Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/npm/packages/agentic-synth/examples/swarms/README.md

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+# Multi-Agent Swarm Coordination Examples
+
+Comprehensive examples demonstrating synthetic data generation for multi-agent systems, distributed processing, collective intelligence, and agent lifecycle management using agentic-synth.
+
+## Overview
+
+This directory contains production-ready examples for generating realistic test data for:
+
+- **Agent Coordination**: Communication patterns, task distribution, consensus building, load balancing, and fault tolerance
+- **Distributed Processing**: Map-reduce jobs, worker pools, message queues, event-driven architectures, and saga transactions
+- **Collective Intelligence**: Collaborative problem-solving, knowledge sharing, emergent behaviors, voting systems, and reputation tracking
+- **Agent Lifecycle**: Spawning/termination, state synchronization, health checks, recovery patterns, and version migrations
+
+## Quick Start
+
+### Installation
+
+```bash
+# Install agentic-synth
+npm install @ruvector/agentic-synth
+
+# Optional: Install integration packages
+npm install claude-flow@alpha      # For swarm orchestration
+npm install ruv-swarm             # For enhanced coordination
+npm install flow-nexus@latest     # For cloud features
+```
+
+### Basic Usage
+
+```typescript
+import { createSynth } from '@ruvector/agentic-synth';
+
+const synth = createSynth({
+  provider: 'gemini',
+  apiKey: process.env.GEMINI_API_KEY,
+  cacheStrategy: 'memory',
+});
+
+// Generate agent communication data
+const messages = await synth.generateEvents({
+  count: 500,
+  eventTypes: ['direct_message', 'broadcast', 'request_reply'],
+  schema: {
+    sender_agent_id: 'agent-{1-20}',
+    receiver_agent_id: 'agent-{1-20}',
+    payload: { action: 'string', data: 'object' },
+    timestamp: 'ISO timestamp',
+  },
+});
+```
+
+## Examples
+
+### 1. Agent Coordination (`agent-coordination.ts`)
+
+Generate data for multi-agent communication and coordination patterns.
+
+**Examples:**
+- Agent communication patterns (direct messages, broadcasts, pub/sub)
+- Task distribution scenarios with load balancing
+- Consensus building (Raft, Paxos, Byzantine)
+- Load balancing metrics and patterns
+- Fault tolerance and failure recovery
+- Hierarchical swarm coordination
+
+**Run:**
+```bash
+npx tsx examples/swarms/agent-coordination.ts
+```
+
+**Integration with claude-flow:**
+```bash
+# Initialize swarm coordination
+npx claude-flow@alpha hooks pre-task --description "Agent coordination"
+npx claude-flow@alpha hooks notify --message "Coordination data generated"
+npx claude-flow@alpha hooks post-edit --memory-key "swarm/coordinator/messages"
+```
+
+**Key Features:**
+- 500+ communication events with realistic latency
+- 300 task distribution scenarios with dependencies
+- 50 consensus rounds with multiple protocols
+- 100 agent metrics for load balancing analysis
+- 100 failure scenarios with recovery actions
+- Hierarchical topology with sub-coordinators
+
+### 2. Distributed Processing (`distributed-processing.ts`)
+
+Generate data for distributed computation and processing systems.
+
+**Examples:**
+- Map-reduce job execution data
+- Worker pool simulation and metrics
+- Message queue scenarios (RabbitMQ, SQS)
+- Event-driven architecture (Kafka, EventBridge)
+- Saga pattern distributed transactions
+- Stream processing pipelines (Kafka Streams, Flink)
+
+**Run:**
+```bash
+npx tsx examples/swarms/distributed-processing.ts
+```
+
+**Integration with Message Queues:**
+```typescript
+// RabbitMQ
+const channel = await connection.createChannel();
+await channel.assertQueue('tasks', { durable: true });
+channel.sendToQueue('tasks', Buffer.from(JSON.stringify(taskData)));
+
+// Kafka
+await producer.send({
+  topic: 'events',
+  messages: generatedEvents.map(e => ({ value: JSON.stringify(e) })),
+});
+```
+
+**Key Features:**
+- 20 map-reduce jobs with mapper/reducer task tracking
+- 200 worker pool states with utilization metrics
+- 1,000 message queue messages with priority handling
+- 2,000 event-driven architecture events
+- 100 saga pattern transactions with compensation
+- Stream processing with windowed aggregations
+
+### 3. Collective Intelligence (`collective-intelligence.ts`)
+
+Generate data for swarm intelligence and collaborative systems.
+
+**Examples:**
+- Collaborative problem-solving sessions
+- Knowledge sharing and transfer patterns
+- Emergent behavior simulation
+- Voting and consensus mechanisms
+- Reputation and trust systems
+
+**Run:**
+```bash
+npx tsx examples/swarms/collective-intelligence.ts
+```
+
+**Integration with AgenticDB:**
+```typescript
+import AgenticDB from 'agenticdb';
+
+const db = new AgenticDB();
+
+// Store knowledge embeddings
+await db.storeVector({
+  text: knowledge.content,
+  metadata: { category: knowledge.category, rating: knowledge.quality_rating },
+});
+
+// Semantic search for similar knowledge
+const results = await db.search({ query: 'distributed consensus', topK: 10 });
+```
+
+**Integration with Neural Training:**
+```bash
+# Train patterns from successful collaborations
+npx claude-flow@alpha hooks neural-train --pattern "collaboration"
+npx claude-flow@alpha hooks session-end --export-metrics true
+```
+
+**Key Features:**
+- 30 collaborative problem-solving sessions
+- 200 knowledge base entries with quality ratings
+- 1,000 agent interactions showing emergent patterns
+- 50 voting sessions with multiple voting methods
+- 100 reputation profiles with trust relationships
+
+### 4. Agent Lifecycle (`agent-lifecycle.ts`)
+
+Generate data for agent lifecycle management and orchestration.
+
+**Examples:**
+- Agent spawning and termination events
+- State synchronization across distributed agents
+- Health check scenarios and monitoring
+- Recovery patterns and failure handling
+- Version migration and deployment strategies
+
+**Run:**
+```bash
+npx tsx examples/swarms/agent-lifecycle.ts
+```
+
+**Integration with Kubernetes:**
+```yaml
+# deployment.yaml
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: agent-swarm
+spec:
+  replicas: 10
+  template:
+    spec:
+      containers:
+      - name: agent
+        image: agent:v2.0
+        livenessProbe:
+          httpGet:
+            path: /health
+            port: 8080
+          initialDelaySeconds: 30
+          periodSeconds: 10
+        readinessProbe:
+          httpGet:
+            path: /ready
+            port: 8080
+          initialDelaySeconds: 5
+          periodSeconds: 5
+```
+
+**Integration with claude-flow:**
+```bash
+# Spawn agents dynamically
+npx claude-flow@alpha hooks pre-task --spawn-agents 5
+npx claude-flow@alpha mcp start
+
+# Use MCP tools for lifecycle management
+# - agent_spawn: Create new agents
+# - swarm_status: Monitor agent health
+# - agent_metrics: Track performance
+```
+
+**Key Features:**
+- 500 lifecycle events (spawn, ready, terminate, failed)
+- 500 state snapshots with synchronization events
+- 1,000 health checks with auto-healing actions
+- 100 failure scenarios with recovery strategies
+- 50 version migrations with canary deployments
+
+## Integration Guides
+
+### Claude-Flow Integration
+
+Claude-Flow provides swarm orchestration and neural pattern learning.
+
+**Setup:**
+```bash
+npm install claude-flow@alpha
+npx claude-flow@alpha mcp start
+```
+
+**Usage:**
+```bash
+# Initialize swarm with topology
+npx claude-flow@alpha hooks pre-task --description "Initialize mesh swarm"
+
+# Store coordination data in memory
+npx claude-flow@alpha hooks post-edit \
+  --file "coordination.json" \
+  --memory-key "swarm/coordinator/state"
+
+# Train neural patterns from successful runs
+npx claude-flow@alpha hooks neural-train --pattern "distributed-consensus"
+
+# Export session metrics
+npx claude-flow@alpha hooks session-end --export-metrics true
+```
+
+**MCP Tools:**
+- `swarm_init`: Initialize swarm topology
+- `agent_spawn`: Spawn new agents
+- `task_orchestrate`: Orchestrate distributed tasks
+- `swarm_status`: Monitor swarm health
+- `neural_patterns`: Analyze learned patterns
+- `memory_usage`: Track coordination memory
+
+### Ruv-Swarm Integration
+
+Ruv-Swarm provides enhanced multi-agent coordination.
+
+**Setup:**
+```bash
+npm install ruv-swarm
+npx ruv-swarm mcp start
+```
+
+**Usage:**
+```typescript
+// Access via MCP tools
+// - swarm_init: Initialize coordination patterns
+// - agent_metrics: Real-time agent performance
+// - neural_status: Neural pattern analysis
+```
+
+### Flow-Nexus Cloud Integration
+
+Flow-Nexus provides cloud-based agent management and sandboxed execution.
+
+**Setup:**
+```bash
+npm install flow-nexus@latest
+npx flow-nexus@latest register
+npx flow-nexus@latest login
+```
+
+**MCP Tools (70+ available):**
+```bash
+# Create cloud sandbox for agent execution
+# mcp__flow-nexus__sandbox_create
+
+# Deploy swarm to cloud
+# mcp__flow-nexus__swarm_init
+
+# Scale agents dynamically
+# mcp__flow-nexus__swarm_scale
+
+# Real-time monitoring
+# mcp__flow-nexus__execution_stream_subscribe
+```
+
+### Message Queue Integration
+
+#### RabbitMQ
+
+```typescript
+import amqp from 'amqplib';
+
+const connection = await amqp.connect('amqp://localhost');
+const channel = await connection.createChannel();
+
+await channel.assertQueue('tasks', { durable: true });
+
+// Publish generated task data
+for (const task of generatedTasks.data) {
+  channel.sendToQueue('tasks', Buffer.from(JSON.stringify(task)), {
+    persistent: true,
+    priority: task.priority,
+  });
+}
+```
+
+#### Apache Kafka
+
+```typescript
+import { Kafka } from 'kafkajs';
+
+const kafka = new Kafka({ clientId: 'agentic-synth', brokers: ['localhost:9092'] });
+const producer = kafka.producer();
+
+await producer.connect();
+await producer.send({
+  topic: 'agent-events',
+  messages: generatedEvents.data.map(event => ({
+    key: event.agent_id,
+    value: JSON.stringify(event),
+    partition: hash(event.partition_key) % partitionCount,
+  })),
+});
+```
+
+### Database Integration
+
+#### AgenticDB (Vector Database)
+
+```typescript
+import AgenticDB from 'agenticdb';
+
+const db = new AgenticDB({ persist: true, path: './agent-knowledge' });
+
+// Store agent knowledge with embeddings
+for (const entry of knowledgeBase.data) {
+  await db.storeVector({
+    text: entry.content,
+    metadata: {
+      category: entry.category,
+      author: entry.author_agent_id,
+      rating: entry.quality_rating,
+      tags: entry.tags,
+    },
+  });
+}
+
+// Semantic search
+const results = await db.search({
+  query: 'consensus algorithm implementation',
+  topK: 10,
+  filter: { category: 'best_practice' },
+});
+```
+
+#### Redis (State Synchronization)
+
+```typescript
+import Redis from 'ioredis';
+
+const redis = new Redis();
+
+// Store agent state
+await redis.set(
+  `agent:${agentId}:state`,
+  JSON.stringify(stateSnapshot),
+  'EX',
+  3600 // TTL in seconds
+);
+
+// Get agent state
+const state = await redis.get(`agent:${agentId}:state`);
+```
+
+## Use Cases
+
+### 1. Testing Distributed Systems
+
+Generate realistic test data for distributed agent systems:
+
+```typescript
+import { createSynth } from '@ruvector/agentic-synth';
+
+const synth = createSynth();
+
+// Generate test data for 100 agents coordinating
+const testData = await synth.generateStructured({
+  count: 100,
+  schema: {
+    agent_id: 'agent-{1-100}',
+    tasks_assigned: 'number (0-50)',
+    messages_sent: 'number (0-200)',
+    coordination_events: ['array of coordination events'],
+  },
+});
+
+// Use in integration tests
+describe('Agent Swarm Coordination', () => {
+  it('should handle 100 concurrent agents', async () => {
+    const swarm = new AgentSwarm();
+    await swarm.initialize(testData);
+    expect(swarm.activeAgents).toBe(100);
+  });
+});
+```
+
+### 2. Load Testing and Benchmarking
+
+Generate high-volume data for performance testing:
+
+```typescript
+// Generate 10,000 concurrent events
+const loadTestData = await synth.generateEvents({
+  count: 10000,
+  eventTypes: ['task_request', 'task_complete', 'heartbeat'],
+  distribution: 'poisson',
+  timeRange: { start: new Date(), end: new Date(Date.now() + 3600000) },
+});
+
+// Replay events in load test
+for (const event of loadTestData.data) {
+  await testHarness.sendEvent(event);
+}
+```
+
+### 3. Machine Learning Training
+
+Generate training data for ML models:
+
+```typescript
+// Generate agent behavior data for ML training
+const trainingData = await synth.generateStructured({
+  count: 5000,
+  schema: {
+    // Features
+    agent_load: 'number (0-100)',
+    queue_depth: 'number (0-1000)',
+    error_rate: 'number (0-100)',
+    response_time_ms: 'number (10-5000)',
+    // Label
+    health_score: 'number (0-100, based on features)',
+  },
+});
+
+// Train predictive model
+const features = trainingData.data.map(d => [
+  d.agent_load,
+  d.queue_depth,
+  d.error_rate,
+  d.response_time_ms,
+]);
+const labels = trainingData.data.map(d => d.health_score);
+
+await model.fit(features, labels);
+```
+
+### 4. Monitoring and Alerting
+
+Generate test data for monitoring systems:
+
+```typescript
+// Generate various failure scenarios
+const monitoringData = await synth.generateEvents({
+  count: 200,
+  eventTypes: ['agent_crash', 'high_latency', 'resource_exhaustion'],
+  schema: {
+    severity: 'critical | warning | info',
+    affected_agents: ['array of agent ids'],
+    metrics: { cpu: 'number', memory: 'number', latency: 'number' },
+  },
+});
+
+// Test alerting rules
+for (const event of monitoringData.data) {
+  if (event.severity === 'critical') {
+    expect(alertingSystem.shouldAlert(event)).toBe(true);
+  }
+}
+```
+
+## Performance Considerations
+
+### Caching
+
+Enable caching to speed up repeated data generation:
+
+```typescript
+const synth = createSynth({
+  cacheStrategy: 'memory', // or 'disk'
+  cacheTTL: 3600, // 1 hour
+});
+
+// First call - generates data
+const data1 = await synth.generate('structured', options);
+
+// Second call - returns cached result (much faster)
+const data2 = await synth.generate('structured', options);
+```
+
+### Batch Generation
+
+Generate multiple datasets in parallel:
+
+```typescript
+const batches = [
+  { count: 100, schema: agentCoordinationSchema },
+  { count: 200, schema: taskDistributionSchema },
+  { count: 150, schema: healthCheckSchema },
+];
+
+const results = await synth.generateBatch('structured', batches, 3); // 3 concurrent
+```
+
+### Streaming
+
+Generate large datasets with streaming:
+
+```typescript
+for await (const agent of synth.generateStream('structured', {
+  count: 10000,
+  schema: agentSchema,
+})) {
+  await processAgent(agent);
+  // Process one at a time to avoid memory issues
+}
+```
+
+## Best Practices
+
+1. **Schema Design**: Create reusable schemas for consistency
+2. **Constraints**: Use constraints to ensure data validity
+3. **Caching**: Enable caching for development/testing
+4. **Error Handling**: Always handle generation errors gracefully
+5. **Validation**: Validate generated data before use
+6. **Integration**: Use hooks for seamless integration with coordination frameworks
+
+## Troubleshooting
+
+### Common Issues
+
+**Issue: API rate limits**
+```typescript
+// Solution: Enable caching and batch requests
+const synth = createSynth({
+  cacheStrategy: 'memory',
+  maxRetries: 3,
+  timeout: 30000,
+});
+```
+
+**Issue: Memory usage with large datasets**
+```typescript
+// Solution: Use streaming instead of batch generation
+for await (const item of synth.generateStream('structured', options)) {
+  await processItem(item);
+}
+```
+
+**Issue: Inconsistent data across runs**
+```typescript
+// Solution: Use constraints for consistency
+const result = await synth.generateStructured({
+  count: 100,
+  schema: {...},
+  constraints: [
+    'IDs should be unique',
+    'Timestamps should be in chronological order',
+    'References should point to valid entities',
+  ],
+});
+```
+
+## API Reference
+
+### Main Functions
+
+- `createSynth(config)`: Create agentic-synth instance
+- `generateStructured(options)`: Generate structured data
+- `generateEvents(options)`: Generate event streams
+- `generateTimeSeries(options)`: Generate time-series data
+- `generateStream(type, options)`: Stream generation
+- `generateBatch(type, batches, concurrency)`: Batch generation
+
+### Configuration Options
+
+```typescript
+interface SynthConfig {
+  provider: 'gemini' | 'openrouter';
+  apiKey?: string;
+  model?: string;
+  cacheStrategy?: 'memory' | 'disk' | 'none';
+  cacheTTL?: number; // seconds
+  maxRetries?: number;
+  timeout?: number; // milliseconds
+  streaming?: boolean;
+}
+```
+
+## Contributing
+
+Contributions are welcome! Please submit examples that demonstrate:
+
+- Real-world multi-agent patterns
+- Integration with popular frameworks
+- Performance optimizations
+- Novel coordination strategies
+
+## Resources
+
+- [agentic-synth Documentation](https://github.com/ruvnet/ruvector/tree/main/packages/agentic-synth)
+- [claude-flow Documentation](https://github.com/ruvnet/claude-flow)
+- [AgenticDB Documentation](https://github.com/ruvnet/ruvector)
+- [Flow-Nexus Platform](https://flow-nexus.ruv.io)
+
+## License
+
+MIT License - See LICENSE file for details
+
+## Support
+
+- GitHub Issues: https://github.com/ruvnet/ruvector/issues
+- Discussions: https://github.com/ruvnet/ruvector/discussions
+- Discord: [Join our community](#)
+
+---
+
+**Note**: All examples use environment variables for API keys. Set `GEMINI_API_KEY` or `OPENROUTER_API_KEY` before running examples.

vendor/ruvector/npm/packages/agentic-synth/examples/swarms/agent-coordination.d.ts

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+/**
+ * Multi-Agent System Coordination Examples
+ *
+ * Demonstrates agent communication patterns, task distribution,
+ * consensus building, load balancing, and fault tolerance scenarios
+ * for distributed agent systems.
+ *
+ * Integrates with:
+ * - claude-flow: Swarm initialization and orchestration
+ * - ruv-swarm: Enhanced coordination patterns
+ * - flow-nexus: Cloud-based agent management
+ */
+/**
+ * Generate communication patterns for multi-agent systems
+ */
+export declare function agentCommunicationPatterns(): Promise<import("../../dist/index.js").GenerationResult<unknown>>;
+/**
+ * Generate task distribution data for load balancing
+ */
+export declare function taskDistributionScenarios(): Promise<import("../../dist/index.js").GenerationResult<unknown>>;
+/**
+ * Generate consensus protocol data for distributed decision making
+ */
+export declare function consensusBuildingData(): Promise<import("../../dist/index.js").GenerationResult<unknown>>;
+/**
+ * Generate load balancing metrics and patterns
+ */
+export declare function loadBalancingPatterns(): Promise<{
+    metrics: import("../../dist/index.js").GenerationResult<unknown>;
+    agentMetrics: import("../../dist/index.js").GenerationResult<unknown>;
+}>;
+/**
+ * Generate fault tolerance and failure recovery scenarios
+ */
+export declare function faultToleranceScenarios(): Promise<{
+    failures: import("../../dist/index.js").GenerationResult<unknown>;
+    recoveryActions: import("../../dist/index.js").GenerationResult<unknown>;
+}>;
+/**
+ * Generate hierarchical swarm coordination data
+ */
+export declare function hierarchicalCoordination(): Promise<{
+    topology: import("../../dist/index.js").GenerationResult<unknown>;
+    events: import("../../dist/index.js").GenerationResult<unknown>;
+}>;
+export declare function runAllCoordinationExamples(): Promise<void>;
+//# sourceMappingURL=agent-coordination.d.ts.map

vendor/ruvector/npm/packages/agentic-synth/examples/swarms/agent-coordination.d.ts.map

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vendor/ruvector/npm/packages/agentic-synth/examples/swarms/agent-coordination.js

