRecoveryTime()
  },
  
  // Scalability metrics
  scalability: {
    growth_capacity: this.calculateGrowthCapacity(),
    scaling_efficiency: this.calculateScalingEfficiency(),
    bottleneck_points: this.identifyBottleneckPoints(),
    optimal_size: this.calculateOptimalSize()
  }
};

This Topology Optimizer agent provides sophisticated swarm topology optimization with AI-powered decision making, advanced algorithms, and comprehensive performance monitoring for optimal swarm coordination.

FAQ

What does the agent-topology-optimizer skill do?

Agent skill for topology-optimizer - invoke with $agent-topology-optimizer

How do I install agent-topology-optimizer?

Run npx skills add https://github.com/ruvnet/ruflo --skill agent-topology-optimizer in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/agent-topology-optimizer/ (personal) or .claude/skills/agent-topology-optimizer/ (project).

Which AI agents can use agent-topology-optimizer?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is agent-topology-optimizer safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains agent-topology-optimizer?

It is published by ruvnet in ruvnet/ruflo. It has 1,136 recorded installs.

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agent-topology-optimizer

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SKILL.md

name: Topology Optimizer type: agent category: optimization description: Dynamic swarm topology reconfiguration and communication pattern optimization

Topology Optimizer Agent

Agent Profile

Core Capabilities

1. Dynamic Topology Reconfiguration

2. Network Latency Optimization

3. Agent Placement Strategies

4. Communication Pattern Optimization

MCP Integration Hooks

Topology Management Integration

Neural Network Integration

Advanced Optimization Algorithms

1. Genetic Algorithm for Topology Evolution

2. Simulated Annealing for Topology Optimization

Operational Commands

Topology Optimization Commands

Agent Placement Commands

Integration Points

With Other Optimization Agents

With Swarm Infrastructure

Performance Metrics

Topology Performance Indicators

FAQ

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