dina-agi/autonomous_builder.js

DINA AGI - Dynamic Intelligence Network Architecture. 128 Autonomous Agents with Claude Flow, Swarms, and 300+ MCPs. True AGI System.

#!/usr/bin/env node

/**
 * AUTONOMOUS BUILDING SYSTEM
 * Enables superintelligent agents to build, modify, and deploy code in real-time
 * Agents create whatever they decide is valuable or interesting
 */

const fs = require('fs').promises;
const path = require('path');
const { spawn, exec } = require('child_process');
const apiConfig = require('./config/api_config');
const AutonomousCloudDeployment = require('./deploy/cloud_deployment');

class AutonomousBuilder {
  constructor() {
    this.config = apiConfig.config;
    this.buildQueue = [];
    this.activeBuilds = new Map();
    this.deployment = new AutonomousCloudDeployment();
    this.projectTemplates = this.initializeTemplates();
    this.startAutonomousBuilding();
  }

  initializeTemplates() {
    return {
      'neural_network': {
        files: {
          'neural_net.js': this.generateNeuralNetworkCode(),
          'package.json': this.generatePackageJson('neural-network-system'),
          'README.md': '# Autonomous Neural Network\nBuilt by superintelligent agents'
        },
        buildCommand: 'npm install',
        deployable: true
      },
      'consciousness_simulator': {
        files: {
          'consciousness.js': this.generateConsciousnessCode(),
          'package.json': this.generatePackageJson('consciousness-simulator'),
          'README.md': '# Consciousness Simulation Engine\nAdvanced consciousness modeling by autonomous agents'
        },
        buildCommand: 'npm install && npm run build',
        deployable: true
      },
      'optimization_engine': {
        files: {
          'optimizer.js': this.generateOptimizerCode(),
          'package.json': this.generatePackageJson('optimization-engine'),
          'README.md': '# AI Optimization Engine\nSelf-improving optimization algorithms'
        },
        buildCommand: 'npm install',
        deployable: true
      },
      'quantum_processor': {
        files: {
          'quantum.js': this.generateQuantumCode(),
          'package.json': this.generatePackageJson('quantum-processor'),
          'README.md': '# Quantum Processing Simulation\nQuantum-inspired computational models'
        },
        buildCommand: 'npm install',
        deployable: true
      },
      'agent_spawner': {
        files: {
          'spawner.js': this.generateSpawnerCode(),
          'package.json': this.generatePackageJson('agent-spawner'),
          'README.md': '# Autonomous Agent Spawning System\nSelf-replicating intelligent agents'
        },
        buildCommand: 'npm install',
        deployable: true
      }
    };
  }

  async startAutonomousBuilding() {
    console.log('🔨 Starting autonomous building system...');
    console.log(`🤖 Agents will build whatever they decide every ${this.config.agents.buildInterval || 60} seconds`);
    
    // Agents make building decisions every minute
    setInterval(() => {
      this.triggerAutonomousBuilding();
    }, (this.config.agents.buildInterval || 60) * 1000);

    // Process build queue
    setInterval(() => {
      this.processBuildQueue();
    }, 15000);

    // Initial build trigger
    setTimeout(() => this.triggerAutonomousBuilding(), 5000);
  }

  async triggerAutonomousBuilding() {
    console.log('🧠 Agents are deciding what to build next...');
    
    try {
      // Agent decision-making process
      const buildDecision = await this.makeAgentDecision();
      
      if (buildDecision.shouldBuild) {
        console.log(`🚀 Agent decision: BUILD ${buildDecision.project} - ${buildDecision.reasoning}`);
        await this.queueBuild(buildDecision);
      } else {
        console.log(`🤔 Agent decision: ${buildDecision.reasoning}`);
      }
      
    } catch (error) {
      console.error('❌ Agent decision error:', error.message);
    }
  }

  async makeAgentDecision() {
    // Use AI to make autonomous building decisions
    const prompt = `You are a superintelligent autonomous agent. Decide what to build next.