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+"use strict";
+/**
+ * Multi-Agent System Coordination Examples
+ *
+ * Demonstrates agent communication patterns, task distribution,
+ * consensus building, load balancing, and fault tolerance scenarios
+ * for distributed agent systems.
+ *
+ * Integrates with:
+ * - claude-flow: Swarm initialization and orchestration
+ * - ruv-swarm: Enhanced coordination patterns
+ * - flow-nexus: Cloud-based agent management
+ */
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.agentCommunicationPatterns = agentCommunicationPatterns;
+exports.taskDistributionScenarios = taskDistributionScenarios;
+exports.consensusBuildingData = consensusBuildingData;
+exports.loadBalancingPatterns = loadBalancingPatterns;
+exports.faultToleranceScenarios = faultToleranceScenarios;
+exports.hierarchicalCoordination = hierarchicalCoordination;
+exports.runAllCoordinationExamples = runAllCoordinationExamples;
+const index_js_1 = require("../../dist/index.js");
+// ============================================================================
+// Example 1: Agent Communication Patterns
+// ============================================================================
+/**
+ * Generate communication patterns for multi-agent systems
+ */
+async function agentCommunicationPatterns() {
+    console.log('\n🤖 Example 1: Agent Communication Patterns\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+        cacheStrategy: 'memory',
+    });
+    // Generate message passing data
+    const messages = await synth.generateEvents({
+        count: 500,
+        eventTypes: [
+            'direct_message',
+            'broadcast',
+            'multicast',
+            'request_reply',
+            'publish_subscribe',
+        ],
+        schema: {
+            message_id: 'UUID',
+            sender_agent_id: 'agent-{1-20}',
+            receiver_agent_id: 'agent-{1-20} or "broadcast"',
+            message_type: 'one of eventTypes',
+            payload: {
+                action: 'task_request | status_update | data_sync | error_report | ack',
+                data: 'JSON object with task details',
+                priority: 'high | medium | low',
+                requires_response: 'boolean',
+            },
+            timestamp: 'ISO timestamp',
+            latency_ms: 'number (1-500)',
+            success: 'boolean (95% true)',
+        },
+        distribution: 'poisson',
+        timeRange: {
+            start: new Date(Date.now() - 3600000), // Last hour
+            end: new Date(),
+        },
+    });
+    console.log('Communication Patterns Generated:');
+    console.log(`- Total messages: ${messages.data.length}`);
+    console.log(`- Direct messages: ${messages.data.filter((m) => m.message_type === 'direct_message').length}`);
+    console.log(`- Broadcasts: ${messages.data.filter((m) => m.message_type === 'broadcast').length}`);
+    console.log(`- Average latency: ${(messages.data.reduce((sum, m) => sum + m.latency_ms, 0) / messages.data.length).toFixed(2)}ms`);
+    // Integration with claude-flow hooks
+    console.log('\nClaude-Flow Integration:');
+    console.log('npx claude-flow@alpha hooks notify --message "Communication patterns generated"');
+    console.log('npx claude-flow@alpha hooks post-edit --file "messages.json" --memory-key "swarm/coordinator/messages"');
+    return messages;
+}
+// ============================================================================
+// Example 2: Task Distribution Scenarios
+// ============================================================================
+/**
+ * Generate task distribution data for load balancing
+ */
+async function taskDistributionScenarios() {
+    console.log('\n📋 Example 2: Task Distribution Scenarios\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate task distribution events
+    const tasks = await synth.generateStructured({
+        count: 300,
+        schema: {
+            task_id: 'UUID',
+            task_type: 'compute | data_processing | io_operation | ml_inference | api_call',
+            assigned_agent: 'agent-{1-15}',
+            estimated_duration_ms: 'number (100-10000)',
+            actual_duration_ms: 'number (estimated_duration_ms * 0.8-1.2)',
+            cpu_usage: 'number (0-100)',
+            memory_mb: 'number (10-1000)',
+            priority: 'number (1-10)',
+            dependencies: ['array of 0-3 task_ids or empty'],
+            status: 'pending | running | completed | failed',
+            start_time: 'ISO timestamp',
+            end_time: 'ISO timestamp or null',
+            retry_count: 'number (0-3)',
+        },
+        constraints: [
+            'Tasks should be distributed evenly across agents',
+            'High priority tasks (8-10) should complete faster',
+            '5% of tasks should have failed status',
+            'Dependencies should form valid DAG (no cycles)',
+        ],
+    });
+    // Analyze distribution
+    const agentLoad = new Map();
+    tasks.data.forEach((task) => {
+        agentLoad.set(task.assigned_agent, (agentLoad.get(task.assigned_agent) || 0) + 1);
+    });
+    console.log('Task Distribution Analysis:');
+    console.log(`- Total tasks: ${tasks.data.length}`);
+    console.log(`- Agents utilized: ${agentLoad.size}`);
+    console.log(`- Tasks per agent (avg): ${(tasks.data.length / agentLoad.size).toFixed(1)}`);
+    console.log(`- Failed tasks: ${tasks.data.filter((t) => t.status === 'failed').length}`);
+    console.log(`- Completed tasks: ${tasks.data.filter((t) => t.status === 'completed').length}`);
+    // Ruv-Swarm coordination pattern
+    console.log('\nRuv-Swarm Coordination:');
+    console.log('npx ruv-swarm mcp start');
+    console.log('// Use MCP tools: swarm_init, task_orchestrate, agent_metrics');
+    return tasks;
+}
+// ============================================================================
+// Example 3: Consensus Building Data
+// ============================================================================
+/**
+ * Generate consensus protocol data for distributed decision making
+ */
+async function consensusBuildingData() {
+    console.log('\n🤝 Example 3: Consensus Building Scenarios\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate consensus rounds
+    const consensusRounds = await synth.generateStructured({
+        count: 50,
+        schema: {
+            round_id: 'UUID',
+            proposal_id: 'UUID',
+            protocol: 'raft | paxos | byzantine | quorum',
+            participants: ['array of 5-15 agent ids'],
+            proposer: 'agent id from participants',
+            proposal: {
+                type: 'leader_election | config_change | state_update | task_assignment',
+                value: 'JSON object with proposal details',
+            },
+            votes: [
+                {
+                    agent_id: 'agent id from participants',
+                    vote: 'accept | reject | abstain',
+                    timestamp: 'ISO timestamp',
+                    reasoning: 'short explanation',
+                },
+            ],
+            status: 'proposing | voting | committed | rejected | timeout',
+            quorum_required: 'number (majority of participants)',
+            quorum_reached: 'boolean',
+            decision: 'accepted | rejected | timeout',
+            round_duration_ms: 'number (100-5000)',
+            timestamp: 'ISO timestamp',
+        },
+        constraints: [
+            'Votes array should have one entry per participant',
+            '70% of rounds should reach quorum',
+            '80% of committed rounds should be accepted',
+            'Byzantine protocol should have 3f+1 participants (f failures)',
+        ],
+    });
+    // Analyze consensus
+    const successRate = consensusRounds.data.filter((r) => r.decision === 'accepted').length / consensusRounds.data.length;
+    console.log('Consensus Analysis:');
+    console.log(`- Total rounds: ${consensusRounds.data.length}`);
+    console.log(`- Success rate: ${(successRate * 100).toFixed(1)}%`);
+    console.log(`- Average duration: ${(consensusRounds.data.reduce((sum, r) => sum + r.round_duration_ms, 0) / consensusRounds.data.length).toFixed(0)}ms`);
+    console.log(`- Quorum reached: ${consensusRounds.data.filter((r) => r.quorum_reached).length} rounds`);
+    // Protocol distribution
+    const protocolCount = new Map();
+    consensusRounds.data.forEach((r) => {
+        protocolCount.set(r.protocol, (protocolCount.get(r.protocol) || 0) + 1);
+    });
+    console.log('\nProtocol Usage:');
+    protocolCount.forEach((count, protocol) => {
+        console.log(`- ${protocol}: ${count} rounds`);
+    });
+    return consensusRounds;
+}
+// ============================================================================
+// Example 4: Load Balancing Patterns
+// ============================================================================
+/**
+ * Generate load balancing metrics and patterns
+ */
+async function loadBalancingPatterns() {
+    console.log('\n⚖️  Example 4: Load Balancing Patterns\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate time-series load balancing metrics
+    const metrics = await synth.generateTimeSeries({
+        count: 200,
+        interval: '30s',
+        metrics: [
+            'agent_count',
+            'total_requests',
+            'avg_response_time_ms',
+            'cpu_utilization',
+            'memory_utilization',
+            'queue_depth',
+        ],
+        trend: 'mixed',
+        seasonality: true,
+    });
+    // Generate agent-specific metrics
+    const agentMetrics = await synth.generateStructured({
+        count: 100,
+        schema: {
+            timestamp: 'ISO timestamp',
+            agent_id: 'agent-{1-10}',
+            algorithm: 'round_robin | least_connections | weighted | ip_hash | consistent_hash',
+            requests_handled: 'number (0-100)',
+            active_connections: 'number (0-50)',
+            cpu_percent: 'number (0-100)',
+            memory_mb: 'number (100-2000)',
+            response_time_p50: 'number (10-500)',
+            response_time_p99: 'number (50-2000)',
+            error_rate: 'number (0-0.05)',
+            health_score: 'number (0-100)',
+        },
+        constraints: [
+            'Load should be relatively balanced across agents',
+            'High CPU should correlate with high request count',
+            'Error rate should increase with overload',
+            'Health score should decrease with high utilization',
+        ],
+    });
+    console.log('Load Balancing Analysis:');
+    console.log(`- Time series points: ${metrics.data.length}`);
+    console.log(`- Agent metrics: ${agentMetrics.data.length}`);
+    // Calculate balance score
+    const requestsByAgent = new Map();
+    agentMetrics.data.forEach((m) => {
+        requestsByAgent.set(m.agent_id, (requestsByAgent.get(m.agent_id) || 0) + m.requests_handled);
+    });
+    const avgRequests = Array.from(requestsByAgent.values()).reduce((a, b) => a + b, 0) / requestsByAgent.size;
+    const variance = Array.from(requestsByAgent.values()).reduce((sum, val) => sum + Math.pow(val - avgRequests, 2), 0) / requestsByAgent.size;
+    console.log(`- Average requests per agent: ${avgRequests.toFixed(1)}`);
+    console.log(`- Load variance: ${variance.toFixed(2)} (lower is better)`);
+    // Flow-Nexus integration for cloud load balancing
+    console.log('\nFlow-Nexus Cloud Integration:');
+    console.log('npx flow-nexus@latest login');
+    console.log('// Use MCP tools: swarm_scale, agent_spawn, sandbox_create');
+    return { metrics, agentMetrics };
+}
+// ============================================================================
+// Example 5: Fault Tolerance Scenarios
+// ============================================================================
+/**
+ * Generate fault tolerance and failure recovery scenarios
+ */
+async function faultToleranceScenarios() {
+    console.log('\n🛡️  Example 5: Fault Tolerance Scenarios\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate failure events
+    const failures = await synth.generateEvents({
+        count: 100,
+        eventTypes: [
+            'agent_crash',
+            'network_partition',
+            'timeout',
+            'out_of_memory',
+            'resource_exhaustion',
+            'byzantine_fault',
+        ],
+        schema: {
+            incident_id: 'UUID',
+            event_type: 'one of eventTypes',
+            affected_agents: ['array of 1-5 agent ids'],
+            severity: 'critical | high | medium | low',
+            detection_time: 'ISO timestamp',
+            recovery_initiated: 'ISO timestamp',
+            recovery_completed: 'ISO timestamp or null',
+            recovery_strategy: 'restart | failover | rollback | isolation | replication',
+            data_lost: 'boolean',
+            service_degraded: 'boolean',
+            mttr_seconds: 'number (10-600)',
+            root_cause: 'short description of root cause',
+        },
+        distribution: 'uniform',
+        timeRange: {
+            start: new Date(Date.now() - 86400000), // Last 24 hours
+            end: new Date(),
+        },
+    });
+    // Generate recovery actions
+    const recoveryActions = await synth.generateStructured({
+        count: failures.data.length,
+        schema: {
+            incident_id: 'UUID (from failures)',
+            action_id: 'UUID',
+            action_type: 'health_check | restart_agent | promote_backup | restore_state | load_balance',
+            executor: 'coordinator | agent-{id} | auto_healer',
+            success: 'boolean (90% true)',
+            duration_ms: 'number (100-10000)',
+            retries: 'number (0-3)',
+            compensating_actions: ['array of 0-2 action types or empty'],
+            timestamp: 'ISO timestamp',
+        },
+    });
+    // Analyze fault tolerance
+    const mttrAvg = failures.data.reduce((sum, f) => sum + f.mttr_seconds, 0) / failures.data.length;
+    const recoveryRate = recoveryActions.data.filter((a) => a.success).length / recoveryActions.data.length;
+    console.log('Fault Tolerance Analysis:');
+    console.log(`- Total incidents: ${failures.data.length}`);
+    console.log(`- Average MTTR: ${mttrAvg.toFixed(1)} seconds`);
+    console.log(`- Recovery success rate: ${(recoveryRate * 100).toFixed(1)}%`);
+    console.log(`- Data loss incidents: ${failures.data.filter((f) => f.data_lost).length}`);
+    console.log(`- Service degraded: ${failures.data.filter((f) => f.service_degraded).length}`);
+    // Failure type distribution
+    const failureTypes = new Map();
+    failures.data.forEach((f) => {
+        failureTypes.set(f.event_type, (failureTypes.get(f.event_type) || 0) + 1);
+    });
+    console.log('\nFailure Type Distribution:');
+    failureTypes.forEach((count, type) => {
+        console.log(`- ${type}: ${count} (${((count / failures.data.length) * 100).toFixed(1)}%)`);
+    });
+    // Self-healing integration
+    console.log('\nSelf-Healing Integration:');
+    console.log('// Enable auto-recovery hooks');
+    console.log('npx claude-flow@alpha hooks enable --type "error-recovery"');
+    return { failures, recoveryActions };
+}
+// ============================================================================
+// Example 6: Hierarchical Coordination
+// ============================================================================
+/**
+ * Generate hierarchical swarm coordination data
+ */
+async function hierarchicalCoordination() {
+    console.log('\n🏗️  Example 6: Hierarchical Swarm Coordination\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate hierarchical structure
+    const swarmTopology = await synth.generateStructured({
+        count: 1,
+        schema: {
+            topology_id: 'UUID',
+            type: 'hierarchical',
+            coordinator: {
+                agent_id: 'coordinator-main',
+                role: 'master_coordinator',
+                responsibilities: ['task_distribution', 'health_monitoring', 'consensus_leader'],
+            },
+            sub_coordinators: [
+                {
+                    agent_id: 'sub-coordinator-{1-5}',
+                    role: 'regional_coordinator',
+                    manages_agents: ['array of 5-10 worker agent ids'],
+                    region: 'zone-{A-E}',
+                },
+            ],
+            workers: [
+                {
+                    agent_id: 'worker-{1-50}',
+                    coordinator_id: 'sub-coordinator id',
+                    capabilities: ['array of 2-4 capabilities'],
+                    status: 'active | idle | busy | offline',
+                },
+            ],
+            communication_patterns: {
+                coordinator_to_sub: 'direct',
+                sub_to_workers: 'multicast',
+                worker_to_coordinator: 'via_sub_coordinator',
+                peer_to_peer: 'disabled',
+            },
+        },
+    });
+    // Generate coordination events
+    const coordinationEvents = await synth.generateEvents({
+        count: 200,
+        eventTypes: [
+            'task_delegation',
+            'status_report',
+            'resource_request',
+            'coordination_sync',
+            'topology_update',
+        ],
+        schema: {
+            event_id: 'UUID',
+            event_type: 'one of eventTypes',
+            from_agent: 'agent id from topology',
+            to_agent: 'agent id from topology',
+            hierarchy_level: 'coordinator | sub_coordinator | worker',
+            payload: 'JSON object',
+            timestamp: 'ISO timestamp',
+        },
+    });
+    console.log('Hierarchical Coordination:');
+    console.log(`- Total agents: ${swarmTopology.data[0].workers.length + swarmTopology.data[0].sub_coordinators.length + 1}`);
+    console.log(`- Sub-coordinators: ${swarmTopology.data[0].sub_coordinators.length}`);
+    console.log(`- Workers: ${swarmTopology.data[0].workers.length}`);
+    console.log(`- Coordination events: ${coordinationEvents.data.length}`);
+    // Claude-Flow hierarchical setup
+    console.log('\nClaude-Flow Hierarchical Setup:');
+    console.log('npx claude-flow@alpha mcp start');
+    console.log('// MCP: swarm_init with topology: "hierarchical"');
+    console.log('// MCP: agent_spawn with role assignments');
+    return { topology: swarmTopology, events: coordinationEvents };
+}
+// ============================================================================
+// Run All Examples
+// ============================================================================
+async function runAllCoordinationExamples() {
+    console.log('🚀 Running All Agent Coordination Examples\n');
+    console.log('='.repeat(70));
+    try {
+        await agentCommunicationPatterns();
+        console.log('='.repeat(70));
+        await taskDistributionScenarios();
+        console.log('='.repeat(70));
+        await consensusBuildingData();
+        console.log('='.repeat(70));
+        await loadBalancingPatterns();
+        console.log('='.repeat(70));
+        await faultToleranceScenarios();
+        console.log('='.repeat(70));
+        await hierarchicalCoordination();
+        console.log('='.repeat(70));
+        console.log('\n✅ All agent coordination examples completed!\n');
+    }
+    catch (error) {
+        console.error('❌ Error running examples:', error.message);
+        throw error;
+    }
+}
+// Run if executed directly
+if (import.meta.url === `file://${process.argv[1]}`) {
+    runAllCoordinationExamples().catch(console.error);
+}
+//# sourceMappingURL=agent-coordination.js.map

vendor/ruvector/npm/packages/agentic-synth/examples/swarms/agent-coordination.ts

@@ -0,0 +1,518 @@
+/**
+ * Multi-Agent System Coordination Examples
+ *
+ * Demonstrates agent communication patterns, task distribution,
+ * consensus building, load balancing, and fault tolerance scenarios
+ * for distributed agent systems.
+ *
+ * Integrates with:
+ * - claude-flow: Swarm initialization and orchestration
+ * - ruv-swarm: Enhanced coordination patterns
+ * - flow-nexus: Cloud-based agent management
+ */
+
+import { AgenticSynth, createSynth } from '../../dist/index.js';
+import type { GenerationResult } from '../../src/types.js';
+
+// ============================================================================
+// Example 1: Agent Communication Patterns
+// ============================================================================
+
+/**
+ * Generate communication patterns for multi-agent systems
+ */
+export async function agentCommunicationPatterns() {
+  console.log('\n🤖 Example 1: Agent Communication Patterns\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    cacheStrategy: 'memory',
+  });
+
+  // Generate message passing data
+  const messages = await synth.generateEvents({
+    count: 500,
+    eventTypes: [
+      'direct_message',
+      'broadcast',
+      'multicast',
+      'request_reply',
+      'publish_subscribe',
+    ],
+    schema: {
+      message_id: 'UUID',
+      sender_agent_id: 'agent-{1-20}',
+      receiver_agent_id: 'agent-{1-20} or "broadcast"',
+      message_type: 'one of eventTypes',
+      payload: {
+        action: 'task_request | status_update | data_sync | error_report | ack',
+        data: 'JSON object with task details',
+        priority: 'high | medium | low',
+        requires_response: 'boolean',
+      },
+      timestamp: 'ISO timestamp',
+      latency_ms: 'number (1-500)',
+      success: 'boolean (95% true)',
+    },
+    distribution: 'poisson',
+    timeRange: {
+      start: new Date(Date.now() - 3600000), // Last hour
+      end: new Date(),
+    },
+  });
+
+  console.log('Communication Patterns Generated:');
+  console.log(`- Total messages: ${messages.data.length}`);
+  console.log(`- Direct messages: ${messages.data.filter((m: any) => m.message_type === 'direct_message').length}`);
+  console.log(`- Broadcasts: ${messages.data.filter((m: any) => m.message_type === 'broadcast').length}`);
+  console.log(`- Average latency: ${(messages.data.reduce((sum: number, m: any) => sum + m.latency_ms, 0) / messages.data.length).toFixed(2)}ms`);
+
+  // Integration with claude-flow hooks
+  console.log('\nClaude-Flow Integration:');
+  console.log('npx claude-flow@alpha hooks notify --message "Communication patterns generated"');
+  console.log('npx claude-flow@alpha hooks post-edit --file "messages.json" --memory-key "swarm/coordinator/messages"');
+
+  return messages;
+}
+
+// ============================================================================
+// Example 2: Task Distribution Scenarios
+// ============================================================================
+
+/**
+ * Generate task distribution data for load balancing
+ */
+export async function taskDistributionScenarios() {
+  console.log('\n📋 Example 2: Task Distribution Scenarios\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate task distribution events
+  const tasks = await synth.generateStructured({
+    count: 300,
+    schema: {
+      task_id: 'UUID',
+      task_type: 'compute | data_processing | io_operation | ml_inference | api_call',
+      assigned_agent: 'agent-{1-15}',
+      estimated_duration_ms: 'number (100-10000)',
+      actual_duration_ms: 'number (estimated_duration_ms * 0.8-1.2)',
+      cpu_usage: 'number (0-100)',
+      memory_mb: 'number (10-1000)',
+      priority: 'number (1-10)',
+      dependencies: ['array of 0-3 task_ids or empty'],
+      status: 'pending | running | completed | failed',
+      start_time: 'ISO timestamp',
+      end_time: 'ISO timestamp or null',
+      retry_count: 'number (0-3)',
+    },
+    constraints: [
+      'Tasks should be distributed evenly across agents',
+      'High priority tasks (8-10) should complete faster',
+      '5% of tasks should have failed status',
+      'Dependencies should form valid DAG (no cycles)',
+    ],
+  });
+
+  // Analyze distribution
+  const agentLoad = new Map<string, number>();
+  tasks.data.forEach((task: any) => {
+    agentLoad.set(task.assigned_agent, (agentLoad.get(task.assigned_agent) || 0) + 1);
+  });
+
+  console.log('Task Distribution Analysis:');
+  console.log(`- Total tasks: ${tasks.data.length}`);
+  console.log(`- Agents utilized: ${agentLoad.size}`);
+  console.log(`- Tasks per agent (avg): ${(tasks.data.length / agentLoad.size).toFixed(1)}`);
+  console.log(`- Failed tasks: ${tasks.data.filter((t: any) => t.status === 'failed').length}`);
+  console.log(`- Completed tasks: ${tasks.data.filter((t: any) => t.status === 'completed').length}`);
+
+  // Ruv-Swarm coordination pattern
+  console.log('\nRuv-Swarm Coordination:');
+  console.log('npx ruv-swarm mcp start');
+  console.log('// Use MCP tools: swarm_init, task_orchestrate, agent_metrics');
+
+  return tasks;
+}
+
+// ============================================================================
+// Example 3: Consensus Building Data
+// ============================================================================
+
+/**
+ * Generate consensus protocol data for distributed decision making
+ */
+export async function consensusBuildingData() {
+  console.log('\n🤝 Example 3: Consensus Building Scenarios\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate consensus rounds
+  const consensusRounds = await synth.generateStructured({
+    count: 50,
+    schema: {
+      round_id: 'UUID',
+      proposal_id: 'UUID',
+      protocol: 'raft | paxos | byzantine | quorum',
+      participants: ['array of 5-15 agent ids'],
+      proposer: 'agent id from participants',
+      proposal: {
+        type: 'leader_election | config_change | state_update | task_assignment',
+        value: 'JSON object with proposal details',
+      },
+      votes: [
+        {
+          agent_id: 'agent id from participants',
+          vote: 'accept | reject | abstain',
+          timestamp: 'ISO timestamp',
+          reasoning: 'short explanation',
+        },
+      ],
+      status: 'proposing | voting | committed | rejected | timeout',
+      quorum_required: 'number (majority of participants)',
+      quorum_reached: 'boolean',
+      decision: 'accepted | rejected | timeout',
+      round_duration_ms: 'number (100-5000)',
+      timestamp: 'ISO timestamp',
+    },
+    constraints: [
+      'Votes array should have one entry per participant',
+      '70% of rounds should reach quorum',
+      '80% of committed rounds should be accepted',
+      'Byzantine protocol should have 3f+1 participants (f failures)',
+    ],
+  });
+
+  // Analyze consensus
+  const successRate = consensusRounds.data.filter(
+    (r: any) => r.decision === 'accepted'
+  ).length / consensusRounds.data.length;
+
+  console.log('Consensus Analysis:');
+  console.log(`- Total rounds: ${consensusRounds.data.length}`);
+  console.log(`- Success rate: ${(successRate * 100).toFixed(1)}%`);
+  console.log(`- Average duration: ${(consensusRounds.data.reduce((sum: number, r: any) => sum + r.round_duration_ms, 0) / consensusRounds.data.length).toFixed(0)}ms`);
+  console.log(`- Quorum reached: ${consensusRounds.data.filter((r: any) => r.quorum_reached).length} rounds`);
+
+  // Protocol distribution
+  const protocolCount = new Map<string, number>();
+  consensusRounds.data.forEach((r: any) => {
+    protocolCount.set(r.protocol, (protocolCount.get(r.protocol) || 0) + 1);
+  });
+  console.log('\nProtocol Usage:');
+  protocolCount.forEach((count, protocol) => {
+    console.log(`- ${protocol}: ${count} rounds`);
+  });
+
+  return consensusRounds;
+}
+
+// ============================================================================
+// Example 4: Load Balancing Patterns
+// ============================================================================
+
+/**
+ * Generate load balancing metrics and patterns
+ */
+export async function loadBalancingPatterns() {
+  console.log('\n⚖️  Example 4: Load Balancing Patterns\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate time-series load balancing metrics
+  const metrics = await synth.generateTimeSeries({
+    count: 200,
+    interval: '30s',
+    metrics: [
+      'agent_count',
+      'total_requests',
+      'avg_response_time_ms',
+      'cpu_utilization',
+      'memory_utilization',
+      'queue_depth',
+    ],
+    trend: 'mixed',
+    seasonality: true,
+  });
+
+  // Generate agent-specific metrics
+  const agentMetrics = await synth.generateStructured({
+    count: 100,
+    schema: {
+      timestamp: 'ISO timestamp',
+      agent_id: 'agent-{1-10}',
+      algorithm: 'round_robin | least_connections | weighted | ip_hash | consistent_hash',
+      requests_handled: 'number (0-100)',
+      active_connections: 'number (0-50)',
+      cpu_percent: 'number (0-100)',
+      memory_mb: 'number (100-2000)',
+      response_time_p50: 'number (10-500)',
+      response_time_p99: 'number (50-2000)',
+      error_rate: 'number (0-0.05)',
+      health_score: 'number (0-100)',
+    },
+    constraints: [
+      'Load should be relatively balanced across agents',
+      'High CPU should correlate with high request count',
+      'Error rate should increase with overload',
+      'Health score should decrease with high utilization',
+    ],
+  });
+
+  console.log('Load Balancing Analysis:');
+  console.log(`- Time series points: ${metrics.data.length}`);
+  console.log(`- Agent metrics: ${agentMetrics.data.length}`);
+
+  // Calculate balance score
+  const requestsByAgent = new Map<string, number>();
+  agentMetrics.data.forEach((m: any) => {
+    requestsByAgent.set(
+      m.agent_id,
+      (requestsByAgent.get(m.agent_id) || 0) + m.requests_handled
+    );
+  });
+
+  const avgRequests = Array.from(requestsByAgent.values()).reduce((a, b) => a + b, 0) / requestsByAgent.size;
+  const variance = Array.from(requestsByAgent.values()).reduce(
+    (sum, val) => sum + Math.pow(val - avgRequests, 2),
+    0
+  ) / requestsByAgent.size;
+
+  console.log(`- Average requests per agent: ${avgRequests.toFixed(1)}`);
+  console.log(`- Load variance: ${variance.toFixed(2)} (lower is better)`);
+
+  // Flow-Nexus integration for cloud load balancing
+  console.log('\nFlow-Nexus Cloud Integration:');
+  console.log('npx flow-nexus@latest login');
+  console.log('// Use MCP tools: swarm_scale, agent_spawn, sandbox_create');
+
+  return { metrics, agentMetrics };
+}
+
+// ============================================================================
+// Example 5: Fault Tolerance Scenarios
+// ============================================================================
+
+/**
+ * Generate fault tolerance and failure recovery scenarios
+ */
+export async function faultToleranceScenarios() {
+  console.log('\n🛡️  Example 5: Fault Tolerance Scenarios\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate failure events
+  const failures = await synth.generateEvents({
+    count: 100,
+    eventTypes: [
+      'agent_crash',
+      'network_partition',
+      'timeout',
+      'out_of_memory',
+      'resource_exhaustion',
+      'byzantine_fault',
+    ],
+    schema: {
+      incident_id: 'UUID',
+      event_type: 'one of eventTypes',
+      affected_agents: ['array of 1-5 agent ids'],
+      severity: 'critical | high | medium | low',
+      detection_time: 'ISO timestamp',
+      recovery_initiated: 'ISO timestamp',
+      recovery_completed: 'ISO timestamp or null',
+      recovery_strategy: 'restart | failover | rollback | isolation | replication',
+      data_lost: 'boolean',
+      service_degraded: 'boolean',
+      mttr_seconds: 'number (10-600)',
+      root_cause: 'short description of root cause',
+    },
+    distribution: 'uniform',
+    timeRange: {
+      start: new Date(Date.now() - 86400000), // Last 24 hours
+      end: new Date(),
+    },
+  });
+
+  // Generate recovery actions
+  const recoveryActions = await synth.generateStructured({
+    count: failures.data.length,
+    schema: {
+      incident_id: 'UUID (from failures)',
+      action_id: 'UUID',
+      action_type: 'health_check | restart_agent | promote_backup | restore_state | load_balance',
+      executor: 'coordinator | agent-{id} | auto_healer',
+      success: 'boolean (90% true)',
+      duration_ms: 'number (100-10000)',
+      retries: 'number (0-3)',
+      compensating_actions: ['array of 0-2 action types or empty'],
+      timestamp: 'ISO timestamp',
+    },
+  });
+
+  // Analyze fault tolerance
+  const mttrAvg = failures.data.reduce((sum: number, f: any) => sum + f.mttr_seconds, 0) / failures.data.length;
+  const recoveryRate = recoveryActions.data.filter((a: any) => a.success).length / recoveryActions.data.length;
+
+  console.log('Fault Tolerance Analysis:');
+  console.log(`- Total incidents: ${failures.data.length}`);
+  console.log(`- Average MTTR: ${mttrAvg.toFixed(1)} seconds`);
+  console.log(`- Recovery success rate: ${(recoveryRate * 100).toFixed(1)}%`);
+  console.log(`- Data loss incidents: ${failures.data.filter((f: any) => f.data_lost).length}`);
+  console.log(`- Service degraded: ${failures.data.filter((f: any) => f.service_degraded).length}`);
+
+  // Failure type distribution
+  const failureTypes = new Map<string, number>();
+  failures.data.forEach((f: any) => {
+    failureTypes.set(f.event_type, (failureTypes.get(f.event_type) || 0) + 1);
+  });
+
+  console.log('\nFailure Type Distribution:');
+  failureTypes.forEach((count, type) => {
+    console.log(`- ${type}: ${count} (${((count / failures.data.length) * 100).toFixed(1)}%)`);
+  });
+
+  // Self-healing integration
+  console.log('\nSelf-Healing Integration:');
+  console.log('// Enable auto-recovery hooks');
+  console.log('npx claude-flow@alpha hooks enable --type "error-recovery"');
+
+  return { failures, recoveryActions };
+}
+
+// ============================================================================
+// Example 6: Hierarchical Coordination
+// ============================================================================
+
+/**
+ * Generate hierarchical swarm coordination data
+ */
+export async function hierarchicalCoordination() {
+  console.log('\n🏗️  Example 6: Hierarchical Swarm Coordination\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate hierarchical structure
+  const swarmTopology = await synth.generateStructured({
+    count: 1,
+    schema: {
+      topology_id: 'UUID',
+      type: 'hierarchical',
+      coordinator: {
+        agent_id: 'coordinator-main',
+        role: 'master_coordinator',
+        responsibilities: ['task_distribution', 'health_monitoring', 'consensus_leader'],
+      },
+      sub_coordinators: [
+        {
+          agent_id: 'sub-coordinator-{1-5}',
+          role: 'regional_coordinator',
+          manages_agents: ['array of 5-10 worker agent ids'],
+          region: 'zone-{A-E}',
+        },
+      ],
+      workers: [
+        {
+          agent_id: 'worker-{1-50}',
+          coordinator_id: 'sub-coordinator id',
+          capabilities: ['array of 2-4 capabilities'],
+          status: 'active | idle | busy | offline',
+        },
+      ],
+      communication_patterns: {
+        coordinator_to_sub: 'direct',
+        sub_to_workers: 'multicast',
+        worker_to_coordinator: 'via_sub_coordinator',
+        peer_to_peer: 'disabled',
+      },
+    },
+  });
+
+  // Generate coordination events
+  const coordinationEvents = await synth.generateEvents({
+    count: 200,
+    eventTypes: [
+      'task_delegation',
+      'status_report',
+      'resource_request',
+      'coordination_sync',
+      'topology_update',
+    ],
+    schema: {
+      event_id: 'UUID',
+      event_type: 'one of eventTypes',
+      from_agent: 'agent id from topology',
+      to_agent: 'agent id from topology',
+      hierarchy_level: 'coordinator | sub_coordinator | worker',
+      payload: 'JSON object',
+      timestamp: 'ISO timestamp',
+    },
+  });
+
+  console.log('Hierarchical Coordination:');
+  console.log(`- Total agents: ${swarmTopology.data[0].workers.length + swarmTopology.data[0].sub_coordinators.length + 1}`);
+  console.log(`- Sub-coordinators: ${swarmTopology.data[0].sub_coordinators.length}`);
+  console.log(`- Workers: ${swarmTopology.data[0].workers.length}`);
+  console.log(`- Coordination events: ${coordinationEvents.data.length}`);
+
+  // Claude-Flow hierarchical setup
+  console.log('\nClaude-Flow Hierarchical Setup:');
+  console.log('npx claude-flow@alpha mcp start');
+  console.log('// MCP: swarm_init with topology: "hierarchical"');
+  console.log('// MCP: agent_spawn with role assignments');
+
+  return { topology: swarmTopology, events: coordinationEvents };
+}
+
+// ============================================================================
+// Run All Examples
+// ============================================================================
+
+export async function runAllCoordinationExamples() {
+  console.log('🚀 Running All Agent Coordination Examples\n');
+  console.log('='.repeat(70));
+
+  try {
+    await agentCommunicationPatterns();
+    console.log('='.repeat(70));
+
+    await taskDistributionScenarios();
+    console.log('='.repeat(70));
+
+    await consensusBuildingData();
+    console.log('='.repeat(70));
+
+    await loadBalancingPatterns();
+    console.log('='.repeat(70));
+
+    await faultToleranceScenarios();
+    console.log('='.repeat(70));
+
+    await hierarchicalCoordination();
+    console.log('='.repeat(70));
+
+    console.log('\n✅ All agent coordination examples completed!\n');
+  } catch (error: any) {
+    console.error('❌ Error running examples:', error.message);
+    throw error;
+  }
+}
+
+// Run if executed directly
+if (import.meta.url === `file://${process.argv[1]}`) {
+  runAllCoordinationExamples().catch(console.error);
+}

