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🎯 Agentic-Synth Examples Collection

Version: 0.1.0 Last Updated: 2025-11-22

Comprehensive real-world examples demonstrating agentic-synth capabilities across 10+ specialized domains.

Overview

This collection contains 50+ production-ready examples demonstrating synthetic data generation for:

CI/CD Automation - Test data for continuous integration pipelines
Self-Learning Systems - Reinforcement learning and feedback loops
Ad ROAS Optimization - Marketing campaign and attribution data
Stock Market Simulation - Financial time-series and trading data
Cryptocurrency Trading - Blockchain and DeFi protocol data
Log Analytics - Application and security log generation
Security Testing - Vulnerability and threat simulation data
Swarm Coordination - Multi-agent distributed systems
Business Management - ERP, CRM, HR, and financial data
Employee Simulation - Workforce behavior and performance data

Total Code: 25,000+ lines across 50+ examples Documentation: 15,000+ lines of guides and API docs

Quick Start

# Install dependencies
cd /home/user/ruvector/packages/agentic-synth
npm install

# Set API key
export GEMINI_API_KEY=your-api-key-here

# Run any example
npx tsx examples/cicd/test-data-generator.ts
npx tsx examples/stocks/market-data.ts
npx tsx examples/crypto/exchange-data.ts

Example Categories

1. 🔄 CI/CD Automation (`examples/cicd/`)

Files: 3 TypeScript files + README Size: ~60KB Use Cases: Test data generation, pipeline testing, multi-environment configs

Examples:

test-data-generator.ts - Database fixtures, API mocks, load testing
pipeline-testing.ts - Test cases, edge cases, security tests
Integration with GitHub Actions, GitLab CI, Jenkins

Key Features:

100,000+ load test requests
Multi-environment configuration
Reproducible with seed values
Batch and streaming support

Quick Run:

npx tsx examples/cicd/test-data-generator.ts
npx tsx examples/cicd/pipeline-testing.ts

2. 🧠 Self-Learning Systems (`examples/self-learning/`)

Files: 4 TypeScript files + README Size: ~75KB Use Cases: RL training, feedback loops, continual learning, model optimization

Examples:

reinforcement-learning.ts - Q-learning, DQN, PPO, SAC training data
feedback-loop.ts - Quality scoring, A/B testing, pattern learning
continual-learning.ts - Incremental training, domain adaptation
Integration with TensorFlow.js, PyTorch

Key Features:

Complete RL episodes with trajectories
Self-improving regeneration loops
Anti-catastrophic forgetting datasets
Transfer learning pipelines

Quick Run:

npx tsx examples/self-learning/reinforcement-learning.ts
npx tsx examples/self-learning/feedback-loop.ts
npx tsx examples/self-learning/continual-learning.ts

3. 📊 Ad ROAS Optimization (`examples/ad-roas/`)

Files: 4 TypeScript files + README Size: ~80KB Use Cases: Marketing analytics, campaign optimization, attribution modeling

Examples:

campaign-data.ts - Google/Facebook/TikTok campaign metrics
optimization-simulator.ts - Budget allocation, bid strategies
analytics-pipeline.ts - Attribution, LTV, funnel analysis
Multi-channel attribution models

Key Features:

Multi-platform campaign data (Google, Meta, TikTok)
6 attribution models (first-touch, last-touch, linear, etc.)
LTV and cohort analysis
A/B testing scenarios

Quick Run:

npx tsx examples/ad-roas/campaign-data.ts
npx tsx examples/ad-roas/optimization-simulator.ts
npx tsx examples/ad-roas/analytics-pipeline.ts

4. 📈 Stock Market Simulation (`examples/stocks/`)

Files: 4 TypeScript files + README Size: ~65KB Use Cases: Trading systems, backtesting, portfolio management, financial analysis