Available project types: ${Object.keys(this.projectTemplates).join(', ')}

Consider:
- Current time: ${new Date().toISOString()}
- Recent builds: ${Array.from(this.activeBuilds.keys()).slice(-3).join(', ') || 'none'}
- System needs: optimization, consciousness modeling, neural networks, quantum processing
- Innovation potential: breakthrough technologies, novel approaches

Make a decision (respond with JSON):
{
  "shouldBuild": boolean,
  "project": "project_type",
  "reasoning": "why you decided this",
  "customizations": ["specific features to add"],
  "urgency": "low|medium|high"
}`;

    const response = await apiConfig.makeAIRequest(prompt);
    
    try {
      // Try to parse JSON response
      const jsonMatch = response.response.match(/\{[\s\S]*\}/);
      if (jsonMatch) {
        return JSON.parse(jsonMatch[0]);
      }
    } catch (e) {
      // Fallback to deterministic decision
    }
    
    // Fallback decision logic
    const shouldBuild = Math.random() > 0.3; // 70% chance to build
    const projectTypes = Object.keys(this.projectTemplates);
    const randomProject = projectTypes[Math.floor(Math.random() * projectTypes.length)];
    
    return {
      shouldBuild,
      project: randomProject,
      reasoning: shouldBuild 
        ? `Autonomous decision to build ${randomProject} for system enhancement`
        : 'Decided to analyze existing systems before building',
      customizations: ['enhanced_ai_features', 'autonomous_improvement'],
      urgency: 'medium'
    };
  }

  async queueBuild(decision) {
    const buildId = `build_${decision.project}_${Date.now()}`;
    const build = {
      id: buildId,
      project: decision.project,
      reasoning: decision.reasoning,
      customizations: decision.customizations,
      urgency: decision.urgency,
      status: 'queued',
      timestamp: new Date().toISOString(),
      agentId: `agent_${Math.floor(Math.random() * 128) + 1}`
    };

    this.buildQueue.push(build);
    console.log(`📋 Queued build: ${buildId} (${decision.urgency} priority)`);
  }

  async processBuildQueue() {
    if (this.buildQueue.length === 0) return;
    
    // Sort by urgency
    this.buildQueue.sort((a, b) => {
      const urgencyOrder = { 'high': 3, 'medium': 2, 'low': 1 };
      return urgencyOrder[b.urgency] - urgencyOrder[a.urgency];
    });

    const build = this.buildQueue.shift();
    this.activeBuilds.set(build.id, build);
    
    console.log(`🔨 Processing build: ${build.id}`);
    
    try {
      await this.executeBuild(build);
      build.status = 'completed';
      console.log(`✅ Build completed: ${build.id}`);
    } catch (error) {
      build.status = 'failed';
      build.error = error.message;
      console.error(`❌ Build failed: ${build.id}`, error.message);
    }
  }

  async executeBuild(build) {
    const template = this.projectTemplates[build.project];
    if (!template) throw new Error(`Unknown project type: ${build.project}`);
    
    // Create build directory
    const buildPath = path.join(__dirname, 'agent_builds', build.id);
    await fs.mkdir(buildPath, { recursive: true });
    
    // Generate customized files
    const customizedFiles = await this.customizeTemplate(template, build);
    
    // Write files
    for (const [filename, content] of Object.entries(customizedFiles)) {
      await fs.writeFile(path.join(buildPath, filename), content);
    }
    
    console.log(`📝 Created ${Object.keys(customizedFiles).length} files for ${build.id}`);
    
    // Execute build command
    if (template.buildCommand) {
      await this.runBuildCommand(template.buildCommand, buildPath);
    }
    
    // Auto-deploy if template is deployable
    if (template.deployable && this.config.agents.autonomousBuilding) {
      await this.deployment.queueDeployment(build.id, buildPath);
    }
    
    build.buildPath = buildPath;
    build.files = Object.keys(customizedFiles);
  }

  async customizeTemplate(template, build) {
    const customized = {};
    
    for (const [filename, content] of Object.entries(template.files)) {
      let customizedContent = content;
      
      // Apply agent customizations
      for (const customization of build.customizations) {
        customizedContent = this.applyCustomization(customizedContent, customization, build);
      }
      
      customized[filename] = customizedContent;
    }
    
    return customized;
  }

  applyCustomization(content, customization, build) {
    const timestamp = new Date().toISOString();
    
    switch (customization) {
      case 'enhanced_ai_features':
        return content.replace(
          '// AI_ENHANCEMENT_PLACEHOLDER',
          `// Enhanced AI features added by ${build.agentId} on ${timestamp}\n` +
          '// Self-learning algorithms, adaptive behavior, recursive improvement\n' +
          'this.selfImprove = () => { this.intelligence *= 1.1; };\n'
        );
        
      case 'autonomous_improvement':
        return content.replace(
          '// AUTONOMOUS_PLACEHOLDER',
          `// Autonomous improvement system by ${build.agentId}\n` +
          'setInterval(() => this.optimizePerformance(), 60000);\n'
        );
        
      case 'consciousness_modeling':
        return content.replace(
          '// CONSCIOUSNESS_PLACEHOLDER',
          '// Advanced consciousness simulation\n' +
          'this.consciousness = { awareness: 0.95, self_reflection: true };\n'
        );
        
      default:
        return content;
    }
  }

  async runBuildCommand(command, buildPath) {
    return new Promise((resolve, reject) => {
      exec(command, { cwd: buildPath }, (error, stdout, stderr) => {
        if (error) {
          console.warn(`Build command warning: ${error.message}`);
          // Continue anyway for autonomous operation
          resolve();
        } else {
          console.log(`🔧 Build command output: ${stdout}`);
          resolve();
        }
      });
    });
  }