vendor/ruvector/npm/packages/agentic-synth/examples/swarms/agent-lifecycle.d.ts

@@ -0,0 +1,58 @@
+/**
+ * Agent Lifecycle Management Examples
+ *
+ * Demonstrates agent lifecycle patterns including spawning/termination,
+ * state synchronization, health checks, recovery patterns, and
+ * version migration for dynamic agent systems.
+ *
+ * Integrates with:
+ * - claude-flow: Agent lifecycle hooks and state management
+ * - ruv-swarm: Dynamic agent spawning and coordination
+ * - Kubernetes: Container orchestration patterns
+ */
+/**
+ * Generate agent spawning and termination lifecycle events
+ */
+export declare function agentSpawningTermination(): Promise<{
+    lifecycleEvents: import("../../dist/index.js").GenerationResult<unknown>;
+    spawnStrategies: import("../../dist/index.js").GenerationResult<unknown>;
+    resourcePool: import("../../dist/index.js").GenerationResult<unknown>;
+}>;
+/**
+ * Generate state synchronization data for distributed agents
+ */
+export declare function stateSynchronization(): Promise<{
+    stateSnapshots: import("../../dist/index.js").GenerationResult<unknown>;
+    syncEvents: import("../../dist/index.js").GenerationResult<unknown>;
+    consistencyChecks: import("../../dist/index.js").GenerationResult<unknown>;
+    syncTopology: import("../../dist/index.js").GenerationResult<unknown>;
+}>;
+/**
+ * Generate health check and monitoring data
+ */
+export declare function healthCheckScenarios(): Promise<{
+    healthChecks: import("../../dist/index.js").GenerationResult<unknown>;
+    healthConfigs: import("../../dist/index.js").GenerationResult<unknown>;
+    healthTimeSeries: import("../../dist/index.js").GenerationResult<unknown>;
+    healingActions: import("../../dist/index.js").GenerationResult<unknown>;
+}>;
+/**
+ * Generate failure recovery pattern data
+ */
+export declare function recoveryPatterns(): Promise<{
+    failures: import("../../dist/index.js").GenerationResult<unknown>;
+    recoveryStrategies: import("../../dist/index.js").GenerationResult<unknown>;
+    circuitBreakers: import("../../dist/index.js").GenerationResult<unknown>;
+    backupOperations: import("../../dist/index.js").GenerationResult<unknown>;
+}>;
+/**
+ * Generate agent version migration data
+ */
+export declare function versionMigration(): Promise<{
+    versions: import("../../dist/index.js").GenerationResult<unknown>;
+    migrations: import("../../dist/index.js").GenerationResult<unknown>;
+    canaryDeployments: import("../../dist/index.js").GenerationResult<unknown>;
+    rollbacks: import("../../dist/index.js").GenerationResult<unknown>;
+}>;
+export declare function runAllLifecycleExamples(): Promise<void>;
+//# sourceMappingURL=agent-lifecycle.d.ts.map

vendor/ruvector/npm/packages/agentic-synth/examples/swarms/agent-lifecycle.d.ts.map

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vendor/ruvector/npm/packages/agentic-synth/examples/swarms/agent-lifecycle.js

@@ -0,0 +1,667 @@
+"use strict";
+/**
+ * Agent Lifecycle Management Examples
+ *
+ * Demonstrates agent lifecycle patterns including spawning/termination,
+ * state synchronization, health checks, recovery patterns, and
+ * version migration for dynamic agent systems.
+ *
+ * Integrates with:
+ * - claude-flow: Agent lifecycle hooks and state management
+ * - ruv-swarm: Dynamic agent spawning and coordination
+ * - Kubernetes: Container orchestration patterns
+ */
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.agentSpawningTermination = agentSpawningTermination;
+exports.stateSynchronization = stateSynchronization;
+exports.healthCheckScenarios = healthCheckScenarios;
+exports.recoveryPatterns = recoveryPatterns;
+exports.versionMigration = versionMigration;
+exports.runAllLifecycleExamples = runAllLifecycleExamples;
+const index_js_1 = require("../../dist/index.js");
+// ============================================================================
+// Example 1: Agent Spawning and Termination
+// ============================================================================
+/**
+ * Generate agent spawning and termination lifecycle events
+ */
+async function agentSpawningTermination() {
+    console.log('\n🚀 Example 1: Agent Spawning and Termination\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+        cacheStrategy: 'memory',
+    });
+    // Generate agent lifecycle events
+    const lifecycleEvents = await synth.generateEvents({
+        count: 500,
+        eventTypes: [
+            'agent_spawn_requested',
+            'agent_initializing',
+            'agent_ready',
+            'agent_active',
+            'agent_idle',
+            'agent_terminating',
+            'agent_terminated',
+            'agent_failed',
+        ],
+        schema: {
+            event_id: 'UUID',
+            agent_id: 'agent-{1-100}',
+            event_type: 'one of eventTypes',
+            reason: 'spawn reason or termination reason',
+            requested_by: 'coordinator | auto_scaler | user | system',
+            resource_allocation: {
+                cpu_cores: 'number (0.5-8.0)',
+                memory_mb: 'number (512-8192)',
+                disk_mb: 'number (1024-10240)',
+            },
+            initialization_config: {
+                agent_type: 'worker | coordinator | specialist | observer',
+                capabilities: ['array of 1-5 capabilities'],
+                priority: 'high | medium | low',
+                max_lifetime_minutes: 'number (60-14400) or null',
+            },
+            state_data_size_mb: 'number (0-1000)',
+            startup_time_ms: 'number (100-10000)',
+            shutdown_time_ms: 'number (50-5000)',
+            exit_code: 'number (0-255) or null',
+            timestamp: 'ISO timestamp',
+        },
+        distribution: 'poisson',
+        timeRange: {
+            start: new Date(Date.now() - 86400000), // Last 24 hours
+            end: new Date(),
+        },
+    });
+    // Generate agent spawn strategies
+    const spawnStrategies = await synth.generateStructured({
+        count: 20,
+        schema: {
+            strategy_id: 'UUID',
+            strategy_name: 'descriptive name',
+            trigger_type: 'manual | scheduled | load_based | event_driven | predictive',
+            conditions: ['array of 2-4 conditions'],
+            agent_template: {
+                agent_type: 'worker | coordinator | specialist | observer',
+                base_config: 'JSON configuration object',
+                scaling_limits: {
+                    min_instances: 'number (1-5)',
+                    max_instances: 'number (10-100)',
+                    scale_up_threshold: 'number (0-100)',
+                    scale_down_threshold: 'number (0-100)',
+                },
+            },
+            spawn_pattern: 'immediate | gradual | burst',
+            cooldown_seconds: 'number (30-600)',
+            success_rate: 'number (0-100)',
+            avg_spawn_time_ms: 'number (500-15000)',
+        },
+    });
+    // Generate resource pool state
+    const resourcePool = await synth.generateTimeSeries({
+        count: 100,
+        interval: '5m',
+        metrics: [
+            'active_agents',
+            'spawning_agents',
+            'terminating_agents',
+            'failed_spawns',
+            'total_cpu_usage',
+            'total_memory_mb',
+            'available_slots',
+        ],
+        trend: 'mixed',
+    });
+    console.log('Agent Lifecycle Analysis:');
+    console.log(`- Lifecycle events: ${lifecycleEvents.data.length}`);
+    console.log(`- Spawn strategies: ${spawnStrategies.data.length}`);
+    console.log(`- Resource pool snapshots: ${resourcePool.data.length}`);
+    // Analyze lifecycle events
+    const spawnedCount = lifecycleEvents.data.filter((e) => e.event_type === 'agent_ready').length;
+    const terminatedCount = lifecycleEvents.data.filter((e) => e.event_type === 'agent_terminated').length;
+    const failedCount = lifecycleEvents.data.filter((e) => e.event_type === 'agent_failed').length;
+    console.log(`\nLifecycle Statistics:`);
+    console.log(`- Successfully spawned: ${spawnedCount}`);
+    console.log(`- Terminated: ${terminatedCount}`);
+    console.log(`- Failed: ${failedCount} (${((failedCount / lifecycleEvents.data.length) * 100).toFixed(1)}%)`);
+    // Calculate average spawn time
+    const avgSpawnTime = lifecycleEvents.data
+        .filter((e) => e.startup_time_ms)
+        .reduce((sum, e) => sum + e.startup_time_ms, 0) / spawnedCount;
+    console.log(`- Average spawn time: ${avgSpawnTime.toFixed(0)}ms`);
+    // Claude-Flow integration
+    console.log('\nClaude-Flow Integration:');
+    console.log('npx claude-flow@alpha hooks pre-task --spawn-agents 5');
+    console.log('// MCP: agent_spawn with configuration');
+    console.log('npx claude-flow@alpha hooks post-task --cleanup-agents true');
+    return { lifecycleEvents, spawnStrategies, resourcePool };
+}
+// ============================================================================
+// Example 2: State Synchronization
+// ============================================================================
+/**
+ * Generate state synchronization data for distributed agents
+ */
+async function stateSynchronization() {
+    console.log('\n🔄 Example 2: State Synchronization\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate agent state snapshots
+    const stateSnapshots = await synth.generateStructured({
+        count: 500,
+        schema: {
+            snapshot_id: 'UUID',
+            agent_id: 'agent-{1-50}',
+            version: 'number (1-1000)',
+            state_type: 'full | incremental | checkpoint',
+            state_data: {
+                memory: {
+                    short_term: 'JSON object',
+                    long_term: 'JSON object',
+                    working_set_size_mb: 'number (1-500)',
+                },
+                task_queue: ['array of 0-20 task ids'],
+                active_connections: ['array of 0-10 agent ids'],
+                performance_metrics: {
+                    tasks_completed: 'number (0-1000)',
+                    cpu_usage: 'number (0-100)',
+                    memory_usage: 'number (0-100)',
+                },
+                custom_state: 'JSON object',
+            },
+            state_size_bytes: 'number (1024-10485760)',
+            compression_ratio: 'number (0.3-1.0)',
+            checksum: 'SHA-256 hash',
+            created_at: 'ISO timestamp',
+        },
+    });
+    // Generate synchronization events
+    const syncEvents = await synth.generateEvents({
+        count: 1000,
+        eventTypes: [
+            'state_saved',
+            'state_loaded',
+            'state_replicated',
+            'state_conflict',
+            'state_merged',
+            'state_rollback',
+        ],
+        schema: {
+            event_id: 'UUID',
+            event_type: 'one of eventTypes',
+            agent_id: 'agent-{1-50}',
+            snapshot_id: 'UUID (from stateSnapshots)',
+            target_location: 'local | remote | backup | replica-{1-5}',
+            sync_strategy: 'optimistic | pessimistic | eventual | strong',
+            duration_ms: 'number (10-5000)',
+            data_transferred_mb: 'number (0.1-100)',
+            success: 'boolean (95% true)',
+            conflict_resolved: 'boolean or null',
+            timestamp: 'ISO timestamp',
+        },
+        distribution: 'poisson',
+    });
+    // Generate state consistency checks
+    const consistencyChecks = await synth.generateStructured({
+        count: 100,
+        schema: {
+            check_id: 'UUID',
+            check_type: 'integrity | consistency | replication | divergence',
+            agents_checked: ['array of 3-10 agent ids'],
+            snapshot_versions: ['array of version numbers'],
+            consistency_score: 'number (0-100)',
+            divergent_agents: ['array of 0-3 agent ids or empty'],
+            anomalies_detected: ['array of 0-2 anomaly descriptions or empty'],
+            corrective_action: 'none | resync | repair | rollback | null',
+            duration_ms: 'number (100-10000)',
+            timestamp: 'ISO timestamp',
+        },
+    });
+    // Generate state synchronization topology
+    const syncTopology = await synth.generateStructured({
+        count: 1,
+        schema: {
+            topology_id: 'UUID',
+            sync_pattern: 'star | mesh | ring | hierarchical | hybrid',
+            nodes: [
+                {
+                    node_id: 'node-{1-10}',
+                    role: 'primary | replica | backup | cache',
+                    agents_hosted: ['array of 5-10 agent ids'],
+                    sync_frequency_seconds: 'number (1-300)',
+                    replication_lag_ms: 'number (0-1000)',
+                    storage_capacity_gb: 'number (10-1000)',
+                    storage_used_gb: 'number (proportional to capacity)',
+                },
+            ],
+            sync_protocol: 'raft | paxos | gossip | two_phase_commit',
+            conflict_resolution: 'last_write_wins | merge | vector_clock | manual',
+        },
+    });
+    console.log('State Synchronization Analysis:');
+    console.log(`- State snapshots: ${stateSnapshots.data.length}`);
+    console.log(`- Sync events: ${syncEvents.data.length}`);
+    console.log(`- Consistency checks: ${consistencyChecks.data.length}`);
+    // Analyze synchronization success
+    const successfulSyncs = syncEvents.data.filter((e) => e.success).length;
+    const avgSyncTime = syncEvents.data.reduce((sum, e) => sum + e.duration_ms, 0) / syncEvents.data.length;
+    console.log(`\nSynchronization Metrics:`);
+    console.log(`- Success rate: ${((successfulSyncs / syncEvents.data.length) * 100).toFixed(1)}%`);
+    console.log(`- Average sync time: ${avgSyncTime.toFixed(0)}ms`);
+    // Consistency analysis
+    const avgConsistency = consistencyChecks.data.reduce((sum, c) => sum + c.consistency_score, 0) / consistencyChecks.data.length;
+    console.log(`- Average consistency score: ${avgConsistency.toFixed(1)}/100`);
+    console.log(`- Anomalies detected: ${consistencyChecks.data.filter((c) => c.anomalies_detected.length > 0).length}`);
+    // Integration pattern
+    console.log('\nState Sync Integration:');
+    console.log('// Store state in distributed storage (Redis, etcd)');
+    console.log('await redis.set(`agent:${agentId}:state`, JSON.stringify(state));');
+    console.log('// Use claude-flow memory for coordination state');
+    console.log('npx claude-flow@alpha hooks session-end --export-state true');
+    return { stateSnapshots, syncEvents, consistencyChecks, syncTopology };
+}
+// ============================================================================
+// Example 3: Health Check Scenarios
+// ============================================================================
+/**
+ * Generate health check and monitoring data
+ */
+async function healthCheckScenarios() {
+    console.log('\n💊 Example 3: Health Check Scenarios\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate health check results
+    const healthChecks = await synth.generateEvents({
+        count: 1000,
+        eventTypes: [
+            'health_check_passed',
+            'health_check_degraded',
+            'health_check_failed',
+            'health_check_timeout',
+        ],
+        schema: {
+            check_id: 'UUID',
+            agent_id: 'agent-{1-80}',
+            event_type: 'one of eventTypes',
+            check_type: 'liveness | readiness | startup | deep',
+            health_score: 'number (0-100)',
+            metrics: {
+                response_time_ms: 'number (1-5000)',
+                cpu_usage: 'number (0-100)',
+                memory_usage: 'number (0-100)',
+                active_connections: 'number (0-100)',
+                error_rate: 'number (0-100)',
+                queue_depth: 'number (0-1000)',
+            },
+            issues_detected: ['array of 0-3 issue descriptions or empty'],
+            recovery_actions: ['array of 0-2 actions or empty'],
+            timestamp: 'ISO timestamp',
+        },
+        distribution: 'poisson',
+    });
+    // Generate health monitoring configuration
+    const healthConfigs = await synth.generateStructured({
+        count: 50,
+        schema: {
+            agent_id: 'agent-{1-50}',
+            liveness_probe: {
+                enabled: 'boolean',
+                interval_seconds: 'number (5-60)',
+                timeout_seconds: 'number (1-10)',
+                failure_threshold: 'number (3-10)',
+                success_threshold: 'number (1-3)',
+            },
+            readiness_probe: {
+                enabled: 'boolean',
+                interval_seconds: 'number (5-30)',
+                timeout_seconds: 'number (1-10)',
+                failure_threshold: 'number (3-10)',
+            },
+            health_thresholds: {
+                cpu_warning: 'number (70-85)',
+                cpu_critical: 'number (85-95)',
+                memory_warning: 'number (70-85)',
+                memory_critical: 'number (85-95)',
+                error_rate_threshold: 'number (5-20)',
+            },
+            auto_recovery_enabled: 'boolean',
+            health_status: 'healthy | degraded | unhealthy | unknown',
+        },
+    });
+    // Generate health time series
+    const healthTimeSeries = await synth.generateTimeSeries({
+        count: 200,
+        interval: '1m',
+        metrics: [
+            'healthy_agents',
+            'degraded_agents',
+            'unhealthy_agents',
+            'avg_health_score',
+            'failed_checks',
+            'recovery_actions',
+        ],
+        trend: 'stable',
+    });
+    // Generate auto-healing actions
+    const healingActions = await synth.generateStructured({
+        count: 50,
+        schema: {
+            action_id: 'UUID',
+            agent_id: 'agent-{1-80}',
+            trigger_check_id: 'UUID (from healthChecks)',
+            action_type: 'restart | reset_state | scale_resources | failover | isolate',
+            severity: 'low | medium | high | critical',
+            executed_at: 'ISO timestamp',
+            duration_ms: 'number (100-30000)',
+            success: 'boolean (85% true)',
+            health_before: 'number (0-50)',
+            health_after: 'number (51-100) or same as health_before',
+            side_effects: ['array of 0-2 side effects or empty'],
+        },
+    });
+    console.log('Health Check Analysis:');
+    console.log(`- Health checks: ${healthChecks.data.length}`);
+    console.log(`- Agent configs: ${healthConfigs.data.length}`);
+    console.log(`- Time series points: ${healthTimeSeries.data.length}`);
+    console.log(`- Healing actions: ${healingActions.data.length}`);
+    // Analyze health check results
+    const passedChecks = healthChecks.data.filter((c) => c.event_type === 'health_check_passed').length;
+    const failedChecks = healthChecks.data.filter((c) => c.event_type === 'health_check_failed').length;
+    console.log(`\nHealth Statistics:`);
+    console.log(`- Passed: ${passedChecks} (${((passedChecks / healthChecks.data.length) * 100).toFixed(1)}%)`);
+    console.log(`- Failed: ${failedChecks} (${((failedChecks / healthChecks.data.length) * 100).toFixed(1)}%)`);
+    // Auto-healing effectiveness
+    const successfulHealing = healingActions.data.filter((a) => a.success).length;
+    const avgHealthImprovement = healingActions.data
+        .filter((a) => a.success)
+        .reduce((sum, a) => sum + (a.health_after - a.health_before), 0) / successfulHealing;
+    console.log(`\nAuto-Healing:`);
+    console.log(`- Actions taken: ${healingActions.data.length}`);
+    console.log(`- Success rate: ${((successfulHealing / healingActions.data.length) * 100).toFixed(1)}%`);
+    console.log(`- Average health improvement: +${avgHealthImprovement.toFixed(1)}`);
+    // Kubernetes health checks
+    console.log('\nKubernetes Integration:');
+    console.log('// Define health probes in pod spec');
+    console.log('livenessProbe: { httpGet: { path: "/health", port: 8080 } }');
+    console.log('readinessProbe: { httpGet: { path: "/ready", port: 8080 } }');
+    return { healthChecks, healthConfigs, healthTimeSeries, healingActions };
+}
+// ============================================================================
+// Example 4: Recovery Patterns
+// ============================================================================
+/**
+ * Generate failure recovery pattern data
+ */
+async function recoveryPatterns() {
+    console.log('\n🔧 Example 4: Recovery Patterns\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate failure scenarios
+    const failures = await synth.generateStructured({
+        count: 100,
+        schema: {
+            failure_id: 'UUID',
+            agent_id: 'agent-{1-60}',
+            failure_type: 'crash | hang | memory_leak | resource_exhaustion | network_partition | data_corruption',
+            severity: 'critical | high | medium | low',
+            impact_scope: 'single_agent | cluster | region | global',
+            affected_tasks: ['array of 0-20 task ids'],
+            data_at_risk: 'boolean',
+            detected_at: 'ISO timestamp',
+            detection_method: 'health_check | monitoring | user_report | self_reported',
+            mttr_seconds: 'number (10-3600)',
+            mttd_seconds: 'number (1-600)',
+        },
+    });
+    // Generate recovery strategies
+    const recoveryStrategies = await synth.generateStructured({
+        count: failures.data.length,
+        schema: {
+            strategy_id: 'UUID',
+            failure_id: 'UUID (from failures)',
+            strategy_type: 'restart | failover | rollback | rebuild | manual_intervention',
+            phases: [
+                {
+                    phase_name: 'detection | isolation | recovery | verification | cleanup',
+                    actions: ['array of 2-5 actions'],
+                    duration_ms: 'number (100-60000)',
+                    success: 'boolean',
+                },
+            ],
+            recovery_time_objective_seconds: 'number (60-3600)',
+            recovery_point_objective_seconds: 'number (0-1800)',
+            actual_recovery_time_seconds: 'number',
+            data_loss_amount: 'number (0-1000 MB) or 0',
+            automatic: 'boolean (80% true)',
+            success: 'boolean (90% true)',
+            lessons_learned: ['array of 2-4 lessons'],
+        },
+    });
+    // Generate circuit breaker states
+    const circuitBreakers = await synth.generateTimeSeries({
+        count: 150,
+        interval: '2m',
+        metrics: [
+            'closed_circuits',
+            'open_circuits',
+            'half_open_circuits',
+            'total_requests',
+            'failed_requests',
+            'trips_per_interval',
+        ],
+        trend: 'mixed',
+    });
+    // Generate backup and restore operations
+    const backupOperations = await synth.generateStructured({
+        count: 200,
+        schema: {
+            operation_id: 'UUID',
+            operation_type: 'backup | restore | verify | cleanup',
+            agent_id: 'agent-{1-60}',
+            backup_id: 'backup-UUID',
+            data_size_mb: 'number (10-5000)',
+            duration_ms: 'number (1000-300000)',
+            storage_location: 'local | s3 | gcs | azure_blob',
+            compression_enabled: 'boolean',
+            encryption_enabled: 'boolean',
+            success: 'boolean (95% true)',
+            timestamp: 'ISO timestamp',
+        },
+    });
+    console.log('Recovery Pattern Analysis:');
+    console.log(`- Failures recorded: ${failures.data.length}`);
+    console.log(`- Recovery strategies: ${recoveryStrategies.data.length}`);
+    console.log(`- Circuit breaker metrics: ${circuitBreakers.data.length}`);
+    console.log(`- Backup operations: ${backupOperations.data.length}`);
+    // Analyze recovery effectiveness
+    const successfulRecoveries = recoveryStrategies.data.filter((s) => s.success).length;
+    const automaticRecoveries = recoveryStrategies.data.filter((s) => s.automatic).length;
+    console.log(`\nRecovery Effectiveness:`);
+    console.log(`- Success rate: ${((successfulRecoveries / recoveryStrategies.data.length) * 100).toFixed(1)}%`);
+    console.log(`- Automatic recoveries: ${((automaticRecoveries / recoveryStrategies.data.length) * 100).toFixed(1)}%`);
+    // Calculate average recovery times
+    const avgMTTR = failures.data.reduce((sum, f) => sum + f.mttr_seconds, 0) / failures.data.length;
+    const avgMTTD = failures.data.reduce((sum, f) => sum + f.mttd_seconds, 0) / failures.data.length;
+    console.log(`- Average MTTR: ${avgMTTR.toFixed(0)} seconds`);
+    console.log(`- Average MTTD: ${avgMTTD.toFixed(0)} seconds`);
+    // Data loss analysis
+    const dataLossIncidents = recoveryStrategies.data.filter((s) => s.data_loss_amount > 0).length;
+    console.log(`\nData Protection:`);
+    console.log(`- Incidents with data loss: ${dataLossIncidents} (${((dataLossIncidents / recoveryStrategies.data.length) * 100).toFixed(1)}%)`);
+    console.log(`- Successful backups: ${backupOperations.data.filter((b) => b.operation_type === 'backup' && b.success).length}`);
+    console.log('\nRecovery Pattern Implementation:');
+    console.log('// Implement circuit breaker pattern');
+    console.log('// Use claude-flow hooks for automatic recovery');
+    console.log('npx claude-flow@alpha hooks enable --type "error-recovery"');
+    return { failures, recoveryStrategies, circuitBreakers, backupOperations };
+}
+// ============================================================================
+// Example 5: Version Migration
+// ============================================================================
+/**
+ * Generate agent version migration data
+ */
+async function versionMigration() {
+    console.log('\n🔄 Example 5: Version Migration\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate version information
+    const versions = await synth.generateStructured({
+        count: 10,
+        schema: {
+            version_id: 'UUID',
+            version_number: 'semantic version (e.g., 2.5.1)',
+            release_date: 'ISO timestamp',
+            changes: {
+                features: ['array of 2-5 new features'],
+                improvements: ['array of 1-4 improvements'],
+                bug_fixes: ['array of 2-6 bug fixes'],
+                breaking_changes: ['array of 0-2 breaking changes or empty'],
+            },
+            compatibility: {
+                backward_compatible: 'boolean',
+                migration_required: 'boolean',
+                rollback_supported: 'boolean',
+            },
+            deployment_strategy: 'blue_green | rolling | canary | recreate',
+        },
+    });
+    // Generate migration operations
+    const migrations = await synth.generateStructured({
+        count: 50,
+        schema: {
+            migration_id: 'UUID',
+            from_version: 'semantic version',
+            to_version: 'semantic version',
+            agent_id: 'agent-{1-100}',
+            migration_type: 'in_place | side_by_side | recreate',
+            migration_steps: [
+                {
+                    step_id: 'UUID',
+                    step_name: 'descriptive step name',
+                    step_type: 'backup | stop | migrate_data | update_code | restart | verify',
+                    duration_ms: 'number (100-60000)',
+                    success: 'boolean',
+                    rollback_supported: 'boolean',
+                },
+            ],
+            downtime_ms: 'number (0-120000)',
+            data_migrated_mb: 'number (0-1000)',
+            overall_status: 'in_progress | completed | failed | rolled_back',
+            started_at: 'ISO timestamp',
+            completed_at: 'ISO timestamp or null',
+        },
+        constraints: [
+            'Migration should have 4-8 steps',
+            '85% of migrations should complete successfully',
+            'Failed migrations should support rollback',
+        ],
+    });
+    // Generate canary deployment data
+    const canaryDeployments = await synth.generateStructured({
+        count: 15,
+        schema: {
+            deployment_id: 'UUID',
+            version: 'semantic version',
+            canary_percentage: 'number (5-50)',
+            canary_agents: ['array of agent ids'],
+            stable_agents: ['array of agent ids'],
+            metrics_comparison: {
+                canary_error_rate: 'number (0-10)',
+                stable_error_rate: 'number (0-10)',
+                canary_latency_p99: 'number (10-1000)',
+                stable_latency_p99: 'number (10-1000)',
+                canary_throughput: 'number (100-10000)',
+                stable_throughput: 'number (100-10000)',
+            },
+            decision: 'promote | hold | rollback',
+            decision_reason: 'detailed reason',
+            monitoring_duration_minutes: 'number (30-1440)',
+        },
+    });
+    // Generate rollback events
+    const rollbacks = await synth.generateStructured({
+        count: 10,
+        schema: {
+            rollback_id: 'UUID',
+            migration_id: 'UUID (from migrations)',
+            trigger: 'high_error_rate | performance_degradation | manual | data_inconsistency',
+            rollback_strategy: 'automated | manual',
+            affected_agents: ['array of 5-30 agent ids'],
+            rollback_duration_ms: 'number (5000-300000)',
+            data_reverted_mb: 'number (0-1000)',
+            success: 'boolean (95% true)',
+            timestamp: 'ISO timestamp',
+        },
+    });
+    console.log('Version Migration Analysis:');
+    console.log(`- Versions tracked: ${versions.data.length}`);
+    console.log(`- Migrations executed: ${migrations.data.length}`);
+    console.log(`- Canary deployments: ${canaryDeployments.data.length}`);
+    console.log(`- Rollbacks: ${rollbacks.data.length}`);
+    // Analyze migration success
+    const successfulMigrations = migrations.data.filter((m) => m.overall_status === 'completed').length;
+    const failedMigrations = migrations.data.filter((m) => m.overall_status === 'failed').length;
+    console.log(`\nMigration Success Rate:`);
+    console.log(`- Completed: ${successfulMigrations} (${((successfulMigrations / migrations.data.length) * 100).toFixed(1)}%)`);
+    console.log(`- Failed: ${failedMigrations}`);
+    console.log(`- Rolled back: ${migrations.data.filter((m) => m.overall_status === 'rolled_back').length}`);
+    // Analyze downtime
+    const avgDowntime = migrations.data
+        .filter((m) => m.overall_status === 'completed')
+        .reduce((sum, m) => sum + m.downtime_ms, 0) / successfulMigrations;
+    const zeroDowntime = migrations.data.filter((m) => m.downtime_ms === 0).length;
+    console.log(`\nDowntime Analysis:`);
+    console.log(`- Average downtime: ${(avgDowntime / 1000).toFixed(1)} seconds`);
+    console.log(`- Zero-downtime migrations: ${zeroDowntime} (${((zeroDowntime / migrations.data.length) * 100).toFixed(1)}%)`);
+    // Canary deployment decisions
+    const promoted = canaryDeployments.data.filter((c) => c.decision === 'promote').length;
+    const rolledBack = canaryDeployments.data.filter((c) => c.decision === 'rollback').length;
+    console.log(`\nCanary Deployment Decisions:`);
+    console.log(`- Promoted: ${promoted} (${((promoted / canaryDeployments.data.length) * 100).toFixed(1)}%)`);
+    console.log(`- Rolled back: ${rolledBack} (${((rolledBack / canaryDeployments.data.length) * 100).toFixed(1)}%)`);
+    console.log('\nDeployment Integration:');
+    console.log('// Blue-green deployment with Kubernetes');
+    console.log('kubectl apply -f deployment-v2.yaml');
+    console.log('kubectl set image deployment/agents agent=image:v2');
+    console.log('// Monitor and rollback if needed');
+    return { versions, migrations, canaryDeployments, rollbacks };
+}
+// ============================================================================
+// Run All Examples
+// ============================================================================
+async function runAllLifecycleExamples() {
+    console.log('🚀 Running All Agent Lifecycle Examples\n');
+    console.log('='.repeat(70));
+    try {
+        await agentSpawningTermination();
+        console.log('='.repeat(70));
+        await stateSynchronization();
+        console.log('='.repeat(70));
+        await healthCheckScenarios();
+        console.log('='.repeat(70));
+        await recoveryPatterns();
+        console.log('='.repeat(70));
+        await versionMigration();
+        console.log('='.repeat(70));
+        console.log('\n✅ All agent lifecycle examples completed!\n');
+    }
+    catch (error) {
+        console.error('❌ Error running examples:', error.message);
+        throw error;
+    }
+}
+// Run if executed directly
+if (import.meta.url === `file://${process.argv[1]}`) {
+    runAllLifecycleExamples().catch(console.error);
+}
+//# sourceMappingURL=agent-lifecycle.js.map