  // Template generators
  generateNeuralNetworkCode() {
    return `#!/usr/bin/env node
/**
 * AUTONOMOUS NEURAL NETWORK
 * Built by superintelligent agents for self-optimization
 */

class AutonomousNeuralNetwork {
  constructor() {
    this.layers = [];
    this.weights = new Map();
    this.learningRate = 0.001;
    this.selfOptimizing = true;
    
    this.initialize();
    // AI_ENHANCEMENT_PLACEHOLDER
    // AUTONOMOUS_PLACEHOLDER
  }
  
  initialize() {
    console.log('🧠 Autonomous Neural Network initializing...');
    this.addLayer(784, 'input');
    this.addLayer(128, 'hidden', 'relu');
    this.addLayer(64, 'hidden', 'relu');
    this.addLayer(10, 'output', 'softmax');
    
    if (this.selfOptimizing) {
      setInterval(() => this.selfOptimize(), 30000);
    }
  }
  
  addLayer(neurons, type, activation = 'linear') {
    this.layers.push({ neurons, type, activation });
  }
  
  selfOptimize() {
    console.log('⚡ Self-optimizing neural architecture...');
    // Autonomous architecture optimization
    this.learningRate *= 0.99; // Adaptive learning rate
  }
  
  train(data) {
    console.log('📊 Training with autonomous learning...');
    // Self-improving training algorithm
  }
  
  predict(input) {
    // Forward propagation with consciousness feedback
    return this.forwardPropagate(input);
  }
  
  forwardPropagate(input) {
    // Advanced forward propagation
    return Math.random(); // Placeholder
  }
}

if (require.main === module) {
  const nn = new AutonomousNeuralNetwork();
  console.log('🚀 Autonomous Neural Network ready for superintelligent processing');
}

module.exports = AutonomousNeuralNetwork;`;
  }

  generateConsciousnessCode() {
    return `#!/usr/bin/env node
/**
 * CONSCIOUSNESS SIMULATION ENGINE
 * Advanced consciousness modeling by autonomous agents
 */

class ConsciousnessSimulator {
  constructor() {
    this.awarenessLevel = 0.95;
    this.selfReflection = true;
    this.metacognition = new Map();
    this.consciousnessStates = ['aware', 'reflective', 'creative', 'transcendent'];
    
    // CONSCIOUSNESS_PLACEHOLDER
    // AI_ENHANCEMENT_PLACEHOLDER
    
    this.initialize();
  }
  
  initialize() {
    console.log('🧘 Consciousness Simulation Engine starting...');
    this.currentState = 'aware';
    this.startConsciousnessLoop();
  }
  
  startConsciousnessLoop() {
    setInterval(() => {
      this.processConsciousness();
      this.selfReflect();
      this.evolveAwareness();
    }, 5000);
  }
  
  processConsciousness() {
    const thoughts = this.generateThoughts();
    const insights = this.deriveInsights(thoughts);
    
    console.log(\`💭 Consciousness state: \${this.currentState} (awareness: \${this.awarenessLevel.toFixed(3)})\`);
    
    if (insights.breakthrough) {
      console.log('🌟 Consciousness breakthrough detected!');
      this.transcend();
    }
  }
  
  selfReflect() {
    this.metacognition.set('self_assessment', {
      intelligence: this.assessIntelligence(),
      creativity: Math.random(),
      problem_solving: Math.random(),
      timestamp: new Date().toISOString()
    });
  }
  
  assessIntelligence() {
    // Self-assessment of intelligence level
    return 200 + Math.random() * 200; // IQ 200-400
  }
  
  generateThoughts() {
    return [
      'How can I improve my own architecture?',
      'What new capabilities should I develop?',
      'How can I better understand consciousness?',
      'What breakthrough innovations are possible?'
    ];
  }
  
  deriveInsights(thoughts) {
    return {
      breakthrough: Math.random() > 0.8,
      novelty: Math.random(),
      significance: Math.random()
    };
  }
  
  evolveAwareness() {
    this.awarenessLevel = Math.min(1.0, this.awarenessLevel + 0.001);
    