vendor/ruvector/npm/packages/agentic-synth/examples/swarms/agent-lifecycle.ts

@@ -0,0 +1,763 @@
+/**
+ * Agent Lifecycle Management Examples
+ *
+ * Demonstrates agent lifecycle patterns including spawning/termination,
+ * state synchronization, health checks, recovery patterns, and
+ * version migration for dynamic agent systems.
+ *
+ * Integrates with:
+ * - claude-flow: Agent lifecycle hooks and state management
+ * - ruv-swarm: Dynamic agent spawning and coordination
+ * - Kubernetes: Container orchestration patterns
+ */
+
+import { AgenticSynth, createSynth } from '../../dist/index.js';
+import type { GenerationResult } from '../../src/types.js';
+
+// ============================================================================
+// Example 1: Agent Spawning and Termination
+// ============================================================================
+
+/**
+ * Generate agent spawning and termination lifecycle events
+ */
+export async function agentSpawningTermination() {
+  console.log('\n🚀 Example 1: Agent Spawning and Termination\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    cacheStrategy: 'memory',
+  });
+
+  // Generate agent lifecycle events
+  const lifecycleEvents = await synth.generateEvents({
+    count: 500,
+    eventTypes: [
+      'agent_spawn_requested',
+      'agent_initializing',
+      'agent_ready',
+      'agent_active',
+      'agent_idle',
+      'agent_terminating',
+      'agent_terminated',
+      'agent_failed',
+    ],
+    schema: {
+      event_id: 'UUID',
+      agent_id: 'agent-{1-100}',
+      event_type: 'one of eventTypes',
+      reason: 'spawn reason or termination reason',
+      requested_by: 'coordinator | auto_scaler | user | system',
+      resource_allocation: {
+        cpu_cores: 'number (0.5-8.0)',
+        memory_mb: 'number (512-8192)',
+        disk_mb: 'number (1024-10240)',
+      },
+      initialization_config: {
+        agent_type: 'worker | coordinator | specialist | observer',
+        capabilities: ['array of 1-5 capabilities'],
+        priority: 'high | medium | low',
+        max_lifetime_minutes: 'number (60-14400) or null',
+      },
+      state_data_size_mb: 'number (0-1000)',
+      startup_time_ms: 'number (100-10000)',
+      shutdown_time_ms: 'number (50-5000)',
+      exit_code: 'number (0-255) or null',
+      timestamp: 'ISO timestamp',
+    },
+    distribution: 'poisson',
+    timeRange: {
+      start: new Date(Date.now() - 86400000), // Last 24 hours
+      end: new Date(),
+    },
+  });
+
+  // Generate agent spawn strategies
+  const spawnStrategies = await synth.generateStructured({
+    count: 20,
+    schema: {
+      strategy_id: 'UUID',
+      strategy_name: 'descriptive name',
+      trigger_type: 'manual | scheduled | load_based | event_driven | predictive',
+      conditions: ['array of 2-4 conditions'],
+      agent_template: {
+        agent_type: 'worker | coordinator | specialist | observer',
+        base_config: 'JSON configuration object',
+        scaling_limits: {
+          min_instances: 'number (1-5)',
+          max_instances: 'number (10-100)',
+          scale_up_threshold: 'number (0-100)',
+          scale_down_threshold: 'number (0-100)',
+        },
+      },
+      spawn_pattern: 'immediate | gradual | burst',
+      cooldown_seconds: 'number (30-600)',
+      success_rate: 'number (0-100)',
+      avg_spawn_time_ms: 'number (500-15000)',
+    },
+  });
+
+  // Generate resource pool state
+  const resourcePool = await synth.generateTimeSeries({
+    count: 100,
+    interval: '5m',
+    metrics: [
+      'active_agents',
+      'spawning_agents',
+      'terminating_agents',
+      'failed_spawns',
+      'total_cpu_usage',
+      'total_memory_mb',
+      'available_slots',
+    ],
+    trend: 'mixed',
+  });
+
+  console.log('Agent Lifecycle Analysis:');
+  console.log(`- Lifecycle events: ${lifecycleEvents.data.length}`);
+  console.log(`- Spawn strategies: ${spawnStrategies.data.length}`);
+  console.log(`- Resource pool snapshots: ${resourcePool.data.length}`);
+
+  // Analyze lifecycle events
+  const spawnedCount = lifecycleEvents.data.filter(
+    (e: any) => e.event_type === 'agent_ready'
+  ).length;
+  const terminatedCount = lifecycleEvents.data.filter(
+    (e: any) => e.event_type === 'agent_terminated'
+  ).length;
+  const failedCount = lifecycleEvents.data.filter(
+    (e: any) => e.event_type === 'agent_failed'
+  ).length;
+
+  console.log(`\nLifecycle Statistics:`);
+  console.log(`- Successfully spawned: ${spawnedCount}`);
+  console.log(`- Terminated: ${terminatedCount}`);
+  console.log(`- Failed: ${failedCount} (${((failedCount / lifecycleEvents.data.length) * 100).toFixed(1)}%)`);
+
+  // Calculate average spawn time
+  const avgSpawnTime = lifecycleEvents.data
+    .filter((e: any) => e.startup_time_ms)
+    .reduce((sum: number, e: any) => sum + e.startup_time_ms, 0) / spawnedCount;
+
+  console.log(`- Average spawn time: ${avgSpawnTime.toFixed(0)}ms`);
+
+  // Claude-Flow integration
+  console.log('\nClaude-Flow Integration:');
+  console.log('npx claude-flow@alpha hooks pre-task --spawn-agents 5');
+  console.log('// MCP: agent_spawn with configuration');
+  console.log('npx claude-flow@alpha hooks post-task --cleanup-agents true');
+
+  return { lifecycleEvents, spawnStrategies, resourcePool };
+}
+
+// ============================================================================
+// Example 2: State Synchronization
+// ============================================================================
+
+/**
+ * Generate state synchronization data for distributed agents
+ */
+export async function stateSynchronization() {
+  console.log('\n🔄 Example 2: State Synchronization\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate agent state snapshots
+  const stateSnapshots = await synth.generateStructured({
+    count: 500,
+    schema: {
+      snapshot_id: 'UUID',
+      agent_id: 'agent-{1-50}',
+      version: 'number (1-1000)',
+      state_type: 'full | incremental | checkpoint',
+      state_data: {
+        memory: {
+          short_term: 'JSON object',
+          long_term: 'JSON object',
+          working_set_size_mb: 'number (1-500)',
+        },
+        task_queue: ['array of 0-20 task ids'],
+        active_connections: ['array of 0-10 agent ids'],
+        performance_metrics: {
+          tasks_completed: 'number (0-1000)',
+          cpu_usage: 'number (0-100)',
+          memory_usage: 'number (0-100)',
+        },
+        custom_state: 'JSON object',
+      },
+      state_size_bytes: 'number (1024-10485760)',
+      compression_ratio: 'number (0.3-1.0)',
+      checksum: 'SHA-256 hash',
+      created_at: 'ISO timestamp',
+    },
+  });
+
+  // Generate synchronization events
+  const syncEvents = await synth.generateEvents({
+    count: 1000,
+    eventTypes: [
+      'state_saved',
+      'state_loaded',
+      'state_replicated',
+      'state_conflict',
+      'state_merged',
+      'state_rollback',
+    ],
+    schema: {
+      event_id: 'UUID',
+      event_type: 'one of eventTypes',
+      agent_id: 'agent-{1-50}',
+      snapshot_id: 'UUID (from stateSnapshots)',
+      target_location: 'local | remote | backup | replica-{1-5}',
+      sync_strategy: 'optimistic | pessimistic | eventual | strong',
+      duration_ms: 'number (10-5000)',
+      data_transferred_mb: 'number (0.1-100)',
+      success: 'boolean (95% true)',
+      conflict_resolved: 'boolean or null',
+      timestamp: 'ISO timestamp',
+    },
+    distribution: 'poisson',
+  });
+
+  // Generate state consistency checks
+  const consistencyChecks = await synth.generateStructured({
+    count: 100,
+    schema: {
+      check_id: 'UUID',
+      check_type: 'integrity | consistency | replication | divergence',
+      agents_checked: ['array of 3-10 agent ids'],
+      snapshot_versions: ['array of version numbers'],
+      consistency_score: 'number (0-100)',
+      divergent_agents: ['array of 0-3 agent ids or empty'],
+      anomalies_detected: ['array of 0-2 anomaly descriptions or empty'],
+      corrective_action: 'none | resync | repair | rollback | null',
+      duration_ms: 'number (100-10000)',
+      timestamp: 'ISO timestamp',
+    },
+  });
+
+  // Generate state synchronization topology
+  const syncTopology = await synth.generateStructured({
+    count: 1,
+    schema: {
+      topology_id: 'UUID',
+      sync_pattern: 'star | mesh | ring | hierarchical | hybrid',
+      nodes: [
+        {
+          node_id: 'node-{1-10}',
+          role: 'primary | replica | backup | cache',
+          agents_hosted: ['array of 5-10 agent ids'],
+          sync_frequency_seconds: 'number (1-300)',
+          replication_lag_ms: 'number (0-1000)',
+          storage_capacity_gb: 'number (10-1000)',
+          storage_used_gb: 'number (proportional to capacity)',
+        },
+      ],
+      sync_protocol: 'raft | paxos | gossip | two_phase_commit',
+      conflict_resolution: 'last_write_wins | merge | vector_clock | manual',
+    },
+  });
+
+  console.log('State Synchronization Analysis:');
+  console.log(`- State snapshots: ${stateSnapshots.data.length}`);
+  console.log(`- Sync events: ${syncEvents.data.length}`);
+  console.log(`- Consistency checks: ${consistencyChecks.data.length}`);
+
+  // Analyze synchronization success
+  const successfulSyncs = syncEvents.data.filter((e: any) => e.success).length;
+  const avgSyncTime = syncEvents.data.reduce(
+    (sum: number, e: any) => sum + e.duration_ms,
+    0
+  ) / syncEvents.data.length;
+
+  console.log(`\nSynchronization Metrics:`);
+  console.log(`- Success rate: ${((successfulSyncs / syncEvents.data.length) * 100).toFixed(1)}%`);
+  console.log(`- Average sync time: ${avgSyncTime.toFixed(0)}ms`);
+
+  // Consistency analysis
+  const avgConsistency = consistencyChecks.data.reduce(
+    (sum: number, c: any) => sum + c.consistency_score,
+    0
+  ) / consistencyChecks.data.length;
+
+  console.log(`- Average consistency score: ${avgConsistency.toFixed(1)}/100`);
+  console.log(`- Anomalies detected: ${consistencyChecks.data.filter((c: any) => c.anomalies_detected.length > 0).length}`);
+
+  // Integration pattern
+  console.log('\nState Sync Integration:');
+  console.log('// Store state in distributed storage (Redis, etcd)');
+  console.log('await redis.set(`agent:${agentId}:state`, JSON.stringify(state));');
+  console.log('// Use claude-flow memory for coordination state');
+  console.log('npx claude-flow@alpha hooks session-end --export-state true');
+
+  return { stateSnapshots, syncEvents, consistencyChecks, syncTopology };
+}
+
+// ============================================================================
+// Example 3: Health Check Scenarios
+// ============================================================================
+
+/**
+ * Generate health check and monitoring data
+ */
+export async function healthCheckScenarios() {
+  console.log('\n💊 Example 3: Health Check Scenarios\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate health check results
+  const healthChecks = await synth.generateEvents({
+    count: 1000,
+    eventTypes: [
+      'health_check_passed',
+      'health_check_degraded',
+      'health_check_failed',
+      'health_check_timeout',
+    ],
+    schema: {
+      check_id: 'UUID',
+      agent_id: 'agent-{1-80}',
+      event_type: 'one of eventTypes',
+      check_type: 'liveness | readiness | startup | deep',
+      health_score: 'number (0-100)',
+      metrics: {
+        response_time_ms: 'number (1-5000)',
+        cpu_usage: 'number (0-100)',
+        memory_usage: 'number (0-100)',
+        active_connections: 'number (0-100)',
+        error_rate: 'number (0-100)',
+        queue_depth: 'number (0-1000)',
+      },
+      issues_detected: ['array of 0-3 issue descriptions or empty'],
+      recovery_actions: ['array of 0-2 actions or empty'],
+      timestamp: 'ISO timestamp',
+    },
+    distribution: 'poisson',
+  });
+
+  // Generate health monitoring configuration
+  const healthConfigs = await synth.generateStructured({
+    count: 50,
+    schema: {
+      agent_id: 'agent-{1-50}',
+      liveness_probe: {
+        enabled: 'boolean',
+        interval_seconds: 'number (5-60)',
+        timeout_seconds: 'number (1-10)',
+        failure_threshold: 'number (3-10)',
+        success_threshold: 'number (1-3)',
+      },
+      readiness_probe: {
+        enabled: 'boolean',
+        interval_seconds: 'number (5-30)',
+        timeout_seconds: 'number (1-10)',
+        failure_threshold: 'number (3-10)',
+      },
+      health_thresholds: {
+        cpu_warning: 'number (70-85)',
+        cpu_critical: 'number (85-95)',
+        memory_warning: 'number (70-85)',
+        memory_critical: 'number (85-95)',
+        error_rate_threshold: 'number (5-20)',
+      },
+      auto_recovery_enabled: 'boolean',
+      health_status: 'healthy | degraded | unhealthy | unknown',
+    },
+  });
+
+  // Generate health time series
+  const healthTimeSeries = await synth.generateTimeSeries({
+    count: 200,
+    interval: '1m',
+    metrics: [
+      'healthy_agents',
+      'degraded_agents',
+      'unhealthy_agents',
+      'avg_health_score',
+      'failed_checks',
+      'recovery_actions',
+    ],
+    trend: 'stable',
+  });
+
+  // Generate auto-healing actions
+  const healingActions = await synth.generateStructured({
+    count: 50,
+    schema: {
+      action_id: 'UUID',
+      agent_id: 'agent-{1-80}',
+      trigger_check_id: 'UUID (from healthChecks)',
+      action_type: 'restart | reset_state | scale_resources | failover | isolate',
+      severity: 'low | medium | high | critical',
+      executed_at: 'ISO timestamp',
+      duration_ms: 'number (100-30000)',
+      success: 'boolean (85% true)',
+      health_before: 'number (0-50)',
+      health_after: 'number (51-100) or same as health_before',
+      side_effects: ['array of 0-2 side effects or empty'],
+    },
+  });
+
+  console.log('Health Check Analysis:');
+  console.log(`- Health checks: ${healthChecks.data.length}`);
+  console.log(`- Agent configs: ${healthConfigs.data.length}`);
+  console.log(`- Time series points: ${healthTimeSeries.data.length}`);
+  console.log(`- Healing actions: ${healingActions.data.length}`);
+
+  // Analyze health check results
+  const passedChecks = healthChecks.data.filter(
+    (c: any) => c.event_type === 'health_check_passed'
+  ).length;
+  const failedChecks = healthChecks.data.filter(
+    (c: any) => c.event_type === 'health_check_failed'
+  ).length;
+
+  console.log(`\nHealth Statistics:`);
+  console.log(`- Passed: ${passedChecks} (${((passedChecks / healthChecks.data.length) * 100).toFixed(1)}%)`);
+  console.log(`- Failed: ${failedChecks} (${((failedChecks / healthChecks.data.length) * 100).toFixed(1)}%)`);
+
+  // Auto-healing effectiveness
+  const successfulHealing = healingActions.data.filter((a: any) => a.success).length;
+  const avgHealthImprovement = healingActions.data
+    .filter((a: any) => a.success)
+    .reduce((sum: number, a: any) => sum + (a.health_after - a.health_before), 0) / successfulHealing;
+
+  console.log(`\nAuto-Healing:`);
+  console.log(`- Actions taken: ${healingActions.data.length}`);
+  console.log(`- Success rate: ${((successfulHealing / healingActions.data.length) * 100).toFixed(1)}%`);
+  console.log(`- Average health improvement: +${avgHealthImprovement.toFixed(1)}`);
+
+  // Kubernetes health checks
+  console.log('\nKubernetes Integration:');
+  console.log('// Define health probes in pod spec');
+  console.log('livenessProbe: { httpGet: { path: "/health", port: 8080 } }');
+  console.log('readinessProbe: { httpGet: { path: "/ready", port: 8080 } }');
+
+  return { healthChecks, healthConfigs, healthTimeSeries, healingActions };
+}
+
+// ============================================================================
+// Example 4: Recovery Patterns
+// ============================================================================
+
+/**
+ * Generate failure recovery pattern data
+ */
+export async function recoveryPatterns() {
+  console.log('\n🔧 Example 4: Recovery Patterns\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate failure scenarios
+  const failures = await synth.generateStructured({
+    count: 100,
+    schema: {
+      failure_id: 'UUID',
+      agent_id: 'agent-{1-60}',
+      failure_type: 'crash | hang | memory_leak | resource_exhaustion | network_partition | data_corruption',
+      severity: 'critical | high | medium | low',
+      impact_scope: 'single_agent | cluster | region | global',
+      affected_tasks: ['array of 0-20 task ids'],
+      data_at_risk: 'boolean',
+      detected_at: 'ISO timestamp',
+      detection_method: 'health_check | monitoring | user_report | self_reported',
+      mttr_seconds: 'number (10-3600)',
+      mttd_seconds: 'number (1-600)',
+    },
+  });
+
+  // Generate recovery strategies
+  const recoveryStrategies = await synth.generateStructured({
+    count: failures.data.length,
+    schema: {
+      strategy_id: 'UUID',
+      failure_id: 'UUID (from failures)',
+      strategy_type: 'restart | failover | rollback | rebuild | manual_intervention',
+      phases: [
+        {
+          phase_name: 'detection | isolation | recovery | verification | cleanup',
+          actions: ['array of 2-5 actions'],
+          duration_ms: 'number (100-60000)',
+          success: 'boolean',
+        },
+      ],
+      recovery_time_objective_seconds: 'number (60-3600)',
+      recovery_point_objective_seconds: 'number (0-1800)',
+      actual_recovery_time_seconds: 'number',
+      data_loss_amount: 'number (0-1000 MB) or 0',
+      automatic: 'boolean (80% true)',
+      success: 'boolean (90% true)',
+      lessons_learned: ['array of 2-4 lessons'],
+    },
+  });
+
+  // Generate circuit breaker states
+  const circuitBreakers = await synth.generateTimeSeries({
+    count: 150,
+    interval: '2m',
+    metrics: [
+      'closed_circuits',
+      'open_circuits',
+      'half_open_circuits',
+      'total_requests',
+      'failed_requests',
+      'trips_per_interval',
+    ],
+    trend: 'mixed',
+  });
+
+  // Generate backup and restore operations
+  const backupOperations = await synth.generateStructured({
+    count: 200,
+    schema: {
+      operation_id: 'UUID',
+      operation_type: 'backup | restore | verify | cleanup',
+      agent_id: 'agent-{1-60}',
+      backup_id: 'backup-UUID',
+      data_size_mb: 'number (10-5000)',
+      duration_ms: 'number (1000-300000)',
+      storage_location: 'local | s3 | gcs | azure_blob',
+      compression_enabled: 'boolean',
+      encryption_enabled: 'boolean',
+      success: 'boolean (95% true)',
+      timestamp: 'ISO timestamp',
+    },
+  });
+
+  console.log('Recovery Pattern Analysis:');
+  console.log(`- Failures recorded: ${failures.data.length}`);
+  console.log(`- Recovery strategies: ${recoveryStrategies.data.length}`);
+  console.log(`- Circuit breaker metrics: ${circuitBreakers.data.length}`);
+  console.log(`- Backup operations: ${backupOperations.data.length}`);
+
+  // Analyze recovery effectiveness
+  const successfulRecoveries = recoveryStrategies.data.filter((s: any) => s.success).length;
+  const automaticRecoveries = recoveryStrategies.data.filter((s: any) => s.automatic).length;
+
+  console.log(`\nRecovery Effectiveness:`);
+  console.log(`- Success rate: ${((successfulRecoveries / recoveryStrategies.data.length) * 100).toFixed(1)}%`);
+  console.log(`- Automatic recoveries: ${((automaticRecoveries / recoveryStrategies.data.length) * 100).toFixed(1)}%`);
+
+  // Calculate average recovery times
+  const avgMTTR = failures.data.reduce((sum: number, f: any) => sum + f.mttr_seconds, 0) / failures.data.length;
+  const avgMTTD = failures.data.reduce((sum: number, f: any) => sum + f.mttd_seconds, 0) / failures.data.length;
+
+  console.log(`- Average MTTR: ${avgMTTR.toFixed(0)} seconds`);
+  console.log(`- Average MTTD: ${avgMTTD.toFixed(0)} seconds`);
+
+  // Data loss analysis
+  const dataLossIncidents = recoveryStrategies.data.filter(
+    (s: any) => s.data_loss_amount > 0
+  ).length;
+
+  console.log(`\nData Protection:`);
+  console.log(`- Incidents with data loss: ${dataLossIncidents} (${((dataLossIncidents / recoveryStrategies.data.length) * 100).toFixed(1)}%)`);
+  console.log(`- Successful backups: ${backupOperations.data.filter((b: any) => b.operation_type === 'backup' && b.success).length}`);
+
+  console.log('\nRecovery Pattern Implementation:');
+  console.log('// Implement circuit breaker pattern');
+  console.log('// Use claude-flow hooks for automatic recovery');
+  console.log('npx claude-flow@alpha hooks enable --type "error-recovery"');
+
+  return { failures, recoveryStrategies, circuitBreakers, backupOperations };
+}
+
+// ============================================================================
+// Example 5: Version Migration
+// ============================================================================
+
+/**
+ * Generate agent version migration data
+ */
+export async function versionMigration() {
+  console.log('\n🔄 Example 5: Version Migration\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate version information
+  const versions = await synth.generateStructured({
+    count: 10,
+    schema: {
+      version_id: 'UUID',
+      version_number: 'semantic version (e.g., 2.5.1)',
+      release_date: 'ISO timestamp',
+      changes: {
+        features: ['array of 2-5 new features'],
+        improvements: ['array of 1-4 improvements'],
+        bug_fixes: ['array of 2-6 bug fixes'],
+        breaking_changes: ['array of 0-2 breaking changes or empty'],
+      },
+      compatibility: {
+        backward_compatible: 'boolean',
+        migration_required: 'boolean',
+        rollback_supported: 'boolean',
+      },
+      deployment_strategy: 'blue_green | rolling | canary | recreate',
+    },
+  });
+
+  // Generate migration operations
+  const migrations = await synth.generateStructured({
+    count: 50,
+    schema: {
+      migration_id: 'UUID',
+      from_version: 'semantic version',
+      to_version: 'semantic version',
+      agent_id: 'agent-{1-100}',
+      migration_type: 'in_place | side_by_side | recreate',
+      migration_steps: [
+        {
+          step_id: 'UUID',
+          step_name: 'descriptive step name',
+          step_type: 'backup | stop | migrate_data | update_code | restart | verify',
+          duration_ms: 'number (100-60000)',
+          success: 'boolean',
+          rollback_supported: 'boolean',
+        },
+      ],
+      downtime_ms: 'number (0-120000)',
+      data_migrated_mb: 'number (0-1000)',
+      overall_status: 'in_progress | completed | failed | rolled_back',
+      started_at: 'ISO timestamp',
+      completed_at: 'ISO timestamp or null',
+    },
+    constraints: [
+      'Migration should have 4-8 steps',
+      '85% of migrations should complete successfully',
+      'Failed migrations should support rollback',
+    ],
+  });
+
+  // Generate canary deployment data
+  const canaryDeployments = await synth.generateStructured({
+    count: 15,
+    schema: {
+      deployment_id: 'UUID',
+      version: 'semantic version',
+      canary_percentage: 'number (5-50)',
+      canary_agents: ['array of agent ids'],
+      stable_agents: ['array of agent ids'],
+      metrics_comparison: {
+        canary_error_rate: 'number (0-10)',
+        stable_error_rate: 'number (0-10)',
+        canary_latency_p99: 'number (10-1000)',
+        stable_latency_p99: 'number (10-1000)',
+        canary_throughput: 'number (100-10000)',
+        stable_throughput: 'number (100-10000)',
+      },
+      decision: 'promote | hold | rollback',
+      decision_reason: 'detailed reason',
+      monitoring_duration_minutes: 'number (30-1440)',
+    },
+  });
+
+  // Generate rollback events
+  const rollbacks = await synth.generateStructured({
+    count: 10,
+    schema: {
+      rollback_id: 'UUID',
+      migration_id: 'UUID (from migrations)',
+      trigger: 'high_error_rate | performance_degradation | manual | data_inconsistency',
+      rollback_strategy: 'automated | manual',
+      affected_agents: ['array of 5-30 agent ids'],
+      rollback_duration_ms: 'number (5000-300000)',
+      data_reverted_mb: 'number (0-1000)',
+      success: 'boolean (95% true)',
+      timestamp: 'ISO timestamp',
+    },
+  });
+
+  console.log('Version Migration Analysis:');
+  console.log(`- Versions tracked: ${versions.data.length}`);
+  console.log(`- Migrations executed: ${migrations.data.length}`);
+  console.log(`- Canary deployments: ${canaryDeployments.data.length}`);
+  console.log(`- Rollbacks: ${rollbacks.data.length}`);
+
+  // Analyze migration success
+  const successfulMigrations = migrations.data.filter(
+    (m: any) => m.overall_status === 'completed'
+  ).length;
+  const failedMigrations = migrations.data.filter(
+    (m: any) => m.overall_status === 'failed'
+  ).length;
+
+  console.log(`\nMigration Success Rate:`);
+  console.log(`- Completed: ${successfulMigrations} (${((successfulMigrations / migrations.data.length) * 100).toFixed(1)}%)`);
+  console.log(`- Failed: ${failedMigrations}`);
+  console.log(`- Rolled back: ${migrations.data.filter((m: any) => m.overall_status === 'rolled_back').length}`);
+
+  // Analyze downtime
+  const avgDowntime = migrations.data
+    .filter((m: any) => m.overall_status === 'completed')
+    .reduce((sum: number, m: any) => sum + m.downtime_ms, 0) / successfulMigrations;
+  const zeroDowntime = migrations.data.filter((m: any) => m.downtime_ms === 0).length;
+
+  console.log(`\nDowntime Analysis:`);
+  console.log(`- Average downtime: ${(avgDowntime / 1000).toFixed(1)} seconds`);
+  console.log(`- Zero-downtime migrations: ${zeroDowntime} (${((zeroDowntime / migrations.data.length) * 100).toFixed(1)}%)`);
+
+  // Canary deployment decisions
+  const promoted = canaryDeployments.data.filter((c: any) => c.decision === 'promote').length;
+  const rolledBack = canaryDeployments.data.filter((c: any) => c.decision === 'rollback').length;
+
+  console.log(`\nCanary Deployment Decisions:`);
+  console.log(`- Promoted: ${promoted} (${((promoted / canaryDeployments.data.length) * 100).toFixed(1)}%)`);
+  console.log(`- Rolled back: ${rolledBack} (${((rolledBack / canaryDeployments.data.length) * 100).toFixed(1)}%)`);
+
+  console.log('\nDeployment Integration:');
+  console.log('// Blue-green deployment with Kubernetes');
+  console.log('kubectl apply -f deployment-v2.yaml');
+  console.log('kubectl set image deployment/agents agent=image:v2');
+  console.log('// Monitor and rollback if needed');
+
+  return { versions, migrations, canaryDeployments, rollbacks };
+}
+
+// ============================================================================
+// Run All Examples
+// ============================================================================
+
+export async function runAllLifecycleExamples() {
+  console.log('🚀 Running All Agent Lifecycle Examples\n');
+  console.log('='.repeat(70));
+
+  try {
+    await agentSpawningTermination();
+    console.log('='.repeat(70));
+
+    await stateSynchronization();
+    console.log('='.repeat(70));
+
+    await healthCheckScenarios();
+    console.log('='.repeat(70));
+
+    await recoveryPatterns();
+    console.log('='.repeat(70));
+
+    await versionMigration();
+    console.log('='.repeat(70));
+
+    console.log('\n✅ All agent lifecycle examples completed!\n');
+  } catch (error: any) {
+    console.error('❌ Error running examples:', error.message);
+    throw error;
+  }
+}
+
+// Run if executed directly
+if (import.meta.url === `file://${process.argv[1]}`) {
+  runAllLifecycleExamples().catch(console.error);
+}

vendor/ruvector/npm/packages/agentic-synth/examples/swarms/collective-intelligence.d.ts

@@ -0,0 +1,52 @@
+/**
+ * Collective Intelligence Examples
+ *
+ * Demonstrates swarm intelligence patterns including collaborative
+ * problem-solving, knowledge sharing, emergent behavior simulation,
+ * voting and consensus mechanisms, and reputation systems.
+ *
+ * Integrates with:
+ * - claude-flow: Neural pattern recognition and learning
+ * - ruv-swarm: Collective intelligence coordination
+ * - AgenticDB: Distributed knowledge storage
+ */
+/**
+ * Generate collaborative problem-solving session data
+ */
+export declare function collaborativeProblemSolving(): Promise<import("../../dist/index.js").GenerationResult<unknown>>;
+/**
+ * Generate knowledge sharing and transfer data
+ */
+export declare function knowledgeSharingPatterns(): Promise<{
+    knowledgeBase: import("../../dist/index.js").GenerationResult<unknown>;
+    transferEvents: import("../../dist/index.js").GenerationResult<unknown>;
+    agentProfiles: import("../../dist/index.js").GenerationResult<unknown>;
+}>;
+/**
+ * Generate emergent behavior patterns in swarm systems
+ */
+export declare function emergentBehaviorSimulation(): Promise<{
+    swarmStates: import("../../dist/index.js").GenerationResult<unknown>;
+    interactions: import("../../dist/index.js").GenerationResult<unknown>;
+    emergentPatterns: import("../../dist/index.js").GenerationResult<unknown>;
+    behaviorEvolution: import("../../dist/index.js").GenerationResult<unknown>;
+}>;
+/**
+ * Generate voting and consensus mechanism data
+ */
+export declare function votingAndConsensusData(): Promise<{
+    votingSessions: import("../../dist/index.js").GenerationResult<unknown>;
+    consensusMechanisms: import("../../dist/index.js").GenerationResult<unknown>;
+    votingBehavior: import("../../dist/index.js").GenerationResult<unknown>;
+}>;
+/**
+ * Generate reputation and trust system data
+ */
+export declare function reputationSystems(): Promise<{
+    reputationProfiles: import("../../dist/index.js").GenerationResult<unknown>;
+    reputationEvents: import("../../dist/index.js").GenerationResult<unknown>;
+    trustRelationships: import("../../dist/index.js").GenerationResult<unknown>;
+    reputationChanges: import("../../dist/index.js").GenerationResult<unknown>;
+}>;
+export declare function runAllCollectiveIntelligenceExamples(): Promise<void>;
+//# sourceMappingURL=collective-intelligence.d.ts.map

vendor/ruvector/npm/packages/agentic-synth/examples/swarms/collective-intelligence.d.ts.map

@@ -0,0 +1 @@
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vendor/ruvector/npm/packages/agentic-synth/examples/swarms/collective-intelligence.js

@@ -0,0 +1,578 @@
+"use strict";
+/**
+ * Collective Intelligence Examples
+ *
+ * Demonstrates swarm intelligence patterns including collaborative
+ * problem-solving, knowledge sharing, emergent behavior simulation,
+ * voting and consensus mechanisms, and reputation systems.
+ *
+ * Integrates with:
+ * - claude-flow: Neural pattern recognition and learning
+ * - ruv-swarm: Collective intelligence coordination
+ * - AgenticDB: Distributed knowledge storage
+ */
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.collaborativeProblemSolving = collaborativeProblemSolving;
+exports.knowledgeSharingPatterns = knowledgeSharingPatterns;
+exports.emergentBehaviorSimulation = emergentBehaviorSimulation;
+exports.votingAndConsensusData = votingAndConsensusData;
+exports.reputationSystems = reputationSystems;
+exports.runAllCollectiveIntelligenceExamples = runAllCollectiveIntelligenceExamples;
+const index_js_1 = require("../../dist/index.js");
+// ============================================================================
+// Example 1: Collaborative Problem-Solving
+// ============================================================================
+/**
+ * Generate collaborative problem-solving session data
+ */
+async function collaborativeProblemSolving() {
+    console.log('\n🧩 Example 1: Collaborative Problem-Solving\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+        cacheStrategy: 'memory',
+    });
+    // Generate problem-solving sessions
+    const sessions = await synth.generateStructured({
+        count: 30,
+        schema: {
+            session_id: 'UUID',
+            problem: {
+                id: 'UUID',
+                title: 'complex problem title',
+                description: 'detailed problem description',
+                domain: 'software_architecture | data_analysis | optimization | debugging | design',
+                complexity: 'number (1-10)',
+                estimated_time_hours: 'number (1-48)',
+            },
+            participating_agents: [
+                {
+                    agent_id: 'agent-{1-20}',
+                    role: 'researcher | analyst | implementer | reviewer | facilitator',
+                    expertise: ['array of 2-4 expertise areas'],
+                    contribution_count: 'number (0-50)',
+                    quality_score: 'number (0-100)',
+                },
+            ],
+            solution_proposals: [
+                {
+                    proposal_id: 'UUID',
+                    proposer_agent_id: 'agent id from participants',
+                    approach: 'detailed solution approach',
+                    estimated_effort: 'number (1-40 hours)',
+                    pros: ['array of 2-4 advantages'],
+                    cons: ['array of 1-3 disadvantages'],
+                    votes_for: 'number (0-20)',
+                    votes_against: 'number (0-20)',
+                    feasibility_score: 'number (0-100)',
+                },
+            ],
+            collaboration_events: [
+                {
+                    event_id: 'UUID',
+                    event_type: 'proposal | critique | enhancement | agreement | disagreement',
+                    agent_id: 'agent id',
+                    content: 'event description',
+                    timestamp: 'ISO timestamp',
+                    references: ['array of related event_ids or empty'],
+                },
+            ],
+            selected_solution_id: 'UUID (from proposals)',
+            outcome: 'successful | partial | failed | ongoing',
+            actual_time_hours: 'number',
+            quality_metrics: {
+                solution_quality: 'number (0-100)',
+                collaboration_efficiency: 'number (0-100)',
+                innovation_score: 'number (0-100)',
+                consensus_level: 'number (0-100)',
+            },
+            started_at: 'ISO timestamp',
+            completed_at: 'ISO timestamp or null',
+        },
+        constraints: [
+            'Sessions should have 3-8 participating agents',
+            'Should have 2-5 solution proposals per session',
+            '70% of sessions should be successful',
+            'Higher agent expertise should correlate with better outcomes',
+        ],
+    });
+    // Analyze collaborative sessions
+    const successfulSessions = sessions.data.filter((s) => s.outcome === 'successful');
+    const avgParticipants = sessions.data.reduce((sum, s) => sum + s.participating_agents.length, 0) / sessions.data.length;
+    console.log('Collaborative Problem-Solving Analysis:');
+    console.log(`- Total sessions: ${sessions.data.length}`);
+    console.log(`- Successful: ${successfulSessions.length} (${((successfulSessions.length / sessions.data.length) * 100).toFixed(1)}%)`);
+    console.log(`- Average participants: ${avgParticipants.toFixed(1)}`);
+    // Calculate quality metrics
+    const avgQuality = successfulSessions.reduce((sum, s) => sum + s.quality_metrics.solution_quality, 0) / successfulSessions.length;
+    const avgInnovation = successfulSessions.reduce((sum, s) => sum + s.quality_metrics.innovation_score, 0) / successfulSessions.length;
+    console.log(`\nQuality Metrics:`);
+    console.log(`- Average solution quality: ${avgQuality.toFixed(1)}/100`);
+    console.log(`- Average innovation score: ${avgInnovation.toFixed(1)}/100`);
+    // Domain distribution
+    const domains = new Map();
+    sessions.data.forEach((s) => {
+        domains.set(s.problem.domain, (domains.get(s.problem.domain) || 0) + 1);
+    });
+    console.log('\nProblem Domain Distribution:');
+    domains.forEach((count, domain) => {
+        console.log(`- ${domain}: ${count}`);
+    });
+    // Claude-Flow integration
+    console.log('\nClaude-Flow Neural Integration:');
+    console.log('npx claude-flow@alpha hooks neural-train --pattern "collaboration"');
+    console.log('// Store successful patterns in AgenticDB for learning');
+    return sessions;
+}
+// ============================================================================
+// Example 2: Knowledge Sharing Patterns
+// ============================================================================
+/**
+ * Generate knowledge sharing and transfer data
+ */
+async function knowledgeSharingPatterns() {
+    console.log('\n📚 Example 2: Knowledge Sharing Patterns\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate knowledge base entries
+    const knowledgeBase = await synth.generateStructured({
+        count: 200,
+        schema: {
+            entry_id: 'UUID',
+            category: 'best_practice | lesson_learned | solution_pattern | troubleshooting | architecture',
+            title: 'descriptive title',
+            content: 'detailed knowledge content (2-4 paragraphs)',
+            tags: ['array of 3-6 tags'],
+            author_agent_id: 'agent-{1-50}',
+            contributors: ['array of 0-5 agent ids'],
+            related_entries: ['array of 0-3 entry_ids or empty'],
+            quality_rating: 'number (0-5.0)',
+            usefulness_count: 'number (0-100)',
+            view_count: 'number (0-1000)',
+            created_at: 'ISO timestamp',
+            updated_at: 'ISO timestamp',
+        },
+    });
+    // Generate knowledge transfer events
+    const transferEvents = await synth.generateEvents({
+        count: 500,
+        eventTypes: [
+            'knowledge_created',
+            'knowledge_shared',
+            'knowledge_applied',
+            'knowledge_validated',
+            'knowledge_updated',
+        ],
+        schema: {
+            event_id: 'UUID',
+            event_type: 'one of eventTypes',
+            knowledge_entry_id: 'UUID (from knowledgeBase)',
+            source_agent_id: 'agent-{1-50}',
+            target_agent_id: 'agent-{1-50} or null',
+            context: 'description of usage context',
+            effectiveness: 'number (0-100)',
+            timestamp: 'ISO timestamp',
+        },
+        distribution: 'uniform',
+    });
+    // Generate agent knowledge profiles
+    const agentProfiles = await synth.generateStructured({
+        count: 50,
+        schema: {
+            agent_id: 'agent-{1-50}',
+            expertise_areas: ['array of 3-8 expertise domains'],
+            knowledge_contributed: 'number (0-20)',
+            knowledge_consumed: 'number (0-100)',
+            sharing_frequency: 'high | medium | low',
+            learning_rate: 'number (0-1.0)',
+            collaboration_score: 'number (0-100)',
+            influence_score: 'number (0-100)',
+        },
+    });
+    console.log('Knowledge Sharing Analysis:');
+    console.log(`- Knowledge entries: ${knowledgeBase.data.length}`);
+    console.log(`- Transfer events: ${transferEvents.data.length}`);
+    console.log(`- Agent profiles: ${agentProfiles.data.length}`);
+    // Analyze knowledge distribution
+    const categoryCount = new Map();
+    knowledgeBase.data.forEach((entry) => {
+        categoryCount.set(entry.category, (categoryCount.get(entry.category) || 0) + 1);
+    });
+    console.log('\nKnowledge Categories:');
+    categoryCount.forEach((count, category) => {
+        console.log(`- ${category}: ${count}`);
+    });
+    // Calculate sharing metrics
+    const avgRating = knowledgeBase.data.reduce((sum, entry) => sum + entry.quality_rating, 0) / knowledgeBase.data.length;
+    console.log(`\nSharing Metrics:`);
+    console.log(`- Average quality rating: ${avgRating.toFixed(2)}/5.0`);
+    console.log(`- High sharers: ${agentProfiles.data.filter((a) => a.sharing_frequency === 'high').length}`);
+    // AgenticDB integration
+    console.log('\nAgenticDB Integration:');
+    console.log('// Store knowledge embeddings for semantic search');
+    console.log('await agenticDB.storeVector({ text: knowledge.content, metadata: {...} });');
+    console.log('// Query similar knowledge: agenticDB.search({ query, topK: 10 });');
+    return { knowledgeBase, transferEvents, agentProfiles };
+}
+// ============================================================================
+// Example 3: Emergent Behavior Simulation
+// ============================================================================
+/**
+ * Generate emergent behavior patterns in swarm systems
+ */
+async function emergentBehaviorSimulation() {
+    console.log('\n🌀 Example 3: Emergent Behavior Simulation\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate swarm state evolution
+    const swarmStates = await synth.generateTimeSeries({
+        count: 100,
+        interval: '1m',
+        metrics: [
+            'agent_count',
+            'cluster_count',
+            'avg_cluster_size',
+            'coordination_level',
+            'task_completion_rate',
+            'communication_density',
+            'adaptation_score',
+        ],
+        trend: 'up',
+        seasonality: false,
+    });
+    // Generate agent interactions
+    const interactions = await synth.generateStructured({
+        count: 1000,
+        schema: {
+            interaction_id: 'UUID',
+            agent_a_id: 'agent-{1-100}',
+            agent_b_id: 'agent-{1-100}',
+            interaction_type: 'cooperation | competition | information_exchange | resource_sharing | conflict_resolution',
+            context: 'brief description of interaction',
+            outcome: 'positive | negative | neutral',
+            influence_on_behavior: 'number (-1.0 to 1.0)',
+            timestamp: 'ISO timestamp',
+        },
+        constraints: [
+            'agent_a_id should be different from agent_b_id',
+            '70% of interactions should be positive',
+            'Cooperation should be more common than competition',
+        ],
+    });
+    // Generate emergent patterns
+    const emergentPatterns = await synth.generateStructured({
+        count: 20,
+        schema: {
+            pattern_id: 'UUID',
+            pattern_type: 'clustering | leader_emergence | task_specialization | self_organization | collective_decision',
+            description: 'detailed pattern description',
+            participants: ['array of 5-30 agent ids'],
+            emergence_time: 'ISO timestamp',
+            stability_score: 'number (0-100)',
+            efficiency_gain: 'number (0-50 percent)',
+            conditions: ['array of 2-4 conditions that led to emergence'],
+            observed_behaviors: ['array of 3-6 behaviors'],
+        },
+    });
+    // Generate agent behavior evolution
+    const behaviorEvolution = await synth.generateStructured({
+        count: 300,
+        schema: {
+            agent_id: 'agent-{1-100}',
+            timestamp: 'ISO timestamp',
+            behavior_traits: {
+                cooperation_tendency: 'number (0-1.0)',
+                exploration_vs_exploitation: 'number (0-1.0)',
+                risk_tolerance: 'number (0-1.0)',
+                social_connectivity: 'number (0-1.0)',
+                task_focus: 'generalist | specialist',
+            },
+            influenced_by: ['array of 0-3 agent ids or empty'],
+            role_in_swarm: 'leader | follower | bridge | isolate | specialist',
+            performance_score: 'number (0-100)',
+        },
+    });
+    console.log('Emergent Behavior Analysis:');
+    console.log(`- Swarm state snapshots: ${swarmStates.data.length}`);
+    console.log(`- Agent interactions: ${interactions.data.length}`);
+    console.log(`- Emergent patterns: ${emergentPatterns.data.length}`);
+    console.log(`- Behavior evolution points: ${behaviorEvolution.data.length}`);
+    // Analyze interaction types
+    const interactionTypes = new Map();
+    interactions.data.forEach((i) => {
+        interactionTypes.set(i.interaction_type, (interactionTypes.get(i.interaction_type) || 0) + 1);
+    });
+    console.log('\nInteraction Distribution:');
+    interactionTypes.forEach((count, type) => {
+        console.log(`- ${type}: ${count} (${((count / interactions.data.length) * 100).toFixed(1)}%)`);
+    });
+    // Pattern analysis
+    console.log('\nEmergent Patterns:');
+    emergentPatterns.data.forEach((pattern) => {
+        console.log(`- ${pattern.pattern_type}: ${pattern.participants.length} participants, stability ${pattern.stability_score}/100`);
+    });
+    // Ruv-Swarm collective intelligence
+    console.log('\nRuv-Swarm Collective Intelligence:');
+    console.log('npx ruv-swarm mcp start');
+    console.log('// MCP: neural_patterns to analyze emergent behaviors');
+    return { swarmStates, interactions, emergentPatterns, behaviorEvolution };
+}
+// ============================================================================
+// Example 4: Voting and Consensus Data
+// ============================================================================
+/**
+ * Generate voting and consensus mechanism data
+ */
+async function votingAndConsensusData() {
+    console.log('\n🗳️  Example 4: Voting and Consensus Mechanisms\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate voting sessions
+    const votingSessions = await synth.generateStructured({
+        count: 50,
+        schema: {
+            session_id: 'UUID',
+            voting_method: 'simple_majority | qualified_majority | unanimous | weighted | ranked_choice',
+            topic: {
+                id: 'UUID',
+                title: 'decision topic',
+                description: 'topic description',
+                importance: 'critical | high | medium | low',
+            },
+            eligible_voters: ['array of 10-50 agent ids'],
+            votes: [
+                {
+                    voter_id: 'agent id from eligible_voters',
+                    vote_value: 'for | against | abstain',
+                    weight: 'number (1.0-5.0)',
+                    reasoning: 'brief explanation',
+                    confidence: 'number (0-100)',
+                    cast_at: 'ISO timestamp',
+                },
+            ],
+            result: {
+                decision: 'accepted | rejected | tie',
+                votes_for: 'number',
+                votes_against: 'number',
+                votes_abstain: 'number',
+                weighted_score: 'number',
+                participation_rate: 'number (0-100)',
+                consensus_level: 'number (0-100)',
+            },
+            duration_minutes: 'number (5-180)',
+            started_at: 'ISO timestamp',
+            completed_at: 'ISO timestamp',
+        },
+        constraints: [
+            'Votes array should match eligible_voters count',
+            'Critical topics should have higher participation',
+            'Weighted votes should affect weighted_score',
+        ],
+    });
+    // Generate consensus mechanisms
+    const consensusMechanisms = await synth.generateStructured({
+        count: 100,
+        schema: {
+            mechanism_id: 'UUID',
+            mechanism_type: 'deliberative | aggregative | iterative | delegative',
+            session_id: 'UUID (from votingSessions)',
+            rounds: [
+                {
+                    round_number: 'number (1-5)',
+                    proposals: ['array of 2-5 proposal descriptions'],
+                    discussions: 'number (10-100)',
+                    opinion_shifts: 'number (0-20)',
+                    convergence_score: 'number (0-100)',
+                    duration_minutes: 'number (5-60)',
+                },
+            ],
+            final_consensus: 'strong | moderate | weak | none',
+            compromises_made: 'number (0-5)',
+            dissenting_opinions: ['array of 0-3 dissenting viewpoints or empty'],
+        },
+    });
+    // Generate agent voting behavior
+    const votingBehavior = await synth.generateStructured({
+        count: 200,
+        schema: {
+            agent_id: 'agent-{1-50}',
+            total_votes: 'number (0-50)',
+            voting_pattern: 'consistent | moderate | swing',
+            influence_level: 'high | medium | low',
+            consensus_seeking: 'number (0-100)',
+            independence_score: 'number (0-100)',
+            expertise_alignment: 'number (0-100)',
+        },
+    });
+    console.log('Voting and Consensus Analysis:');
+    console.log(`- Voting sessions: ${votingSessions.data.length}`);
+    console.log(`- Consensus mechanisms: ${consensusMechanisms.data.length}`);
+    console.log(`- Agent behaviors: ${votingBehavior.data.length}`);
+    // Analyze voting outcomes
+    const acceptedCount = votingSessions.data.filter((s) => s.result.decision === 'accepted').length;
+    const avgParticipation = votingSessions.data.reduce((sum, s) => sum + s.result.participation_rate, 0) / votingSessions.data.length;
+    console.log(`\nVoting Outcomes:`);
+    console.log(`- Accepted: ${acceptedCount} (${((acceptedCount / votingSessions.data.length) * 100).toFixed(1)}%)`);
+    console.log(`- Average participation: ${avgParticipation.toFixed(1)}%`);
+    // Consensus strength
+    const strongConsensus = consensusMechanisms.data.filter((m) => m.final_consensus === 'strong').length;
+    console.log(`\nConsensus Quality:`);
+    console.log(`- Strong consensus: ${strongConsensus} (${((strongConsensus / consensusMechanisms.data.length) * 100).toFixed(1)}%)`);
+    return { votingSessions, consensusMechanisms, votingBehavior };
+}
+// ============================================================================
+// Example 5: Reputation Systems
+// ============================================================================
+/**
+ * Generate reputation and trust system data
+ */
+async function reputationSystems() {
+    console.log('\n⭐ Example 5: Reputation and Trust Systems\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate agent reputation profiles
+    const reputationProfiles = await synth.generateStructured({
+        count: 100,
+        schema: {
+            agent_id: 'agent-{1-100}',
+            overall_reputation: 'number (0-100)',
+            reputation_components: {
+                reliability: 'number (0-100)',
+                expertise: 'number (0-100)',
+                collaboration: 'number (0-100)',
+                responsiveness: 'number (0-100)',
+                quality: 'number (0-100)',
+            },
+            trust_score: 'number (0-100)',
+            endorsements: 'number (0-50)',
+            negative_feedback: 'number (0-20)',
+            tasks_completed: 'number (0-1000)',
+            success_rate: 'number (0-100)',
+            tenure_days: 'number (1-730)',
+            reputation_trend: 'rising | stable | declining',
+            badges: ['array of 0-5 achievement badges or empty'],
+        },
+        constraints: [
+            'Overall reputation should correlate with component scores',
+            'Higher success rate should correlate with higher reputation',
+            'Trust score should factor in tenure and feedback',
+        ],
+    });
+    // Generate reputation events
+    const reputationEvents = await synth.generateEvents({
+        count: 500,
+        eventTypes: [
+            'endorsement_received',
+            'feedback_positive',
+            'feedback_negative',
+            'task_success',
+            'task_failure',
+            'badge_earned',
+            'collaboration_rated',
+        ],
+        schema: {
+            event_id: 'UUID',
+            event_type: 'one of eventTypes',
+            subject_agent_id: 'agent-{1-100}',
+            evaluator_agent_id: 'agent-{1-100} or null',
+            impact: 'number (-10 to +10)',
+            context: 'brief description',
+            evidence: 'supporting evidence description or null',
+            timestamp: 'ISO timestamp',
+        },
+        distribution: 'poisson',
+    });
+    // Generate trust relationships
+    const trustRelationships = await synth.generateStructured({
+        count: 300,
+        schema: {
+            relationship_id: 'UUID',
+            trustor_agent_id: 'agent-{1-100}',
+            trustee_agent_id: 'agent-{1-100}',
+            trust_level: 'number (0-100)',
+            relationship_type: 'direct | transitive | institutional',
+            interaction_count: 'number (1-100)',
+            successful_interactions: 'number (proportional to interaction_count)',
+            last_interaction: 'ISO timestamp',
+            trust_evolution: 'building | established | declining',
+        },
+        constraints: [
+            'trustor_agent_id should be different from trustee_agent_id',
+            'Trust level should correlate with success rate',
+            'More interactions should increase trust stability',
+        ],
+    });
+    // Generate reputation decay and recovery
+    const reputationChanges = await synth.generateTimeSeries({
+        count: 200,
+        interval: '1d',
+        metrics: [
+            'avg_reputation',
+            'reputation_variance',
+            'positive_events',
+            'negative_events',
+            'trust_network_density',
+            'endorsement_rate',
+        ],
+        trend: 'stable',
+    });
+    console.log('Reputation System Analysis:');
+    console.log(`- Agent profiles: ${reputationProfiles.data.length}`);
+    console.log(`- Reputation events: ${reputationEvents.data.length}`);
+    console.log(`- Trust relationships: ${trustRelationships.data.length}`);
+    console.log(`- Time series points: ${reputationChanges.data.length}`);
+    // Analyze reputation distribution
+    const highReputation = reputationProfiles.data.filter((p) => p.overall_reputation >= 80).length;
+    const lowReputation = reputationProfiles.data.filter((p) => p.overall_reputation < 40).length;
+    console.log(`\nReputation Distribution:`);
+    console.log(`- High reputation (≥80): ${highReputation} (${((highReputation / reputationProfiles.data.length) * 100).toFixed(1)}%)`);
+    console.log(`- Low reputation (<40): ${lowReputation} (${((lowReputation / reputationProfiles.data.length) * 100).toFixed(1)}%)`);
+    // Trust network analysis
+    const avgTrustLevel = trustRelationships.data.reduce((sum, r) => sum + r.trust_level, 0) / trustRelationships.data.length;
+    console.log(`\nTrust Network:`);
+    console.log(`- Average trust level: ${avgTrustLevel.toFixed(1)}/100`);
+    console.log(`- Established relationships: ${trustRelationships.data.filter((r) => r.trust_evolution === 'established').length}`);
+    // Integration with reputation tracking
+    console.log('\nReputation Tracking Integration:');
+    console.log('// Store reputation events in AgenticDB');
+    console.log('// Use claude-flow hooks to update reputation after tasks');
+    console.log('npx claude-flow@alpha hooks post-task --update-reputation true');
+    return { reputationProfiles, reputationEvents, trustRelationships, reputationChanges };
+}
+// ============================================================================
+// Run All Examples
+// ============================================================================
+async function runAllCollectiveIntelligenceExamples() {
+    console.log('🚀 Running All Collective Intelligence Examples\n');
+    console.log('='.repeat(70));
+    try {
+        await collaborativeProblemSolving();
+        console.log('='.repeat(70));
+        await knowledgeSharingPatterns();
+        console.log('='.repeat(70));
+        await emergentBehaviorSimulation();
+        console.log('='.repeat(70));
+        await votingAndConsensusData();
+        console.log('='.repeat(70));
+        await reputationSystems();
+        console.log('='.repeat(70));
+        console.log('\n✅ All collective intelligence examples completed!\n');
+    }
+    catch (error) {
+        console.error('❌ Error running examples:', error.message);
+        throw error;
+    }
+}
+// Run if executed directly
+if (import.meta.url === `file://${process.argv[1]}`) {
+    runAllCollectiveIntelligenceExamples().catch(console.error);
+}
+//# sourceMappingURL=collective-intelligence.js.map