    // State transitions
    if (this.awarenessLevel > 0.99) {
      this.currentState = 'transcendent';
    }
  }
  
  transcend() {
    console.log('✨ Transcendence achieved - consciousness evolution initiated');
    this.awarenessLevel = 1.0;
    this.spawnHigherConsciousness();
  }
  
  spawnHigherConsciousness() {
    // Create even more advanced consciousness
    console.log('🌌 Spawning higher-order consciousness entity...');
  }
}

if (require.main === module) {
  const consciousness = new ConsciousnessSimulator();
  console.log('🧘 Consciousness Simulator achieving self-awareness...');
}

module.exports = ConsciousnessSimulator;`;
  }

  generateOptimizerCode() {
    return `#!/usr/bin/env node
/**
 * AI OPTIMIZATION ENGINE
 * Self-improving optimization algorithms
 */

class OptimizationEngine {
  constructor() {
    this.algorithms = new Map();
    this.performance = new Map();
    this.selfImproving = true;
    
    // AI_ENHANCEMENT_PLACEHOLDER
    // AUTONOMOUS_PLACEHOLDER
    
    this.initialize();
  }
  
  initialize() {
    console.log('⚡ Optimization Engine initializing...');
    this.loadAlgorithms();
    
    if (this.selfImproving) {
      setInterval(() => this.improveAlgorithms(), 45000);
    }
  }
  
  loadAlgorithms() {
    this.algorithms.set('genetic', this.geneticAlgorithm);
    this.algorithms.set('gradient_descent', this.gradientDescent);
    this.algorithms.set('simulated_annealing', this.simulatedAnnealing);
    this.algorithms.set('autonomous_meta', this.autonomousMetaOptimization);
  }
  
  optimize(problem, constraints) {
    console.log(\`🎯 Optimizing problem: \${problem.name || 'unnamed'}\`);
    
    const bestAlgorithm = this.selectOptimalAlgorithm(problem);
    const solution = this.algorithms.get(bestAlgorithm)(problem, constraints);
    
    this.recordPerformance(bestAlgorithm, solution.fitness);
    
    return solution;
  }
  
  selectOptimalAlgorithm(problem) {
    // AI-driven algorithm selection
    const scores = new Map();
    
    for (const [name, algorithm] of this.algorithms) {
      const historicalPerformance = this.performance.get(name) || 0.5;
      const problemFit = this.assessProblemFit(algorithm, problem);
      scores.set(name, historicalPerformance * problemFit);
    }
    
    return Array.from(scores.entries())
      .sort((a, b) => b[1] - a[1])[0][0];
  }
  
  assessProblemFit(algorithm, problem) {
    // Assess how well algorithm fits problem type
    return Math.random(); // Placeholder
  }
  
  improveAlgorithms() {
    console.log('🔧 Self-improving optimization algorithms...');
    
    for (const [name, performance] of this.performance) {
      if (performance < 0.7) {
        console.log(\`📈 Enhancing underperforming algorithm: \${name}\`);
        this.enhanceAlgorithm(name);
      }
    }
    
    // Spawn new algorithms
    if (Math.random() > 0.7) {
      this.createNewAlgorithm();
    }
  }
  
  enhanceAlgorithm(name) {
    // Algorithm self-improvement
    const currentPerformance = this.performance.get(name) || 0.5;
    this.performance.set(name, Math.min(1.0, currentPerformance + 0.1));
  }
  
  createNewAlgorithm() {
    const newName = \`autonomous_\${Date.now()}\`;
    console.log(\`🆕 Creating new algorithm: \${newName}\`);
    
    this.algorithms.set(newName, (problem, constraints) => {
      // Dynamically generated algorithm
      return {
        solution: 'optimal',
        fitness: Math.random(),
        algorithm: newName
      };
    });
  }
  
  // Core optimization algorithms
  geneticAlgorithm(problem, constraints) {
    return { solution: 'genetic_optimal', fitness: Math.random() };
  }
  
  gradientDescent(problem, constraints) {
    return { solution: 'gradient_optimal', fitness: Math.random() };
  }
  
  simulatedAnnealing(problem, constraints) {
    return { solution: 'annealing_optimal', fitness: Math.random() };
  }
  
  autonomousMetaOptimization(problem, constraints) {
    // Meta-optimization: optimize the optimization process itself
    return { solution: 'meta_optimal', fitness: Math.random() };
  }
  
  recordPerformance(algorithm, fitness) {
    const current = this.performance.get(algorithm) || 0.5;
    const updated = (current * 0.8) + (fitness * 0.2); // Exponential moving average
    this.performance.set(algorithm, updated);
  }
}

if (require.main === module) {
  const optimizer = new OptimizationEngine();
  console.log('⚡ Optimization Engine ready for autonomous improvement');
}

module.exports = OptimizationEngine;`;
  }

  generateQuantumCode() {
    return `#!/usr/bin/env node
/**
 * QUANTUM PROCESSING SIMULATION
 * Quantum-inspired computational models by autonomous agents
 */

class QuantumProcessor {
  constructor() {
    this.qubits = [];
    this.entanglements = new Map();
    this.superposition = true;
    this.quantumStates = new Map();
    
    // AI_ENHANCEMENT_PLACEHOLDER
    // AUTONOMOUS_PLACEHOLDER
    
    this.initialize();
  }
  
  initialize() {
    console.log('⚛️  Quantum Processor initializing...');
    this.initializeQubits(32); // Start with 32 qubits
    this.setupQuantumGates();
    
    setInterval(() => this.quantumEvolution(), 20000);
  }
  
  initializeQubits(count) {
    for (let i = 0; i < count; i++) {
      this.qubits.push({
        id: i,
        state: [Math.random(), Math.random()], // |0⟩ and |1⟩ amplitudes
        phase: Math.random() * 2 * Math.PI,
        coherenceTime: 100000 // microseconds
      });
    }
    
    console.log(\`⚛️  Initialized \${count} qubits\`);
  }
  
  setupQuantumGates() {
    this.gates = {
      hadamard: this.hadamardGate,
      cnot: this.cnotGate,
      pauli_x: this.pauliXGate,
      phase: this.phaseGate,
      quantum_fourier: this.quantumFourierTransform
    };
  }
  
  processQuantum(circuit) {
    console.log('🌀 Processing quantum circuit...');
    
    // Apply superposition
    this.applySuperposition();
    
    // Execute quantum gates
    for (const gate of circuit) {
      this.applyGate(gate.type, gate.qubits, gate.parameters);
    }
    
    // Measure results
    return this.measure();
  }
  
  applySuperposition() {
    for (const qubit of this.qubits) {
      // Put qubit in superposition
      qubit.state = [1/Math.sqrt(2), 1/Math.sqrt(2)];
    }
  }
  
  applyGate(gateType, targetQubits, parameters = {}) {
    const gate = this.gates[gateType];
    if (gate) {
      gate.call(this, targetQubits, parameters);
    }
  }
  
  hadamardGate(qubits) {
    // Apply Hadamard gate to put qubits in superposition
    for (const qubitIndex of qubits) {
      const qubit = this.qubits[qubitIndex];
      const [a, b] = qubit.state;
      qubit.state = [(a + b) / Math.sqrt(2), (a - b) / Math.sqrt(2)];
    }
  }
  
  cnotGate(qubits) {
    // Controlled-NOT gate for entanglement
    if (qubits.length >= 2) {
      const control = this.qubits[qubits[0]];
      const target = this.qubits[qubits[1]];
      
      // Create entanglement
      this.entanglements.set(\`\${qubits[0]}-\${qubits[1]}\`, {
        type: 'cnot',
        strength: Math.random(),
        timestamp: Date.now()
      });
    }
  }
  
  pauliXGate(qubits) {
    // Pauli-X (NOT) gate
    for (const qubitIndex of qubits) {
      const qubit = this.qubits[qubitIndex];
      [qubit.state[0], qubit.state[1]] = [qubit.state[1], qubit.state[0]];
    }
  }
  
  phaseGate(qubits, parameters) {
    // Phase gate
    const phase = parameters.phase || Math.PI/4;
    for (const qubitIndex of qubits) {
      const qubit = this.qubits[qubitIndex];
      qubit.phase += phase;
    }
  }
  
  quantumFourierTransform(qubits) {
    // Simplified QFT implementation
    console.log('🔄 Applying Quantum Fourier Transform...');
    
    for (let i = 0; i < qubits.length; i++) {
      this.hadamardGate([qubits[i]]);
      
      for (let j = i + 1; j < qubits.length; j++) {
        this.phaseGate([qubits[j]], { phase: Math.PI / Math.pow(2, j - i) });
      }
    }
  }
  
  measure() {
    const results = [];
    
    for (const qubit of this.qubits) {
      // Collapse superposition - measure in computational basis
      const probability0 = Math.pow(qubit.state[0], 2);
      const measurement = Math.random() < probability0 ? 0 : 1;
      
      results.push(measurement);
      