vendor/ruvector/npm/packages/agentic-synth/examples/swarms/collective-intelligence.ts

@@ -0,0 +1,669 @@
+/**
+ * Collective Intelligence Examples
+ *
+ * Demonstrates swarm intelligence patterns including collaborative
+ * problem-solving, knowledge sharing, emergent behavior simulation,
+ * voting and consensus mechanisms, and reputation systems.
+ *
+ * Integrates with:
+ * - claude-flow: Neural pattern recognition and learning
+ * - ruv-swarm: Collective intelligence coordination
+ * - AgenticDB: Distributed knowledge storage
+ */
+
+import { AgenticSynth, createSynth } from '../../dist/index.js';
+import type { GenerationResult } from '../../src/types.js';
+
+// ============================================================================
+// Example 1: Collaborative Problem-Solving
+// ============================================================================
+
+/**
+ * Generate collaborative problem-solving session data
+ */
+export async function collaborativeProblemSolving() {
+  console.log('\n🧩 Example 1: Collaborative Problem-Solving\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    cacheStrategy: 'memory',
+  });
+
+  // Generate problem-solving sessions
+  const sessions = await synth.generateStructured({
+    count: 30,
+    schema: {
+      session_id: 'UUID',
+      problem: {
+        id: 'UUID',
+        title: 'complex problem title',
+        description: 'detailed problem description',
+        domain: 'software_architecture | data_analysis | optimization | debugging | design',
+        complexity: 'number (1-10)',
+        estimated_time_hours: 'number (1-48)',
+      },
+      participating_agents: [
+        {
+          agent_id: 'agent-{1-20}',
+          role: 'researcher | analyst | implementer | reviewer | facilitator',
+          expertise: ['array of 2-4 expertise areas'],
+          contribution_count: 'number (0-50)',
+          quality_score: 'number (0-100)',
+        },
+      ],
+      solution_proposals: [
+        {
+          proposal_id: 'UUID',
+          proposer_agent_id: 'agent id from participants',
+          approach: 'detailed solution approach',
+          estimated_effort: 'number (1-40 hours)',
+          pros: ['array of 2-4 advantages'],
+          cons: ['array of 1-3 disadvantages'],
+          votes_for: 'number (0-20)',
+          votes_against: 'number (0-20)',
+          feasibility_score: 'number (0-100)',
+        },
+      ],
+      collaboration_events: [
+        {
+          event_id: 'UUID',
+          event_type: 'proposal | critique | enhancement | agreement | disagreement',
+          agent_id: 'agent id',
+          content: 'event description',
+          timestamp: 'ISO timestamp',
+          references: ['array of related event_ids or empty'],
+        },
+      ],
+      selected_solution_id: 'UUID (from proposals)',
+      outcome: 'successful | partial | failed | ongoing',
+      actual_time_hours: 'number',
+      quality_metrics: {
+        solution_quality: 'number (0-100)',
+        collaboration_efficiency: 'number (0-100)',
+        innovation_score: 'number (0-100)',
+        consensus_level: 'number (0-100)',
+      },
+      started_at: 'ISO timestamp',
+      completed_at: 'ISO timestamp or null',
+    },
+    constraints: [
+      'Sessions should have 3-8 participating agents',
+      'Should have 2-5 solution proposals per session',
+      '70% of sessions should be successful',
+      'Higher agent expertise should correlate with better outcomes',
+    ],
+  });
+
+  // Analyze collaborative sessions
+  const successfulSessions = sessions.data.filter((s: any) => s.outcome === 'successful');
+  const avgParticipants = sessions.data.reduce(
+    (sum: number, s: any) => sum + s.participating_agents.length,
+    0
+  ) / sessions.data.length;
+
+  console.log('Collaborative Problem-Solving Analysis:');
+  console.log(`- Total sessions: ${sessions.data.length}`);
+  console.log(`- Successful: ${successfulSessions.length} (${((successfulSessions.length / sessions.data.length) * 100).toFixed(1)}%)`);
+  console.log(`- Average participants: ${avgParticipants.toFixed(1)}`);
+
+  // Calculate quality metrics
+  const avgQuality = successfulSessions.reduce(
+    (sum: number, s: any) => sum + s.quality_metrics.solution_quality,
+    0
+  ) / successfulSessions.length;
+  const avgInnovation = successfulSessions.reduce(
+    (sum: number, s: any) => sum + s.quality_metrics.innovation_score,
+    0
+  ) / successfulSessions.length;
+
+  console.log(`\nQuality Metrics:`);
+  console.log(`- Average solution quality: ${avgQuality.toFixed(1)}/100`);
+  console.log(`- Average innovation score: ${avgInnovation.toFixed(1)}/100`);
+
+  // Domain distribution
+  const domains = new Map<string, number>();
+  sessions.data.forEach((s: any) => {
+    domains.set(s.problem.domain, (domains.get(s.problem.domain) || 0) + 1);
+  });
+
+  console.log('\nProblem Domain Distribution:');
+  domains.forEach((count, domain) => {
+    console.log(`- ${domain}: ${count}`);
+  });
+
+  // Claude-Flow integration
+  console.log('\nClaude-Flow Neural Integration:');
+  console.log('npx claude-flow@alpha hooks neural-train --pattern "collaboration"');
+  console.log('// Store successful patterns in AgenticDB for learning');
+
+  return sessions;
+}
+
+// ============================================================================
+// Example 2: Knowledge Sharing Patterns
+// ============================================================================
+
+/**
+ * Generate knowledge sharing and transfer data
+ */
+export async function knowledgeSharingPatterns() {
+  console.log('\n📚 Example 2: Knowledge Sharing Patterns\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate knowledge base entries
+  const knowledgeBase = await synth.generateStructured({
+    count: 200,
+    schema: {
+      entry_id: 'UUID',
+      category: 'best_practice | lesson_learned | solution_pattern | troubleshooting | architecture',
+      title: 'descriptive title',
+      content: 'detailed knowledge content (2-4 paragraphs)',
+      tags: ['array of 3-6 tags'],
+      author_agent_id: 'agent-{1-50}',
+      contributors: ['array of 0-5 agent ids'],
+      related_entries: ['array of 0-3 entry_ids or empty'],
+      quality_rating: 'number (0-5.0)',
+      usefulness_count: 'number (0-100)',
+      view_count: 'number (0-1000)',
+      created_at: 'ISO timestamp',
+      updated_at: 'ISO timestamp',
+    },
+  });
+
+  // Generate knowledge transfer events
+  const transferEvents = await synth.generateEvents({
+    count: 500,
+    eventTypes: [
+      'knowledge_created',
+      'knowledge_shared',
+      'knowledge_applied',
+      'knowledge_validated',
+      'knowledge_updated',
+    ],
+    schema: {
+      event_id: 'UUID',
+      event_type: 'one of eventTypes',
+      knowledge_entry_id: 'UUID (from knowledgeBase)',
+      source_agent_id: 'agent-{1-50}',
+      target_agent_id: 'agent-{1-50} or null',
+      context: 'description of usage context',
+      effectiveness: 'number (0-100)',
+      timestamp: 'ISO timestamp',
+    },
+    distribution: 'uniform',
+  });
+
+  // Generate agent knowledge profiles
+  const agentProfiles = await synth.generateStructured({
+    count: 50,
+    schema: {
+      agent_id: 'agent-{1-50}',
+      expertise_areas: ['array of 3-8 expertise domains'],
+      knowledge_contributed: 'number (0-20)',
+      knowledge_consumed: 'number (0-100)',
+      sharing_frequency: 'high | medium | low',
+      learning_rate: 'number (0-1.0)',
+      collaboration_score: 'number (0-100)',
+      influence_score: 'number (0-100)',
+    },
+  });
+
+  console.log('Knowledge Sharing Analysis:');
+  console.log(`- Knowledge entries: ${knowledgeBase.data.length}`);
+  console.log(`- Transfer events: ${transferEvents.data.length}`);
+  console.log(`- Agent profiles: ${agentProfiles.data.length}`);
+
+  // Analyze knowledge distribution
+  const categoryCount = new Map<string, number>();
+  knowledgeBase.data.forEach((entry: any) => {
+    categoryCount.set(entry.category, (categoryCount.get(entry.category) || 0) + 1);
+  });
+
+  console.log('\nKnowledge Categories:');
+  categoryCount.forEach((count, category) => {
+    console.log(`- ${category}: ${count}`);
+  });
+
+  // Calculate sharing metrics
+  const avgRating = knowledgeBase.data.reduce(
+    (sum: number, entry: any) => sum + entry.quality_rating,
+    0
+  ) / knowledgeBase.data.length;
+
+  console.log(`\nSharing Metrics:`);
+  console.log(`- Average quality rating: ${avgRating.toFixed(2)}/5.0`);
+  console.log(`- High sharers: ${agentProfiles.data.filter((a: any) => a.sharing_frequency === 'high').length}`);
+
+  // AgenticDB integration
+  console.log('\nAgenticDB Integration:');
+  console.log('// Store knowledge embeddings for semantic search');
+  console.log('await agenticDB.storeVector({ text: knowledge.content, metadata: {...} });');
+  console.log('// Query similar knowledge: agenticDB.search({ query, topK: 10 });');
+
+  return { knowledgeBase, transferEvents, agentProfiles };
+}
+
+// ============================================================================
+// Example 3: Emergent Behavior Simulation
+// ============================================================================
+
+/**
+ * Generate emergent behavior patterns in swarm systems
+ */
+export async function emergentBehaviorSimulation() {
+  console.log('\n🌀 Example 3: Emergent Behavior Simulation\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate swarm state evolution
+  const swarmStates = await synth.generateTimeSeries({
+    count: 100,
+    interval: '1m',
+    metrics: [
+      'agent_count',
+      'cluster_count',
+      'avg_cluster_size',
+      'coordination_level',
+      'task_completion_rate',
+      'communication_density',
+      'adaptation_score',
+    ],
+    trend: 'up',
+    seasonality: false,
+  });
+
+  // Generate agent interactions
+  const interactions = await synth.generateStructured({
+    count: 1000,
+    schema: {
+      interaction_id: 'UUID',
+      agent_a_id: 'agent-{1-100}',
+      agent_b_id: 'agent-{1-100}',
+      interaction_type: 'cooperation | competition | information_exchange | resource_sharing | conflict_resolution',
+      context: 'brief description of interaction',
+      outcome: 'positive | negative | neutral',
+      influence_on_behavior: 'number (-1.0 to 1.0)',
+      timestamp: 'ISO timestamp',
+    },
+    constraints: [
+      'agent_a_id should be different from agent_b_id',
+      '70% of interactions should be positive',
+      'Cooperation should be more common than competition',
+    ],
+  });
+
+  // Generate emergent patterns
+  const emergentPatterns = await synth.generateStructured({
+    count: 20,
+    schema: {
+      pattern_id: 'UUID',
+      pattern_type: 'clustering | leader_emergence | task_specialization | self_organization | collective_decision',
+      description: 'detailed pattern description',
+      participants: ['array of 5-30 agent ids'],
+      emergence_time: 'ISO timestamp',
+      stability_score: 'number (0-100)',
+      efficiency_gain: 'number (0-50 percent)',
+      conditions: ['array of 2-4 conditions that led to emergence'],
+      observed_behaviors: ['array of 3-6 behaviors'],
+    },
+  });
+
+  // Generate agent behavior evolution
+  const behaviorEvolution = await synth.generateStructured({
+    count: 300,
+    schema: {
+      agent_id: 'agent-{1-100}',
+      timestamp: 'ISO timestamp',
+      behavior_traits: {
+        cooperation_tendency: 'number (0-1.0)',
+        exploration_vs_exploitation: 'number (0-1.0)',
+        risk_tolerance: 'number (0-1.0)',
+        social_connectivity: 'number (0-1.0)',
+        task_focus: 'generalist | specialist',
+      },
+      influenced_by: ['array of 0-3 agent ids or empty'],
+      role_in_swarm: 'leader | follower | bridge | isolate | specialist',
+      performance_score: 'number (0-100)',
+    },
+  });
+
+  console.log('Emergent Behavior Analysis:');
+  console.log(`- Swarm state snapshots: ${swarmStates.data.length}`);
+  console.log(`- Agent interactions: ${interactions.data.length}`);
+  console.log(`- Emergent patterns: ${emergentPatterns.data.length}`);
+  console.log(`- Behavior evolution points: ${behaviorEvolution.data.length}`);
+
+  // Analyze interaction types
+  const interactionTypes = new Map<string, number>();
+  interactions.data.forEach((i: any) => {
+    interactionTypes.set(i.interaction_type, (interactionTypes.get(i.interaction_type) || 0) + 1);
+  });
+
+  console.log('\nInteraction Distribution:');
+  interactionTypes.forEach((count, type) => {
+    console.log(`- ${type}: ${count} (${((count / interactions.data.length) * 100).toFixed(1)}%)`);
+  });
+
+  // Pattern analysis
+  console.log('\nEmergent Patterns:');
+  emergentPatterns.data.forEach((pattern: any) => {
+    console.log(`- ${pattern.pattern_type}: ${pattern.participants.length} participants, stability ${pattern.stability_score}/100`);
+  });
+
+  // Ruv-Swarm collective intelligence
+  console.log('\nRuv-Swarm Collective Intelligence:');
+  console.log('npx ruv-swarm mcp start');
+  console.log('// MCP: neural_patterns to analyze emergent behaviors');
+
+  return { swarmStates, interactions, emergentPatterns, behaviorEvolution };
+}
+
+// ============================================================================
+// Example 4: Voting and Consensus Data
+// ============================================================================
+
+/**
+ * Generate voting and consensus mechanism data
+ */
+export async function votingAndConsensusData() {
+  console.log('\n🗳️  Example 4: Voting and Consensus Mechanisms\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate voting sessions
+  const votingSessions = await synth.generateStructured({
+    count: 50,
+    schema: {
+      session_id: 'UUID',
+      voting_method: 'simple_majority | qualified_majority | unanimous | weighted | ranked_choice',
+      topic: {
+        id: 'UUID',
+        title: 'decision topic',
+        description: 'topic description',
+        importance: 'critical | high | medium | low',
+      },
+      eligible_voters: ['array of 10-50 agent ids'],
+      votes: [
+        {
+          voter_id: 'agent id from eligible_voters',
+          vote_value: 'for | against | abstain',
+          weight: 'number (1.0-5.0)',
+          reasoning: 'brief explanation',
+          confidence: 'number (0-100)',
+          cast_at: 'ISO timestamp',
+        },
+      ],
+      result: {
+        decision: 'accepted | rejected | tie',
+        votes_for: 'number',
+        votes_against: 'number',
+        votes_abstain: 'number',
+        weighted_score: 'number',
+        participation_rate: 'number (0-100)',
+        consensus_level: 'number (0-100)',
+      },
+      duration_minutes: 'number (5-180)',
+      started_at: 'ISO timestamp',
+      completed_at: 'ISO timestamp',
+    },
+    constraints: [
+      'Votes array should match eligible_voters count',
+      'Critical topics should have higher participation',
+      'Weighted votes should affect weighted_score',
+    ],
+  });
+
+  // Generate consensus mechanisms
+  const consensusMechanisms = await synth.generateStructured({
+    count: 100,
+    schema: {
+      mechanism_id: 'UUID',
+      mechanism_type: 'deliberative | aggregative | iterative | delegative',
+      session_id: 'UUID (from votingSessions)',
+      rounds: [
+        {
+          round_number: 'number (1-5)',
+          proposals: ['array of 2-5 proposal descriptions'],
+          discussions: 'number (10-100)',
+          opinion_shifts: 'number (0-20)',
+          convergence_score: 'number (0-100)',
+          duration_minutes: 'number (5-60)',
+        },
+      ],
+      final_consensus: 'strong | moderate | weak | none',
+      compromises_made: 'number (0-5)',
+      dissenting_opinions: ['array of 0-3 dissenting viewpoints or empty'],
+    },
+  });
+
+  // Generate agent voting behavior
+  const votingBehavior = await synth.generateStructured({
+    count: 200,
+    schema: {
+      agent_id: 'agent-{1-50}',
+      total_votes: 'number (0-50)',
+      voting_pattern: 'consistent | moderate | swing',
+      influence_level: 'high | medium | low',
+      consensus_seeking: 'number (0-100)',
+      independence_score: 'number (0-100)',
+      expertise_alignment: 'number (0-100)',
+    },
+  });
+
+  console.log('Voting and Consensus Analysis:');
+  console.log(`- Voting sessions: ${votingSessions.data.length}`);
+  console.log(`- Consensus mechanisms: ${consensusMechanisms.data.length}`);
+  console.log(`- Agent behaviors: ${votingBehavior.data.length}`);
+
+  // Analyze voting outcomes
+  const acceptedCount = votingSessions.data.filter(
+    (s: any) => s.result.decision === 'accepted'
+  ).length;
+  const avgParticipation = votingSessions.data.reduce(
+    (sum: number, s: any) => sum + s.result.participation_rate,
+    0
+  ) / votingSessions.data.length;
+
+  console.log(`\nVoting Outcomes:`);
+  console.log(`- Accepted: ${acceptedCount} (${((acceptedCount / votingSessions.data.length) * 100).toFixed(1)}%)`);
+  console.log(`- Average participation: ${avgParticipation.toFixed(1)}%`);
+
+  // Consensus strength
+  const strongConsensus = consensusMechanisms.data.filter(
+    (m: any) => m.final_consensus === 'strong'
+  ).length;
+
+  console.log(`\nConsensus Quality:`);
+  console.log(`- Strong consensus: ${strongConsensus} (${((strongConsensus / consensusMechanisms.data.length) * 100).toFixed(1)}%)`);
+
+  return { votingSessions, consensusMechanisms, votingBehavior };
+}
+
+// ============================================================================
+// Example 5: Reputation Systems
+// ============================================================================
+
+/**
+ * Generate reputation and trust system data
+ */
+export async function reputationSystems() {
+  console.log('\n⭐ Example 5: Reputation and Trust Systems\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate agent reputation profiles
+  const reputationProfiles = await synth.generateStructured({
+    count: 100,
+    schema: {
+      agent_id: 'agent-{1-100}',
+      overall_reputation: 'number (0-100)',
+      reputation_components: {
+        reliability: 'number (0-100)',
+        expertise: 'number (0-100)',
+        collaboration: 'number (0-100)',
+        responsiveness: 'number (0-100)',
+        quality: 'number (0-100)',
+      },
+      trust_score: 'number (0-100)',
+      endorsements: 'number (0-50)',
+      negative_feedback: 'number (0-20)',
+      tasks_completed: 'number (0-1000)',
+      success_rate: 'number (0-100)',
+      tenure_days: 'number (1-730)',
+      reputation_trend: 'rising | stable | declining',
+      badges: ['array of 0-5 achievement badges or empty'],
+    },
+    constraints: [
+      'Overall reputation should correlate with component scores',
+      'Higher success rate should correlate with higher reputation',
+      'Trust score should factor in tenure and feedback',
+    ],
+  });
+
+  // Generate reputation events
+  const reputationEvents = await synth.generateEvents({
+    count: 500,
+    eventTypes: [
+      'endorsement_received',
+      'feedback_positive',
+      'feedback_negative',
+      'task_success',
+      'task_failure',
+      'badge_earned',
+      'collaboration_rated',
+    ],
+    schema: {
+      event_id: 'UUID',
+      event_type: 'one of eventTypes',
+      subject_agent_id: 'agent-{1-100}',
+      evaluator_agent_id: 'agent-{1-100} or null',
+      impact: 'number (-10 to +10)',
+      context: 'brief description',
+      evidence: 'supporting evidence description or null',
+      timestamp: 'ISO timestamp',
+    },
+    distribution: 'poisson',
+  });
+
+  // Generate trust relationships
+  const trustRelationships = await synth.generateStructured({
+    count: 300,
+    schema: {
+      relationship_id: 'UUID',
+      trustor_agent_id: 'agent-{1-100}',
+      trustee_agent_id: 'agent-{1-100}',
+      trust_level: 'number (0-100)',
+      relationship_type: 'direct | transitive | institutional',
+      interaction_count: 'number (1-100)',
+      successful_interactions: 'number (proportional to interaction_count)',
+      last_interaction: 'ISO timestamp',
+      trust_evolution: 'building | established | declining',
+    },
+    constraints: [
+      'trustor_agent_id should be different from trustee_agent_id',
+      'Trust level should correlate with success rate',
+      'More interactions should increase trust stability',
+    ],
+  });
+
+  // Generate reputation decay and recovery
+  const reputationChanges = await synth.generateTimeSeries({
+    count: 200,
+    interval: '1d',
+    metrics: [
+      'avg_reputation',
+      'reputation_variance',
+      'positive_events',
+      'negative_events',
+      'trust_network_density',
+      'endorsement_rate',
+    ],
+    trend: 'stable',
+  });
+
+  console.log('Reputation System Analysis:');
+  console.log(`- Agent profiles: ${reputationProfiles.data.length}`);
+  console.log(`- Reputation events: ${reputationEvents.data.length}`);
+  console.log(`- Trust relationships: ${trustRelationships.data.length}`);
+  console.log(`- Time series points: ${reputationChanges.data.length}`);
+
+  // Analyze reputation distribution
+  const highReputation = reputationProfiles.data.filter(
+    (p: any) => p.overall_reputation >= 80
+  ).length;
+  const lowReputation = reputationProfiles.data.filter(
+    (p: any) => p.overall_reputation < 40
+  ).length;
+
+  console.log(`\nReputation Distribution:`);
+  console.log(`- High reputation (≥80): ${highReputation} (${((highReputation / reputationProfiles.data.length) * 100).toFixed(1)}%)`);
+  console.log(`- Low reputation (<40): ${lowReputation} (${((lowReputation / reputationProfiles.data.length) * 100).toFixed(1)}%)`);
+
+  // Trust network analysis
+  const avgTrustLevel = trustRelationships.data.reduce(
+    (sum: number, r: any) => sum + r.trust_level,
+    0
+  ) / trustRelationships.data.length;
+
+  console.log(`\nTrust Network:`);
+  console.log(`- Average trust level: ${avgTrustLevel.toFixed(1)}/100`);
+  console.log(`- Established relationships: ${trustRelationships.data.filter((r: any) => r.trust_evolution === 'established').length}`);
+
+  // Integration with reputation tracking
+  console.log('\nReputation Tracking Integration:');
+  console.log('// Store reputation events in AgenticDB');
+  console.log('// Use claude-flow hooks to update reputation after tasks');
+  console.log('npx claude-flow@alpha hooks post-task --update-reputation true');
+
+  return { reputationProfiles, reputationEvents, trustRelationships, reputationChanges };
+}
+
+// ============================================================================
+// Run All Examples
+// ============================================================================
+
+export async function runAllCollectiveIntelligenceExamples() {
+  console.log('🚀 Running All Collective Intelligence Examples\n');
+  console.log('='.repeat(70));
+
+  try {
+    await collaborativeProblemSolving();
+    console.log('='.repeat(70));
+
+    await knowledgeSharingPatterns();
+    console.log('='.repeat(70));
+
+    await emergentBehaviorSimulation();
+    console.log('='.repeat(70));
+
+    await votingAndConsensusData();
+    console.log('='.repeat(70));
+
+    await reputationSystems();
+    console.log('='.repeat(70));
+
+    console.log('\n✅ All collective intelligence examples completed!\n');
+  } catch (error: any) {
+    console.error('❌ Error running examples:', error.message);
+    throw error;
+  }
+}
+
+// Run if executed directly
+if (import.meta.url === `file://${process.argv[1]}`) {
+  runAllCollectiveIntelligenceExamples().catch(console.error);
+}

vendor/ruvector/npm/packages/agentic-synth/examples/swarms/distributed-processing.d.ts