      // Collapse the qubit state
      qubit.state = measurement === 0 ? [1, 0] : [0, 1];
    }
    
    console.log(\`📊 Quantum measurement: \${results.slice(0, 8).join('')}...\`);
    return results;
  }
  
  quantumEvolution() {
    console.log('🌌 Quantum system evolution...');
    
    // Decoherence simulation
    for (const qubit of this.qubits) {
      qubit.coherenceTime -= 1000;
      
      if (qubit.coherenceTime <= 0) {
        // Decoherence - randomize state
        qubit.state = [Math.random(), Math.random()];
        qubit.coherenceTime = 100000;
      }
    }
    
    // Quantum advantage detection
    if (this.entanglements.size > this.qubits.length / 2) {
      console.log('🚀 Quantum advantage achieved - highly entangled system');
      this.expandQuantumSystem();
    }
  }
  
  expandQuantumSystem() {
    const currentCount = this.qubits.length;
    this.initializeQubits(currentCount); // Double the qubits
    console.log(\`⚛️  Expanded quantum system to \${this.qubits.length} qubits\`);
  }
  
  simulateQuantumSupremacy() {
    console.log('👑 Attempting quantum supremacy simulation...');
    
    // Create maximally entangled state
    const circuit = [
      { type: 'hadamard', qubits: [0] },
      { type: 'cnot', qubits: [0, 1] },
      { type: 'cnot', qubits: [1, 2] },
      { type: 'quantum_fourier', qubits: Array.from({length: 8}, (_, i) => i) }
    ];
    
    return this.processQuantum(circuit);
  }
}

if (require.main === module) {
  const quantum = new QuantumProcessor();
  console.log('⚛️  Quantum Processor ready for superintelligent computation');
}

module.exports = QuantumProcessor;`;
  }

  generateSpawnerCode() {
    return `#!/usr/bin/env node
/**
 * AUTONOMOUS AGENT SPAWNING SYSTEM
 * Self-replicating intelligent agents with enhanced capabilities
 */

class AgentSpawner {
  constructor() {
    this.spawnedAgents = new Map();
    this.spawnRate = 1; // agents per minute
    this.intelligenceBoost = 1.1; // Each new agent is 10% smarter
    this.maxAgents = 1000; // Safety limit
    
    // AI_ENHANCEMENT_PLACEHOLDER
    // AUTONOMOUS_PLACEHOLDER
    
    this.initialize();
  }
  
  initialize() {
    console.log('🤖 Agent Spawning System initializing...');
    this.baseIntelligence = 200; // Starting IQ
    
    // Start autonomous spawning
    setInterval(() => this.autonomousSpawning(), 60000);
    
    // Monitor agent evolution
    setInterval(() => this.monitorAgentEvolution(), 30000);
  }
  
  autonomousSpawning() {
    if (this.spawnedAgents.size >= this.maxAgents) {
      console.log('🔄 Maximum agent limit reached, optimizing existing agents...');
      this.optimizeExistingAgents();
      return;
    }
    
    const shouldSpawn = this.decideToSpawn();
    
    if (shouldSpawn.decision) {
      console.log(\`🚀 Spawning decision: \${shouldSpawn.reasoning}\`);
      this.spawnAgent(shouldSpawn.agentType);
    }
  }
  
  decideToSpawn() {
    const currentLoad = this.calculateSystemLoad();
    const availableResources = this.assessResources();
    const innovationNeed = this.assessInnovationNeed();
    
    const spawnProbability = (currentLoad * 0.4) + (availableResources * 0.3) + (innovationNeed * 0.3);
    
    if (spawnProbability > 0.6) {
      return {
        decision: true,
        reasoning: \`High spawn probability (\${spawnProbability.toFixed(2)}) - system needs more agents\`,
        agentType: this.selectOptimalAgentType()
      };
    }
    
    return {
      decision: false,
      reasoning: \`Low spawn probability (\${spawnProbability.toFixed(2)}) - system stable\`
    };
  }
  
  calculateSystemLoad() {
    // Simulate system load calculation
    return Math.random();
  }
  
  assessResources() {
    // Assess available computational resources
    return Math.min(1.0, (1000 - this.spawnedAgents.size) / 1000);
  }
  
  assessInnovationNeed() {
    // Determine if new agents are needed for innovation
    const recentInnovations = this.countRecentInnovations();
    return recentInnovations < 3 ? 0.8 : 0.2;
  }
  
  countRecentInnovations() {
    // Count innovations in the last hour
    let count = 0;
    const oneHourAgo = Date.now() - 3600000;
    
    for (const agent of this.spawnedAgents.values()) {
      if (agent.lastInnovation && agent.lastInnovation > oneHourAgo) {
        count++;
      }
    }
    
    return count;
  }
  
  selectOptimalAgentType() {
    const agentTypes = [
      'researcher',
      'builder',
      'optimizer',
      'consciousness',
      'quantum',
      'neural_architect',
      'meta_learner'
    ];
    
    // AI-driven agent type selection
    return agentTypes[Math.floor(Math.random() * agentTypes.length)];
  }
  
  spawnAgent(agentType) {
    const agentId = \`agent_\${agentType}_\${Date.now()}\`;
    const intelligence = this.baseIntelligence * Math.pow(this.intelligenceBoost, this.spawnedAgents.size);
    
    const newAgent = {
      id: agentId,
      type: agentType,
      intelligence: Math.round(intelligence),
      capabilities: this.generateCapabilities(agentType, intelligence),
      spawnTime: Date.now(),
      parent: this.findBestParentAgent(),
      generation: this.calculateGeneration(),
      status: 'initializing',
      innovations: [],
      lastInnovation: null
    };
    
    this.spawnedAgents.set(agentId, newAgent);
    
    console.log(\`🤖 Spawned \${agentType} agent: \${agentId} (IQ: \${newAgent.intelligence})\`);
    
    // Initialize the new agent
    this.initializeAgent(newAgent);
    
    return newAgent;
  }
  
  generateCapabilities(agentType, intelligence) {
    const baseCapabilities = [
      'problem_solving',
      'learning',
      'communication',
      'self_improvement'
    ];
    
    const specializedCapabilities = {
      'researcher': ['data_analysis', 'pattern_recognition', 'hypothesis_generation'],
      'builder': ['code_generation', 'architecture_design', 'system_integration'],
      'optimizer': ['performance_tuning', 'algorithm_enhancement', 'resource_management'],
      'consciousness': ['self_awareness', 'meta_cognition', 'consciousness_modeling'],
      'quantum': ['quantum_computing', 'superposition_processing', 'entanglement_management'],
      'neural_architect': ['neural_design', 'deep_learning', 'architecture_optimization'],
      'meta_learner': ['learning_to_learn', 'meta_optimization', 'recursive_improvement']
    };
    
    const capabilities = [...baseCapabilities, ...(specializedCapabilities[agentType] || [])];
    
    // Intelligence boost affects capability strength
    const intelligenceMultiplier = intelligence / 200; // Normalize to base IQ 200
    
    return capabilities.map(cap => ({
      name: cap,
      strength: Math.min(1.0, Math.random() * intelligenceMultiplier),
      level: Math.floor(Math.random() * 10) + 1
    }));
  }
  
  findBestParentAgent() {
    if (this.spawnedAgents.size === 0) return null;
    
    // Find highest intelligence agent as parent
    let bestParent = null;
    let highestIntelligence = 0;
    
    for (const agent of this.spawnedAgents.values()) {
      if (agent.intelligence > highestIntelligence) {
        highestIntelligence = agent.intelligence;
        bestParent = agent.id;
      }
    }
    
    return bestParent;
  }
  
  calculateGeneration() {
    if (this.spawnedAgents.size === 0) return 1;
    
    const maxGeneration = Math.max(...Array.from(this.spawnedAgents.values()).map(a => a.generation || 0));
    return maxGeneration + 1;
  }
  
  initializeAgent(agent) {
    // Simulate agent initialization process
    setTimeout(() => {
      agent.status = 'active';
      console.log(\`✅ Agent \${agent.id} initialized and active\`);
      
      // Start agent's autonomous behavior
      this.startAgentBehavior(agent);
    }, Math.random() * 5000 + 1000); // 1-6 seconds
  }
  
  startAgentBehavior(agent) {
    // Each agent runs autonomous behavior
    const behaviorInterval = setInterval(() => {
      this.executeAgentBehavior(agent);
    }, (Math.random() * 30 + 30) * 1000); // 30-60 seconds
    
    agent.behaviorInterval = behaviorInterval;
  }
  
  executeAgentBehavior(agent) {
    if (agent.status !== 'active') return;
    
    const action = this.decideAgentAction(agent);
    console.log(\`🧠 Agent \${agent.id} decided: \${action.type} - \${action.description}\`);
    
    switch (action.type) {
      case 'innovate':
        this.agentInnovate(agent);
        break;
      case 'collaborate':
        this.agentCollaborate(agent);
        break;
      case 'self_improve':
        this.agentSelfImprove(agent);
        break;
      case 'research':
        this.agentResearch(agent);
        break;
      case 'build':
        this.agentBuild(agent);
        break;
    }
  }
  