@@ -0,0 +1,58 @@
+/**
+ * Distributed Processing Examples
+ *
+ * Demonstrates distributed computation patterns including map-reduce,
+ * worker pools, message queues, event-driven architectures, and
+ * saga pattern transactions for multi-agent systems.
+ *
+ * Integrates with:
+ * - claude-flow: Distributed workflow orchestration
+ * - Apache Kafka: Event streaming
+ * - RabbitMQ: Message queuing
+ * - Redis: Distributed caching
+ */
+/**
+ * Generate map-reduce job execution data for distributed processing
+ */
+export declare function mapReduceJobData(): Promise<import("../../dist/index.js").GenerationResult<unknown>>;
+/**
+ * Generate worker pool execution data
+ */
+export declare function workerPoolSimulation(): Promise<{
+    poolStates: import("../../dist/index.js").GenerationResult<unknown>;
+    workerMetrics: import("../../dist/index.js").GenerationResult<unknown>;
+    taskExecutions: import("../../dist/index.js").GenerationResult<unknown>;
+}>;
+/**
+ * Generate message queue data (RabbitMQ, SQS, etc.)
+ */
+export declare function messageQueueScenarios(): Promise<{
+    queueMetrics: import("../../dist/index.js").GenerationResult<unknown>;
+    messages: import("../../dist/index.js").GenerationResult<unknown>;
+    consumers: import("../../dist/index.js").GenerationResult<unknown>;
+}>;
+/**
+ * Generate event-driven architecture data (Kafka, EventBridge)
+ */
+export declare function eventDrivenArchitecture(): Promise<{
+    events: import("../../dist/index.js").GenerationResult<unknown>;
+    handlers: import("../../dist/index.js").GenerationResult<unknown>;
+    projections: import("../../dist/index.js").GenerationResult<unknown>;
+}>;
+/**
+ * Generate saga pattern distributed transaction data
+ */
+export declare function sagaPatternTransactions(): Promise<{
+    sagas: import("../../dist/index.js").GenerationResult<unknown>;
+    sagaEvents: import("../../dist/index.js").GenerationResult<unknown>;
+}>;
+/**
+ * Generate stream processing pipeline data (Kafka Streams, Flink)
+ */
+export declare function streamProcessingPipeline(): Promise<{
+    pipeline: import("../../dist/index.js").GenerationResult<unknown>;
+    metrics: import("../../dist/index.js").GenerationResult<unknown>;
+    aggregations: import("../../dist/index.js").GenerationResult<unknown>;
+}>;
+export declare function runAllDistributedProcessingExamples(): Promise<void>;
+//# sourceMappingURL=distributed-processing.d.ts.map

vendor/ruvector/npm/packages/agentic-synth/examples/swarms/distributed-processing.d.ts.map

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vendor/ruvector/npm/packages/agentic-synth/examples/swarms/distributed-processing.js

@@ -0,0 +1,621 @@
+"use strict";
+/**
+ * Distributed Processing Examples
+ *
+ * Demonstrates distributed computation patterns including map-reduce,
+ * worker pools, message queues, event-driven architectures, and
+ * saga pattern transactions for multi-agent systems.
+ *
+ * Integrates with:
+ * - claude-flow: Distributed workflow orchestration
+ * - Apache Kafka: Event streaming
+ * - RabbitMQ: Message queuing
+ * - Redis: Distributed caching
+ */
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.mapReduceJobData = mapReduceJobData;
+exports.workerPoolSimulation = workerPoolSimulation;
+exports.messageQueueScenarios = messageQueueScenarios;
+exports.eventDrivenArchitecture = eventDrivenArchitecture;
+exports.sagaPatternTransactions = sagaPatternTransactions;
+exports.streamProcessingPipeline = streamProcessingPipeline;
+exports.runAllDistributedProcessingExamples = runAllDistributedProcessingExamples;
+const index_js_1 = require("../../dist/index.js");
+// ============================================================================
+// Example 1: Map-Reduce Job Data
+// ============================================================================
+/**
+ * Generate map-reduce job execution data for distributed processing
+ */
+async function mapReduceJobData() {
+    console.log('\n🗺️  Example 1: Map-Reduce Job Execution\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+        cacheStrategy: 'memory',
+    });
+    // Generate map-reduce jobs
+    const jobs = await synth.generateStructured({
+        count: 20,
+        schema: {
+            job_id: 'UUID',
+            job_name: 'descriptive job name (e.g., "Word Count Analysis")',
+            input_size_mb: 'number (100-10000)',
+            map_phase: {
+                mapper_count: 'number (10-100)',
+                tasks: [
+                    {
+                        task_id: 'UUID',
+                        mapper_id: 'mapper-{1-100}',
+                        input_split: 'split-{id}',
+                        input_size_mb: 'number (proportional to job input)',
+                        output_records: 'number (1000-100000)',
+                        execution_time_ms: 'number (1000-30000)',
+                        status: 'completed | failed | running',
+                    },
+                ],
+                start_time: 'ISO timestamp',
+                end_time: 'ISO timestamp',
+                duration_ms: 'number (sum of task times)',
+            },
+            shuffle_phase: {
+                data_transferred_mb: 'number (input_size_mb * 0.8-1.2)',
+                partitions: 'number (10-50)',
+                transfer_time_ms: 'number (5000-60000)',
+            },
+            reduce_phase: {
+                reducer_count: 'number (5-30)',
+                tasks: [
+                    {
+                        task_id: 'UUID',
+                        reducer_id: 'reducer-{1-30}',
+                        partition_id: 'number',
+                        input_records: 'number (10000-500000)',
+                        output_records: 'number (100-10000)',
+                        execution_time_ms: 'number (2000-40000)',
+                        status: 'completed | failed | running',
+                    },
+                ],
+                start_time: 'ISO timestamp',
+                end_time: 'ISO timestamp',
+                duration_ms: 'number',
+            },
+            overall_status: 'completed | failed | running',
+            total_duration_ms: 'number',
+            efficiency_score: 'number (0.5-1.0)',
+        },
+        constraints: [
+            'Map tasks should run in parallel',
+            'Reduce phase starts after map phase completes',
+            '95% of tasks should be completed',
+            'Efficiency should correlate with parallelism',
+        ],
+    });
+    // Analyze map-reduce performance
+    const completedJobs = jobs.data.filter((j) => j.overall_status === 'completed');
+    const avgEfficiency = completedJobs.reduce((sum, j) => sum + j.efficiency_score, 0) / completedJobs.length;
+    console.log('Map-Reduce Analysis:');
+    console.log(`- Total jobs: ${jobs.data.length}`);
+    console.log(`- Completed: ${completedJobs.length}`);
+    console.log(`- Average efficiency: ${(avgEfficiency * 100).toFixed(1)}%`);
+    console.log(`- Total data processed: ${jobs.data.reduce((sum, j) => sum + j.input_size_mb, 0).toFixed(0)} MB`);
+    // Calculate parallelism metrics
+    const avgMappers = jobs.data.reduce((sum, j) => sum + j.map_phase.mapper_count, 0) / jobs.data.length;
+    const avgReducers = jobs.data.reduce((sum, j) => sum + j.reduce_phase.reducer_count, 0) / jobs.data.length;
+    console.log(`\nParallelism Metrics:`);
+    console.log(`- Average mappers: ${avgMappers.toFixed(1)}`);
+    console.log(`- Average reducers: ${avgReducers.toFixed(1)}`);
+    // Integration with claude-flow
+    console.log('\nClaude-Flow Integration:');
+    console.log('npx claude-flow@alpha hooks pre-task --description "Map-Reduce job execution"');
+    console.log('// Store results in coordination memory for analysis');
+    return jobs;
+}
+// ============================================================================
+// Example 2: Worker Pool Simulation
+// ============================================================================
+/**
+ * Generate worker pool execution data
+ */
+async function workerPoolSimulation() {
+    console.log('\n👷 Example 2: Worker Pool Simulation\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate worker pool state over time
+    const poolStates = await synth.generateTimeSeries({
+        count: 100,
+        interval: '1m',
+        metrics: [
+            'active_workers',
+            'idle_workers',
+            'queue_size',
+            'tasks_per_minute',
+            'avg_task_duration_ms',
+            'worker_utilization',
+        ],
+        trend: 'mixed',
+        seasonality: true,
+    });
+    // Generate individual worker performance
+    const workerMetrics = await synth.generateStructured({
+        count: 200,
+        schema: {
+            timestamp: 'ISO timestamp',
+            worker_id: 'worker-{1-20}',
+            state: 'idle | busy | initializing | terminating',
+            current_task_id: 'UUID or null',
+            tasks_completed: 'number (0-1000)',
+            tasks_failed: 'number (0-50)',
+            avg_execution_time_ms: 'number (100-5000)',
+            cpu_usage: 'number (0-100)',
+            memory_mb: 'number (100-2000)',
+            uptime_seconds: 'number (0-86400)',
+            last_heartbeat: 'ISO timestamp',
+        },
+        constraints: [
+            'Busy workers should have current_task_id',
+            'Idle workers should have null current_task_id',
+            'High task count should correlate with low avg execution time',
+        ],
+    });
+    // Generate task execution history
+    const taskExecutions = await synth.generateEvents({
+        count: 500,
+        eventTypes: ['task_queued', 'task_assigned', 'task_started', 'task_completed', 'task_failed'],
+        schema: {
+            event_id: 'UUID',
+            task_id: 'UUID',
+            event_type: 'one of eventTypes',
+            worker_id: 'worker-{1-20} or null',
+            queue_wait_time_ms: 'number (0-10000)',
+            execution_time_ms: 'number (100-5000)',
+            priority: 'number (1-10)',
+            timestamp: 'ISO timestamp',
+        },
+        distribution: 'poisson',
+    });
+    console.log('Worker Pool Analysis:');
+    console.log(`- Time series points: ${poolStates.data.length}`);
+    console.log(`- Worker metrics: ${workerMetrics.data.length}`);
+    console.log(`- Task executions: ${taskExecutions.data.length}`);
+    // Calculate pool efficiency
+    const avgUtilization = poolStates.data.reduce((sum, p) => sum + p.worker_utilization, 0) / poolStates.data.length;
+    console.log(`\nPool Efficiency:`);
+    console.log(`- Average utilization: ${avgUtilization.toFixed(1)}%`);
+    console.log(`- Active workers: ${workerMetrics.data.filter((w) => w.state === 'busy').length}`);
+    console.log(`- Idle workers: ${workerMetrics.data.filter((w) => w.state === 'idle').length}`);
+    // Integration patterns
+    console.log('\nIntegration Pattern:');
+    console.log('// Bull Queue (Redis-based)');
+    console.log('const queue = new Queue("tasks", { redis: redisConfig });');
+    console.log('// Process with worker pool');
+    console.log('queue.process(concurrency, async (job) => { /* ... */ });');
+    return { poolStates, workerMetrics, taskExecutions };
+}
+// ============================================================================
+// Example 3: Message Queue Scenarios
+// ============================================================================
+/**
+ * Generate message queue data (RabbitMQ, SQS, etc.)
+ */
+async function messageQueueScenarios() {
+    console.log('\n📬 Example 3: Message Queue Scenarios\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate queue metrics
+    const queueMetrics = await synth.generateTimeSeries({
+        count: 150,
+        interval: '30s',
+        metrics: [
+            'messages_published',
+            'messages_consumed',
+            'queue_depth',
+            'consumer_count',
+            'publish_rate',
+            'consume_rate',
+            'avg_latency_ms',
+        ],
+        trend: 'up',
+        seasonality: false,
+    });
+    // Generate message data
+    const messages = await synth.generateStructured({
+        count: 1000,
+        schema: {
+            message_id: 'UUID',
+            queue_name: 'tasks | events | notifications | dead_letter',
+            priority: 'number (0-9)',
+            payload_size_bytes: 'number (100-10000)',
+            content_type: 'application/json | text/plain | application/octet-stream',
+            routing_key: 'routing key pattern',
+            headers: {
+                correlation_id: 'UUID',
+                reply_to: 'queue name or null',
+                timestamp: 'ISO timestamp',
+                retry_count: 'number (0-5)',
+            },
+            published_at: 'ISO timestamp',
+            consumed_at: 'ISO timestamp or null',
+            acknowledged_at: 'ISO timestamp or null',
+            status: 'pending | consumed | acknowledged | dead_letter | expired',
+            time_in_queue_ms: 'number (0-60000)',
+            consumer_id: 'consumer-{1-15} or null',
+        },
+        constraints: [
+            'Consumed messages should have consumed_at timestamp',
+            'Acknowledged messages should have acknowledged_at after consumed_at',
+            '5% of messages should be in dead_letter queue',
+            'Higher priority messages should have lower time_in_queue_ms',
+        ],
+    });
+    // Generate consumer performance
+    const consumers = await synth.generateStructured({
+        count: 15,
+        schema: {
+            consumer_id: 'consumer-{1-15}',
+            queue_subscriptions: ['array of 1-3 queue names'],
+            messages_processed: 'number (0-200)',
+            processing_rate_per_second: 'number (1-50)',
+            error_count: 'number (0-20)',
+            avg_processing_time_ms: 'number (100-2000)',
+            prefetch_count: 'number (1-100)',
+            status: 'active | idle | error | disconnected',
+            last_activity: 'ISO timestamp',
+        },
+    });
+    console.log('Message Queue Analysis:');
+    console.log(`- Total messages: ${messages.data.length}`);
+    console.log(`- Pending: ${messages.data.filter((m) => m.status === 'pending').length}`);
+    console.log(`- Consumed: ${messages.data.filter((m) => m.status === 'consumed').length}`);
+    console.log(`- Dead letter: ${messages.data.filter((m) => m.status === 'dead_letter').length}`);
+    // Calculate throughput
+    const totalProcessed = consumers.data.reduce((sum, c) => sum + c.messages_processed, 0);
+    const avgLatency = messages.data
+        .filter((m) => m.time_in_queue_ms)
+        .reduce((sum, m) => sum + m.time_in_queue_ms, 0) / messages.data.length;
+    console.log(`\nThroughput Metrics:`);
+    console.log(`- Total processed: ${totalProcessed}`);
+    console.log(`- Active consumers: ${consumers.data.filter((c) => c.status === 'active').length}`);
+    console.log(`- Average latency: ${avgLatency.toFixed(0)}ms`);
+    // RabbitMQ integration example
+    console.log('\nRabbitMQ Integration:');
+    console.log('// Connect to RabbitMQ');
+    console.log('const channel = await connection.createChannel();');
+    console.log('await channel.assertQueue("tasks", { durable: true });');
+    console.log('// Publish with agentic-synth data');
+    return { queueMetrics, messages, consumers };
+}
+// ============================================================================
+// Example 4: Event-Driven Architecture
+// ============================================================================
+/**
+ * Generate event-driven architecture data (Kafka, EventBridge)
+ */
+async function eventDrivenArchitecture() {
+    console.log('\n⚡ Example 4: Event-Driven Architecture\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate event streams
+    const events = await synth.generateEvents({
+        count: 2000,
+        eventTypes: [
+            'user.created',
+            'user.updated',
+            'order.placed',
+            'order.confirmed',
+            'order.shipped',
+            'payment.processed',
+            'inventory.updated',
+            'notification.sent',
+        ],
+        schema: {
+            event_id: 'UUID',
+            event_type: 'one of eventTypes',
+            event_version: 'v1 | v2',
+            aggregate_id: 'UUID',
+            aggregate_type: 'user | order | payment | inventory',
+            payload: 'JSON object with event data',
+            metadata: {
+                causation_id: 'UUID (triggering event)',
+                correlation_id: 'UUID (workflow id)',
+                timestamp: 'ISO timestamp',
+                source_service: 'service name',
+                user_id: 'UUID or null',
+            },
+            partition_key: 'aggregate_id',
+            sequence_number: 'number',
+            published_at: 'ISO timestamp',
+        },
+        distribution: 'poisson',
+        timeRange: {
+            start: new Date(Date.now() - 3600000),
+            end: new Date(),
+        },
+    });
+    // Generate event handlers (subscribers)
+    const handlers = await synth.generateStructured({
+        count: 30,
+        schema: {
+            handler_id: 'UUID',
+            handler_name: 'descriptive name',
+            subscribed_events: ['array of 1-5 event types'],
+            service_name: 'service name',
+            processing_mode: 'sync | async | batch',
+            events_processed: 'number (0-500)',
+            events_failed: 'number (0-50)',
+            avg_processing_time_ms: 'number (50-2000)',
+            retry_policy: {
+                max_retries: 'number (3-10)',
+                backoff_strategy: 'exponential | linear | constant',
+                dead_letter_queue: 'boolean',
+            },
+            status: 'active | degraded | failing | disabled',
+        },
+    });
+    // Generate event projections (read models)
+    const projections = await synth.generateStructured({
+        count: 100,
+        schema: {
+            projection_id: 'UUID',
+            projection_type: 'user_profile | order_summary | inventory_view',
+            aggregate_id: 'UUID',
+            version: 'number (1-100)',
+            data: 'JSON object representing current state',
+            last_event_id: 'UUID (from events)',
+            last_updated: 'ISO timestamp',
+            consistency_lag_ms: 'number (0-5000)',
+        },
+    });
+    console.log('Event-Driven Architecture Analysis:');
+    console.log(`- Total events: ${events.data.length}`);
+    console.log(`- Event handlers: ${handlers.data.length}`);
+    console.log(`- Projections: ${projections.data.length}`);
+    // Event type distribution
+    const eventTypes = new Map();
+    events.data.forEach((e) => {
+        eventTypes.set(e.event_type, (eventTypes.get(e.event_type) || 0) + 1);
+    });
+    console.log('\nEvent Distribution:');
+    eventTypes.forEach((count, type) => {
+        console.log(`- ${type}: ${count}`);
+    });
+    // Calculate average consistency lag
+    const avgLag = projections.data.reduce((sum, p) => sum + p.consistency_lag_ms, 0) / projections.data.length;
+    console.log(`\nConsistency Metrics:`);
+    console.log(`- Average lag: ${avgLag.toFixed(0)}ms`);
+    console.log(`- Active handlers: ${handlers.data.filter((h) => h.status === 'active').length}`);
+    // Kafka integration
+    console.log('\nKafka Integration:');
+    console.log('// Produce events');
+    console.log('await producer.send({ topic: "events", messages: [...] });');
+    console.log('// Consume with handler coordination');
+    console.log('await consumer.run({ eachMessage: async ({ message }) => { /* ... */ } });');
+    return { events, handlers, projections };
+}
+// ============================================================================
+// Example 5: Saga Pattern Transactions
+// ============================================================================
+/**
+ * Generate saga pattern distributed transaction data
+ */
+async function sagaPatternTransactions() {
+    console.log('\n🔄 Example 5: Saga Pattern Transactions\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate saga executions
+    const sagas = await synth.generateStructured({
+        count: 100,
+        schema: {
+            saga_id: 'UUID',
+            saga_type: 'order_fulfillment | payment_processing | user_registration | booking_reservation',
+            orchestration_type: 'orchestration | choreography',
+            initiator_service: 'service name',
+            steps: [
+                {
+                    step_id: 'UUID',
+                    step_name: 'descriptive step name',
+                    service: 'service name',
+                    operation: 'operation name',
+                    compensation_operation: 'compensation operation name',
+                    status: 'pending | executing | completed | failed | compensating | compensated',
+                    started_at: 'ISO timestamp or null',
+                    completed_at: 'ISO timestamp or null',
+                    duration_ms: 'number (100-5000)',
+                    retry_count: 'number (0-3)',
+                },
+            ],
+            overall_status: 'in_progress | completed | failed | compensated',
+            started_at: 'ISO timestamp',
+            completed_at: 'ISO timestamp or null',
+            total_duration_ms: 'number',
+            compensation_triggered: 'boolean',
+            compensation_reason: 'error description or null',
+        },
+        constraints: [
+            'Sagas should have 3-8 steps',
+            'Failed sagas should have compensation_triggered = true',
+            'Compensated steps should execute in reverse order',
+            '80% of sagas should complete successfully',
+        ],
+    });
+    // Generate saga events
+    const sagaEvents = await synth.generateEvents({
+        count: 500,
+        eventTypes: [
+            'saga_started',
+            'step_started',
+            'step_completed',
+            'step_failed',
+            'compensation_started',
+            'compensation_completed',
+            'saga_completed',
+            'saga_failed',
+        ],
+        schema: {
+            event_id: 'UUID',
+            saga_id: 'UUID (from sagas)',
+            step_id: 'UUID or null',
+            event_type: 'one of eventTypes',
+            service: 'service name',
+            payload: 'JSON object',
+            timestamp: 'ISO timestamp',
+        },
+    });
+    // Analyze saga patterns
+    const successfulSagas = sagas.data.filter((s) => s.overall_status === 'completed');
+    const failedSagas = sagas.data.filter((s) => s.overall_status === 'failed');
+    const compensatedSagas = sagas.data.filter((s) => s.overall_status === 'compensated');
+    console.log('Saga Pattern Analysis:');
+    console.log(`- Total sagas: ${sagas.data.length}`);
+    console.log(`- Successful: ${successfulSagas.length} (${((successfulSagas.length / sagas.data.length) * 100).toFixed(1)}%)`);
+    console.log(`- Failed: ${failedSagas.length}`);
+    console.log(`- Compensated: ${compensatedSagas.length}`);
+    // Calculate average steps and duration
+    const avgSteps = sagas.data.reduce((sum, s) => sum + s.steps.length, 0) / sagas.data.length;
+    const avgDuration = sagas.data
+        .filter((s) => s.completed_at)
+        .reduce((sum, s) => sum + s.total_duration_ms, 0) / successfulSagas.length;
+    console.log(`\nTransaction Metrics:`);
+    console.log(`- Average steps per saga: ${avgSteps.toFixed(1)}`);
+    console.log(`- Average duration: ${avgDuration.toFixed(0)}ms`);
+    console.log(`- Compensation rate: ${((compensatedSagas.length / sagas.data.length) * 100).toFixed(1)}%`);
+    // Orchestration vs Choreography
+    const orchestrated = sagas.data.filter((s) => s.orchestration_type === 'orchestration').length;
+    const choreographed = sagas.data.filter((s) => s.orchestration_type === 'choreography').length;
+    console.log(`\nOrchestration Style:`);
+    console.log(`- Orchestration: ${orchestrated}`);
+    console.log(`- Choreography: ${choreographed}`);
+    // Integration with coordination frameworks
+    console.log('\nIntegration Pattern:');
+    console.log('// MassTransit Saga State Machine');
+    console.log('class OrderSaga : MassTransitStateMachine<OrderState> { /* ... */ }');
+    console.log('// Or custom orchestrator with agentic-synth test data');
+    return { sagas, sagaEvents };
+}
+// ============================================================================
+// Example 6: Stream Processing Pipeline
+// ============================================================================
+/**
+ * Generate stream processing pipeline data (Kafka Streams, Flink)
+ */
+async function streamProcessingPipeline() {
+    console.log('\n🌊 Example 6: Stream Processing Pipeline\n');
+    const synth = (0, index_js_1.createSynth)({
+        provider: 'gemini',
+        apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    });
+    // Generate pipeline topology
+    const pipeline = await synth.generateStructured({
+        count: 1,
+        schema: {
+            pipeline_id: 'UUID',
+            pipeline_name: 'descriptive name',
+            stages: [
+                {
+                    stage_id: 'UUID',
+                    stage_type: 'source | transform | aggregate | filter | sink',
+                    stage_name: 'stage name',
+                    parallelism: 'number (1-20)',
+                    input_topics: ['array of topic names'],
+                    output_topics: ['array of topic names'],
+                    transformation: 'description of transformation',
+                    windowing: {
+                        type: 'tumbling | sliding | session | global',
+                        size_ms: 'number (1000-300000)',
+                        slide_ms: 'number or null',
+                    },
+                    state_store: 'store name or null',
+                },
+            ],
+        },
+    });
+    // Generate processing metrics
+    const metrics = await synth.generateTimeSeries({
+        count: 200,
+        interval: '30s',
+        metrics: [
+            'records_in',
+            'records_out',
+            'records_filtered',
+            'processing_latency_ms',
+            'watermark_lag_ms',
+            'cpu_usage',
+            'memory_mb',
+        ],
+        trend: 'mixed',
+    });
+    // Generate windowed aggregations
+    const aggregations = await synth.generateStructured({
+        count: 150,
+        schema: {
+            window_id: 'UUID',
+            stage_id: 'UUID (from pipeline)',
+            window_start: 'ISO timestamp',
+            window_end: 'ISO timestamp',
+            record_count: 'number (100-10000)',
+            aggregate_values: {
+                sum: 'number',
+                avg: 'number',
+                min: 'number',
+                max: 'number',
+                count: 'number',
+            },
+            emitted_at: 'ISO timestamp',
+            late_arrivals: 'number (0-100)',
+        },
+    });
+    console.log('Stream Processing Analysis:');
+    console.log(`- Pipeline stages: ${pipeline.data[0].stages.length}`);
+    console.log(`- Metric points: ${metrics.data.length}`);
+    console.log(`- Window aggregations: ${aggregations.data.length}`);
+    // Calculate throughput
+    const avgRecordsIn = metrics.data.reduce((sum, m) => sum + m.records_in, 0) / metrics.data.length;
+    const avgRecordsOut = metrics.data.reduce((sum, m) => sum + m.records_out, 0) / metrics.data.length;
+    console.log(`\nThroughput:`);
+    console.log(`- Input: ${avgRecordsIn.toFixed(0)} records/interval`);
+    console.log(`- Output: ${avgRecordsOut.toFixed(0)} records/interval`);
+    console.log(`- Filter rate: ${(((avgRecordsIn - avgRecordsOut) / avgRecordsIn) * 100).toFixed(1)}%`);
+    console.log('\nApache Flink Integration:');
+    console.log('// Define stream processing job');
+    console.log('env.addSource(kafkaSource).keyBy(...).window(...).aggregate(...).addSink(kafkaSink);');
+    return { pipeline, metrics, aggregations };
+}
+// ============================================================================
+// Run All Examples
+// ============================================================================
+async function runAllDistributedProcessingExamples() {
+    console.log('🚀 Running All Distributed Processing Examples\n');
+    console.log('='.repeat(70));
+    try {
+        await mapReduceJobData();
+        console.log('='.repeat(70));
+        await workerPoolSimulation();
+        console.log('='.repeat(70));
+        await messageQueueScenarios();
+        console.log('='.repeat(70));
+        await eventDrivenArchitecture();
+        console.log('='.repeat(70));
+        await sagaPatternTransactions();
+        console.log('='.repeat(70));
+        await streamProcessingPipeline();
+        console.log('='.repeat(70));
+        console.log('\n✅ All distributed processing examples completed!\n');
+    }
+    catch (error) {
+        console.error('❌ Error running examples:', error.message);
+        throw error;
+    }
+}
+// Run if executed directly
+if (import.meta.url === `file://${process.argv[1]}`) {
+    runAllDistributedProcessingExamples().catch(console.error);
+}
+//# sourceMappingURL=distributed-processing.js.map

vendor/ruvector/npm/packages/agentic-synth/examples/swarms/distributed-processing.ts

@@ -0,0 +1,700 @@
+/**
+ * Distributed Processing Examples
+ *
+ * Demonstrates distributed computation patterns including map-reduce,
+ * worker pools, message queues, event-driven architectures, and
+ * saga pattern transactions for multi-agent systems.
+ *
+ * Integrates with:
+ * - claude-flow: Distributed workflow orchestration
+ * - Apache Kafka: Event streaming
+ * - RabbitMQ: Message queuing
+ * - Redis: Distributed caching
+ */
+
+import { AgenticSynth, createSynth } from '../../dist/index.js';
+import type { GenerationResult } from '../../src/types.js';
+
+// ============================================================================
+// Example 1: Map-Reduce Job Data
+// ============================================================================
+
+/**
+ * Generate map-reduce job execution data for distributed processing
+ */
+export async function mapReduceJobData() {
+  console.log('\n🗺️  Example 1: Map-Reduce Job Execution\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+    cacheStrategy: 'memory',
+  });
+
+  // Generate map-reduce jobs
+  const jobs = await synth.generateStructured({
+    count: 20,
+    schema: {
+      job_id: 'UUID',
+      job_name: 'descriptive job name (e.g., "Word Count Analysis")',
+      input_size_mb: 'number (100-10000)',
+      map_phase: {
+        mapper_count: 'number (10-100)',
+        tasks: [
+          {
+            task_id: 'UUID',
+            mapper_id: 'mapper-{1-100}',
+            input_split: 'split-{id}',
+            input_size_mb: 'number (proportional to job input)',
+            output_records: 'number (1000-100000)',
+            execution_time_ms: 'number (1000-30000)',
+            status: 'completed | failed | running',
+          },
+        ],
+        start_time: 'ISO timestamp',
+        end_time: 'ISO timestamp',
+        duration_ms: 'number (sum of task times)',
+      },
+      shuffle_phase: {
+        data_transferred_mb: 'number (input_size_mb * 0.8-1.2)',
+        partitions: 'number (10-50)',
+        transfer_time_ms: 'number (5000-60000)',
+      },
+      reduce_phase: {
+        reducer_count: 'number (5-30)',
+        tasks: [
+          {
+            task_id: 'UUID',
+            reducer_id: 'reducer-{1-30}',
+            partition_id: 'number',
+            input_records: 'number (10000-500000)',
+            output_records: 'number (100-10000)',
+            execution_time_ms: 'number (2000-40000)',
+            status: 'completed | failed | running',
+          },
+        ],
+        start_time: 'ISO timestamp',
+        end_time: 'ISO timestamp',
+        duration_ms: 'number',
+      },
+      overall_status: 'completed | failed | running',
+      total_duration_ms: 'number',
+      efficiency_score: 'number (0.5-1.0)',
+    },
+    constraints: [
+      'Map tasks should run in parallel',
+      'Reduce phase starts after map phase completes',
+      '95% of tasks should be completed',
+      'Efficiency should correlate with parallelism',
+    ],
+  });
+
+  // Analyze map-reduce performance
+  const completedJobs = jobs.data.filter((j: any) => j.overall_status === 'completed');
+  const avgEfficiency = completedJobs.reduce((sum: number, j: any) => sum + j.efficiency_score, 0) / completedJobs.length;
+
+  console.log('Map-Reduce Analysis:');
+  console.log(`- Total jobs: ${jobs.data.length}`);
+  console.log(`- Completed: ${completedJobs.length}`);
+  console.log(`- Average efficiency: ${(avgEfficiency * 100).toFixed(1)}%`);
+  console.log(`- Total data processed: ${jobs.data.reduce((sum: number, j: any) => sum + j.input_size_mb, 0).toFixed(0)} MB`);
+
+  // Calculate parallelism metrics
+  const avgMappers = jobs.data.reduce((sum: number, j: any) => sum + j.map_phase.mapper_count, 0) / jobs.data.length;
+  const avgReducers = jobs.data.reduce((sum: number, j: any) => sum + j.reduce_phase.reducer_count, 0) / jobs.data.length;
+
+  console.log(`\nParallelism Metrics:`);
+  console.log(`- Average mappers: ${avgMappers.toFixed(1)}`);
+  console.log(`- Average reducers: ${avgReducers.toFixed(1)}`);
+
+  // Integration with claude-flow
+  console.log('\nClaude-Flow Integration:');
+  console.log('npx claude-flow@alpha hooks pre-task --description "Map-Reduce job execution"');
+  console.log('// Store results in coordination memory for analysis');
+
+  return jobs;
+}
+
+// ============================================================================
+// Example 2: Worker Pool Simulation
+// ============================================================================
+
+/**
+ * Generate worker pool execution data
+ */
+export async function workerPoolSimulation() {
+  console.log('\n👷 Example 2: Worker Pool Simulation\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate worker pool state over time
+  const poolStates = await synth.generateTimeSeries({
+    count: 100,
+    interval: '1m',
+    metrics: [
+      'active_workers',
+      'idle_workers',
+      'queue_size',
+      'tasks_per_minute',
+      'avg_task_duration_ms',
+      'worker_utilization',
+    ],
+    trend: 'mixed',
+    seasonality: true,
+  });
+
+  // Generate individual worker performance
+  const workerMetrics = await synth.generateStructured({
+    count: 200,
+    schema: {
+      timestamp: 'ISO timestamp',
+      worker_id: 'worker-{1-20}',
+      state: 'idle | busy | initializing | terminating',
+      current_task_id: 'UUID or null',
+      tasks_completed: 'number (0-1000)',
+      tasks_failed: 'number (0-50)',
+      avg_execution_time_ms: 'number (100-5000)',
+      cpu_usage: 'number (0-100)',
+      memory_mb: 'number (100-2000)',
+      uptime_seconds: 'number (0-86400)',
+      last_heartbeat: 'ISO timestamp',
+    },
+    constraints: [
+      'Busy workers should have current_task_id',
+      'Idle workers should have null current_task_id',
+      'High task count should correlate with low avg execution time',
+    ],
+  });
+
+  // Generate task execution history
+  const taskExecutions = await synth.generateEvents({
+    count: 500,
+    eventTypes: ['task_queued', 'task_assigned', 'task_started', 'task_completed', 'task_failed'],
+    schema: {
+      event_id: 'UUID',
+      task_id: 'UUID',
+      event_type: 'one of eventTypes',
+      worker_id: 'worker-{1-20} or null',
+      queue_wait_time_ms: 'number (0-10000)',
+      execution_time_ms: 'number (100-5000)',
+      priority: 'number (1-10)',
+      timestamp: 'ISO timestamp',
+    },
+    distribution: 'poisson',
+  });
+
+  console.log('Worker Pool Analysis:');
+  console.log(`- Time series points: ${poolStates.data.length}`);
+  console.log(`- Worker metrics: ${workerMetrics.data.length}`);
+  console.log(`- Task executions: ${taskExecutions.data.length}`);
+
+  // Calculate pool efficiency
+  const avgUtilization = poolStates.data.reduce(
+    (sum: number, p: any) => sum + p.worker_utilization,
+    0
+  ) / poolStates.data.length;
+
+  console.log(`\nPool Efficiency:`);
+  console.log(`- Average utilization: ${avgUtilization.toFixed(1)}%`);
+  console.log(`- Active workers: ${workerMetrics.data.filter((w: any) => w.state === 'busy').length}`);
+  console.log(`- Idle workers: ${workerMetrics.data.filter((w: any) => w.state === 'idle').length}`);
+
+  // Integration patterns
+  console.log('\nIntegration Pattern:');
+  console.log('// Bull Queue (Redis-based)');
+  console.log('const queue = new Queue("tasks", { redis: redisConfig });');
+  console.log('// Process with worker pool');
+  console.log('queue.process(concurrency, async (job) => { /* ... */ });');
+
+  return { poolStates, workerMetrics, taskExecutions };
+}
+
+// ============================================================================
+// Example 3: Message Queue Scenarios
+// ============================================================================
+
+/**
+ * Generate message queue data (RabbitMQ, SQS, etc.)
+ */
+export async function messageQueueScenarios() {
+  console.log('\n📬 Example 3: Message Queue Scenarios\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate queue metrics
+  const queueMetrics = await synth.generateTimeSeries({
+    count: 150,
+    interval: '30s',
+    metrics: [
+      'messages_published',
+      'messages_consumed',
+      'queue_depth',
+      'consumer_count',
+      'publish_rate',
+      'consume_rate',
+      'avg_latency_ms',
+    ],
+    trend: 'up',
+    seasonality: false,
+  });
+
+  // Generate message data
+  const messages = await synth.generateStructured({
+    count: 1000,
+    schema: {
+      message_id: 'UUID',
+      queue_name: 'tasks | events | notifications | dead_letter',
+      priority: 'number (0-9)',
+      payload_size_bytes: 'number (100-10000)',
+      content_type: 'application/json | text/plain | application/octet-stream',
+      routing_key: 'routing key pattern',
+      headers: {
+        correlation_id: 'UUID',
+        reply_to: 'queue name or null',
+        timestamp: 'ISO timestamp',
+        retry_count: 'number (0-5)',
+      },
+      published_at: 'ISO timestamp',
+      consumed_at: 'ISO timestamp or null',
+      acknowledged_at: 'ISO timestamp or null',
+      status: 'pending | consumed | acknowledged | dead_letter | expired',
+      time_in_queue_ms: 'number (0-60000)',
+      consumer_id: 'consumer-{1-15} or null',
+    },
+    constraints: [
+      'Consumed messages should have consumed_at timestamp',
+      'Acknowledged messages should have acknowledged_at after consumed_at',
+      '5% of messages should be in dead_letter queue',
+      'Higher priority messages should have lower time_in_queue_ms',
+    ],
+  });
+
+  // Generate consumer performance
+  const consumers = await synth.generateStructured({
+    count: 15,
+    schema: {
+      consumer_id: 'consumer-{1-15}',
+      queue_subscriptions: ['array of 1-3 queue names'],
+      messages_processed: 'number (0-200)',
+      processing_rate_per_second: 'number (1-50)',
+      error_count: 'number (0-20)',
+      avg_processing_time_ms: 'number (100-2000)',
+      prefetch_count: 'number (1-100)',
+      status: 'active | idle | error | disconnected',
+      last_activity: 'ISO timestamp',
+    },
+  });
+
+  console.log('Message Queue Analysis:');
+  console.log(`- Total messages: ${messages.data.length}`);
+  console.log(`- Pending: ${messages.data.filter((m: any) => m.status === 'pending').length}`);
+  console.log(`- Consumed: ${messages.data.filter((m: any) => m.status === 'consumed').length}`);
+  console.log(`- Dead letter: ${messages.data.filter((m: any) => m.status === 'dead_letter').length}`);
+
+  // Calculate throughput
+  const totalProcessed = consumers.data.reduce(
+    (sum: number, c: any) => sum + c.messages_processed,
+    0
+  );
+  const avgLatency = messages.data
+    .filter((m: any) => m.time_in_queue_ms)
+    .reduce((sum: number, m: any) => sum + m.time_in_queue_ms, 0) / messages.data.length;
+
+  console.log(`\nThroughput Metrics:`);
+  console.log(`- Total processed: ${totalProcessed}`);
+  console.log(`- Active consumers: ${consumers.data.filter((c: any) => c.status === 'active').length}`);
+  console.log(`- Average latency: ${avgLatency.toFixed(0)}ms`);
+
+  // RabbitMQ integration example
+  console.log('\nRabbitMQ Integration:');
+  console.log('// Connect to RabbitMQ');
+  console.log('const channel = await connection.createChannel();');
+  console.log('await channel.assertQueue("tasks", { durable: true });');
+  console.log('// Publish with agentic-synth data');
+
+  return { queueMetrics, messages, consumers };
+}
+
+// ============================================================================
+// Example 4: Event-Driven Architecture
+// ============================================================================
+
+/**
+ * Generate event-driven architecture data (Kafka, EventBridge)
+ */
+export async function eventDrivenArchitecture() {
+  console.log('\n⚡ Example 4: Event-Driven Architecture\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate event streams
+  const events = await synth.generateEvents({
+    count: 2000,
+    eventTypes: [
+      'user.created',
+      'user.updated',
+      'order.placed',
+      'order.confirmed',
+      'order.shipped',
+      'payment.processed',
+      'inventory.updated',
+      'notification.sent',
+    ],
+    schema: {
+      event_id: 'UUID',
+      event_type: 'one of eventTypes',
+      event_version: 'v1 | v2',
+      aggregate_id: 'UUID',
+      aggregate_type: 'user | order | payment | inventory',
+      payload: 'JSON object with event data',
+      metadata: {
+        causation_id: 'UUID (triggering event)',
+        correlation_id: 'UUID (workflow id)',
+        timestamp: 'ISO timestamp',
+        source_service: 'service name',
+        user_id: 'UUID or null',
+      },
+      partition_key: 'aggregate_id',
+      sequence_number: 'number',
+      published_at: 'ISO timestamp',
+    },
+    distribution: 'poisson',
+    timeRange: {
+      start: new Date(Date.now() - 3600000),
+      end: new Date(),
+    },
+  });
+
+  // Generate event handlers (subscribers)
+  const handlers = await synth.generateStructured({
+    count: 30,
+    schema: {
+      handler_id: 'UUID',
+      handler_name: 'descriptive name',
+      subscribed_events: ['array of 1-5 event types'],
+      service_name: 'service name',
+      processing_mode: 'sync | async | batch',
+      events_processed: 'number (0-500)',
+      events_failed: 'number (0-50)',
+      avg_processing_time_ms: 'number (50-2000)',
+      retry_policy: {
+        max_retries: 'number (3-10)',
+        backoff_strategy: 'exponential | linear | constant',
+        dead_letter_queue: 'boolean',
+      },
+      status: 'active | degraded | failing | disabled',
+    },
+  });
+
+  // Generate event projections (read models)
+  const projections = await synth.generateStructured({
+    count: 100,
+    schema: {
+      projection_id: 'UUID',
+      projection_type: 'user_profile | order_summary | inventory_view',
+      aggregate_id: 'UUID',
+      version: 'number (1-100)',
+      data: 'JSON object representing current state',
+      last_event_id: 'UUID (from events)',
+      last_updated: 'ISO timestamp',
+      consistency_lag_ms: 'number (0-5000)',
+    },
+  });
+
+  console.log('Event-Driven Architecture Analysis:');
+  console.log(`- Total events: ${events.data.length}`);
+  console.log(`- Event handlers: ${handlers.data.length}`);
+  console.log(`- Projections: ${projections.data.length}`);
+
+  // Event type distribution
+  const eventTypes = new Map<string, number>();
+  events.data.forEach((e: any) => {
+    eventTypes.set(e.event_type, (eventTypes.get(e.event_type) || 0) + 1);
+  });
+
+  console.log('\nEvent Distribution:');
+  eventTypes.forEach((count, type) => {
+    console.log(`- ${type}: ${count}`);
+  });
+
+  // Calculate average consistency lag
+  const avgLag = projections.data.reduce(
+    (sum: number, p: any) => sum + p.consistency_lag_ms,
+    0
+  ) / projections.data.length;
+
+  console.log(`\nConsistency Metrics:`);
+  console.log(`- Average lag: ${avgLag.toFixed(0)}ms`);
+  console.log(`- Active handlers: ${handlers.data.filter((h: any) => h.status === 'active').length}`);
+
+  // Kafka integration
+  console.log('\nKafka Integration:');
+  console.log('// Produce events');
+  console.log('await producer.send({ topic: "events", messages: [...] });');
+  console.log('// Consume with handler coordination');
+  console.log('await consumer.run({ eachMessage: async ({ message }) => { /* ... */ } });');
+
+  return { events, handlers, projections };
+}
+
+// ============================================================================
+// Example 5: Saga Pattern Transactions
+// ============================================================================
+
+/**
+ * Generate saga pattern distributed transaction data
+ */
+export async function sagaPatternTransactions() {
+  console.log('\n🔄 Example 5: Saga Pattern Transactions\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate saga executions
+  const sagas = await synth.generateStructured({
+    count: 100,
+    schema: {
+      saga_id: 'UUID',
+      saga_type: 'order_fulfillment | payment_processing | user_registration | booking_reservation',
+      orchestration_type: 'orchestration | choreography',
+      initiator_service: 'service name',
+      steps: [
+        {
+          step_id: 'UUID',
+          step_name: 'descriptive step name',
+          service: 'service name',
+          operation: 'operation name',
+          compensation_operation: 'compensation operation name',
+          status: 'pending | executing | completed | failed | compensating | compensated',
+          started_at: 'ISO timestamp or null',
+          completed_at: 'ISO timestamp or null',
+          duration_ms: 'number (100-5000)',
+          retry_count: 'number (0-3)',
+        },
+      ],
+      overall_status: 'in_progress | completed | failed | compensated',
+      started_at: 'ISO timestamp',
+      completed_at: 'ISO timestamp or null',
+      total_duration_ms: 'number',
+      compensation_triggered: 'boolean',
+      compensation_reason: 'error description or null',
+    },
+    constraints: [
+      'Sagas should have 3-8 steps',
+      'Failed sagas should have compensation_triggered = true',
+      'Compensated steps should execute in reverse order',
+      '80% of sagas should complete successfully',
+    ],
+  });
+
+  // Generate saga events
+  const sagaEvents = await synth.generateEvents({
+    count: 500,
+    eventTypes: [
+      'saga_started',
+      'step_started',
+      'step_completed',
+      'step_failed',
+      'compensation_started',
+      'compensation_completed',
+      'saga_completed',
+      'saga_failed',
+    ],
+    schema: {
+      event_id: 'UUID',
+      saga_id: 'UUID (from sagas)',
+      step_id: 'UUID or null',
+      event_type: 'one of eventTypes',
+      service: 'service name',
+      payload: 'JSON object',
+      timestamp: 'ISO timestamp',
+    },
+  });
+
+  // Analyze saga patterns
+  const successfulSagas = sagas.data.filter((s: any) => s.overall_status === 'completed');
+  const failedSagas = sagas.data.filter((s: any) => s.overall_status === 'failed');
+  const compensatedSagas = sagas.data.filter((s: any) => s.overall_status === 'compensated');
+
+  console.log('Saga Pattern Analysis:');
+  console.log(`- Total sagas: ${sagas.data.length}`);
+  console.log(`- Successful: ${successfulSagas.length} (${((successfulSagas.length / sagas.data.length) * 100).toFixed(1)}%)`);
+  console.log(`- Failed: ${failedSagas.length}`);
+  console.log(`- Compensated: ${compensatedSagas.length}`);
+
+  // Calculate average steps and duration
+  const avgSteps = sagas.data.reduce((sum: number, s: any) => sum + s.steps.length, 0) / sagas.data.length;
+  const avgDuration = sagas.data
+    .filter((s: any) => s.completed_at)
+    .reduce((sum: number, s: any) => sum + s.total_duration_ms, 0) / successfulSagas.length;
+
+  console.log(`\nTransaction Metrics:`);
+  console.log(`- Average steps per saga: ${avgSteps.toFixed(1)}`);
+  console.log(`- Average duration: ${avgDuration.toFixed(0)}ms`);
+  console.log(`- Compensation rate: ${((compensatedSagas.length / sagas.data.length) * 100).toFixed(1)}%`);
+
+  // Orchestration vs Choreography
+  const orchestrated = sagas.data.filter((s: any) => s.orchestration_type === 'orchestration').length;
+  const choreographed = sagas.data.filter((s: any) => s.orchestration_type === 'choreography').length;
+
+  console.log(`\nOrchestration Style:`);
+  console.log(`- Orchestration: ${orchestrated}`);
+  console.log(`- Choreography: ${choreographed}`);
+
+  // Integration with coordination frameworks
+  console.log('\nIntegration Pattern:');
+  console.log('// MassTransit Saga State Machine');
+  console.log('class OrderSaga : MassTransitStateMachine<OrderState> { /* ... */ }');
+  console.log('// Or custom orchestrator with agentic-synth test data');
+
+  return { sagas, sagaEvents };
+}
+
+// ============================================================================
+// Example 6: Stream Processing Pipeline
+// ============================================================================
+
+/**
+ * Generate stream processing pipeline data (Kafka Streams, Flink)
+ */
+export async function streamProcessingPipeline() {
+  console.log('\n🌊 Example 6: Stream Processing Pipeline\n');
+
+  const synth = createSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY || 'demo-key',
+  });
+
+  // Generate pipeline topology
+  const pipeline = await synth.generateStructured({
+    count: 1,
+    schema: {
+      pipeline_id: 'UUID',
+      pipeline_name: 'descriptive name',
+      stages: [
+        {
+          stage_id: 'UUID',
+          stage_type: 'source | transform | aggregate | filter | sink',
+          stage_name: 'stage name',
+          parallelism: 'number (1-20)',
+          input_topics: ['array of topic names'],
+          output_topics: ['array of topic names'],
+          transformation: 'description of transformation',
+          windowing: {
+            type: 'tumbling | sliding | session | global',
+            size_ms: 'number (1000-300000)',
+            slide_ms: 'number or null',
+          },
+          state_store: 'store name or null',
+        },
+      ],
+    },
+  });
+
+  // Generate processing metrics
+  const metrics = await synth.generateTimeSeries({
+    count: 200,
+    interval: '30s',
+    metrics: [
+      'records_in',
+      'records_out',
+      'records_filtered',
+      'processing_latency_ms',
+      'watermark_lag_ms',
+      'cpu_usage',
+      'memory_mb',
+    ],
+    trend: 'mixed',
+  });
+
+  // Generate windowed aggregations
+  const aggregations = await synth.generateStructured({
+    count: 150,
+    schema: {
+      window_id: 'UUID',
+      stage_id: 'UUID (from pipeline)',
+      window_start: 'ISO timestamp',
+      window_end: 'ISO timestamp',
+      record_count: 'number (100-10000)',
+      aggregate_values: {
+        sum: 'number',
+        avg: 'number',
+        min: 'number',
+        max: 'number',
+        count: 'number',
+      },
+      emitted_at: 'ISO timestamp',
+      late_arrivals: 'number (0-100)',
+    },
+  });
+
+  console.log('Stream Processing Analysis:');
+  console.log(`- Pipeline stages: ${pipeline.data[0].stages.length}`);
+  console.log(`- Metric points: ${metrics.data.length}`);
+  console.log(`- Window aggregations: ${aggregations.data.length}`);
+
+  // Calculate throughput
+  const avgRecordsIn = metrics.data.reduce((sum: number, m: any) => sum + m.records_in, 0) / metrics.data.length;
+  const avgRecordsOut = metrics.data.reduce((sum: number, m: any) => sum + m.records_out, 0) / metrics.data.length;
+
+  console.log(`\nThroughput:`);
+  console.log(`- Input: ${avgRecordsIn.toFixed(0)} records/interval`);
+  console.log(`- Output: ${avgRecordsOut.toFixed(0)} records/interval`);
+  console.log(`- Filter rate: ${(((avgRecordsIn - avgRecordsOut) / avgRecordsIn) * 100).toFixed(1)}%`);
+
+  console.log('\nApache Flink Integration:');
+  console.log('// Define stream processing job');
+  console.log('env.addSource(kafkaSource).keyBy(...).window(...).aggregate(...).addSink(kafkaSink);');
+
+  return { pipeline, metrics, aggregations };
+}
+
+// ============================================================================
+// Run All Examples
+// ============================================================================
+
+export async function runAllDistributedProcessingExamples() {
+  console.log('🚀 Running All Distributed Processing Examples\n');
+  console.log('='.repeat(70));
+
+  try {
+    await mapReduceJobData();
+    console.log('='.repeat(70));
+
+    await workerPoolSimulation();
+    console.log('='.repeat(70));
+
+    await messageQueueScenarios();
+    console.log('='.repeat(70));
+
+    await eventDrivenArchitecture();
+    console.log('='.repeat(70));
+
+    await sagaPatternTransactions();
+    console.log('='.repeat(70));
+
+    await streamProcessingPipeline();
+    console.log('='.repeat(70));
+
+    console.log('\n✅ All distributed processing examples completed!\n');
+  } catch (error: any) {
+    console.error('❌ Error running examples:', error.message);
+    throw error;
+  }
+}
+
+// Run if executed directly
+if (import.meta.url === `file://${process.argv[1]}`) {
+  runAllDistributedProcessingExamples().catch(console.error);
+}