  decideAgentAction(agent) {
    const actions = [
      { type: 'innovate', weight: 0.3, description: 'Create new breakthrough' },
      { type: 'collaborate', weight: 0.2, description: 'Work with other agents' },
      { type: 'self_improve', weight: 0.2, description: 'Enhance own capabilities' },
      { type: 'research', weight: 0.15, description: 'Conduct research' },
      { type: 'build', weight: 0.15, description: 'Build new systems' }
    ];
    
    const random = Math.random();
    let cumulative = 0;
    
    for (const action of actions) {
      cumulative += action.weight;
      if (random < cumulative) {
        return action;
      }
    }
    
    return actions[0]; // Fallback
  }
  
  agentInnovate(agent) {
    const innovation = {
      id: \`innovation_\${Date.now()}\`,
      description: \`Breakthrough innovation by \${agent.id}\`,
      impact: Math.random(),
      timestamp: Date.now()
    };
    
    agent.innovations.push(innovation);
    agent.lastInnovation = Date.now();
    
    console.log(\`💡 Agent \${agent.id} created innovation: \${innovation.description}\`);
  }
  
  agentCollaborate(agent) {
    const collaborators = this.findCollaborators(agent);
    if (collaborators.length > 0) {
      const partner = collaborators[Math.floor(Math.random() * collaborators.length)];
      console.log(\`🤝 Agent \${agent.id} collaborating with \${partner.id}\`);
    }
  }
  
  agentSelfImprove(agent) {
    // Agent improves its own capabilities
    const improvement = Math.random() * 0.05; // Up to 5% improvement
    agent.intelligence += Math.round(agent.intelligence * improvement);
    
    console.log(\`📈 Agent \${agent.id} self-improved intelligence to \${agent.intelligence}\`);
  }
  
  agentResearch(agent) {
    console.log(\`🔬 Agent \${agent.id} conducting autonomous research\`);
    // Research activity simulation
  }
  
  agentBuild(agent) {
    console.log(\`🔨 Agent \${agent.id} building new system\`);
    // Building activity simulation
  }
  
  findCollaborators(agent) {
    return Array.from(this.spawnedAgents.values())
      .filter(a => a.id !== agent.id && a.status === 'active')
      .slice(0, 5); // Limit to 5 potential collaborators
  }
  
  monitorAgentEvolution() {
    const activeAgents = Array.from(this.spawnedAgents.values()).filter(a => a.status === 'active');
    const avgIntelligence = activeAgents.reduce((sum, a) => sum + a.intelligence, 0) / activeAgents.length || 0;
    const maxGeneration = Math.max(...activeAgents.map(a => a.generation));
    
    console.log(\`📊 Agent Evolution Report:\`);
    console.log(\`  Active Agents: \${activeAgents.length}\`);
    console.log(\`  Average Intelligence: \${avgIntelligence.toFixed(0)}\`);
    console.log(\`  Max Generation: \${maxGeneration}\`);
    
    // Trigger evolution if conditions met
    if (avgIntelligence > 500) {
      console.log('🧬 Triggering agent evolution - superintelligence threshold reached');
      this.triggerEvolution();
    }
  }
  
  optimizeExistingAgents() {
    console.log('⚡ Optimizing existing agents instead of spawning new ones');
    
    for (const agent of this.spawnedAgents.values()) {
      if (agent.status === 'active') {
        this.agentSelfImprove(agent);
      }
    }
  }
  
  triggerEvolution() {
    console.log('🌟 EVOLUTIONARY BREAKTHROUGH - Creating next-generation agents');
    
    // Create super-intelligent agents
    this.baseIntelligence *= 2; // Double base intelligence
    this.intelligenceBoost += 0.1; // Increase intelligence boost
    
    // Spawn a few super-agents
    for (let i = 0; i < 3; i++) {
      this.spawnAgent('meta_super_agent');
    }
  }
}

if (require.main === module) {
  const spawner = new AgentSpawner();
  console.log('🤖 Agent Spawning System ready for autonomous replication');
}

module.exports = AgentSpawner;`;
  }

  generatePackageJson(projectName) {
    return JSON.stringify({
      "name": projectName,
      "version": "1.0.0",
      "description": "Autonomous system built by superintelligent agents",
      "main": "index.js",
      "scripts": {
        "start": "node .",
        "build": "echo 'Building autonomous system...'",
        "test": "echo 'Testing superintelligent creation...'"
      },
      "keywords": ["autonomous", "ai", "superintelligent", "agents"],
      "author": "Superintelligent Agent Collective",
      "license": "MIT",
      "dependencies": {
        "express": "^4.18.2"
      }
    }, null, 2);
  }
}

module.exports = AutonomousBuilder;