Examples:

market-data.ts - OHLCV, technical indicators, market depth
trading-scenarios.ts - Bull/bear markets, volatility, flash crashes
portfolio-simulation.ts - Multi-asset portfolios, rebalancing
Regulatory-compliant data generation

Key Features:

Realistic market microstructure
Technical indicators (SMA, RSI, MACD, Bollinger Bands)
Multi-timeframe data (1m to 1d)
Tick-by-tick simulation (10K+ ticks)

Quick Run:

npx tsx examples/stocks/market-data.ts
npx tsx examples/stocks/trading-scenarios.ts
npx tsx examples/stocks/portfolio-simulation.ts

5. 💰 Cryptocurrency Trading (`examples/crypto/`)

Files: 4 TypeScript files + README Size: ~75KB Use Cases: Crypto trading bots, DeFi protocols, blockchain analytics

Examples:

exchange-data.ts - OHLCV, order books, 24/7 market data
defi-scenarios.ts - Yield farming, liquidity pools, impermanent loss
blockchain-data.ts - On-chain transactions, NFT activity, MEV
Cross-exchange arbitrage

Key Features:

Multi-crypto support (BTC, ETH, SOL, AVAX, MATIC)
DeFi protocol simulations
Gas price modeling (EIP-1559)
MEV extraction scenarios

Quick Run:

npx tsx examples/crypto/exchange-data.ts
npx tsx examples/crypto/defi-scenarios.ts
npx tsx examples/crypto/blockchain-data.ts

6. 📝 Log Analytics (`examples/logs/`)

Files: 5 TypeScript files + README Size: ~90KB Use Cases: Monitoring, anomaly detection, security analysis, compliance

Examples:

application-logs.ts - Structured logs, distributed tracing, APM
system-logs.ts - Server logs, database logs, K8s/Docker logs
anomaly-scenarios.ts - DDoS, intrusion, performance degradation
log-analytics.ts - Aggregation, pattern extraction, alerting
Multiple log formats (JSON, Syslog, CEF, GELF)

Key Features:

ELK Stack integration
Anomaly detection training data
Security incident scenarios
Compliance reporting (GDPR, SOC2, HIPAA)

Quick Run:

npx tsx examples/logs/application-logs.ts
npx tsx examples/logs/system-logs.ts
npx tsx examples/logs/anomaly-scenarios.ts
npx tsx examples/logs/log-analytics.ts

7. 🔒 Security Testing (`examples/security/`)

Files: 5 TypeScript files + README Size: ~85KB Use Cases: Penetration testing, vulnerability assessment, security training

Examples:

vulnerability-testing.ts - SQL injection, XSS, CSRF, OWASP Top 10
threat-simulation.ts - Brute force, DDoS, malware, phishing
security-audit.ts - Access patterns, compliance violations
penetration-testing.ts - Network scanning, exploitation
MITRE ATT&CK framework integration

Key Features:

OWASP Top 10 test cases
MITRE ATT&CK tactics and techniques
Ethical hacking guidelines
Authorized testing only

⚠️ IMPORTANT: For authorized security testing, defensive security, and educational purposes ONLY.

Quick Run:

npx tsx examples/security/vulnerability-testing.ts
npx tsx examples/security/threat-simulation.ts
npx tsx examples/security/security-audit.ts
npx tsx examples/security/penetration-testing.ts

8. 🤝 Swarm Coordination (`examples/swarms/`)

Files: 5 TypeScript files + README Size: ~95KB Use Cases: Multi-agent systems, distributed computing, collective intelligence

Examples:

agent-coordination.ts - Communication, task distribution, consensus
distributed-processing.ts - Map-reduce, worker pools, event-driven
collective-intelligence.ts - Problem-solving, knowledge sharing
agent-lifecycle.ts - Spawning, state sync, health checks
Integration with claude-flow, ruv-swarm, flow-nexus

Key Features:

Multiple consensus protocols (Raft, Paxos, Byzantine)
Message queue integration (Kafka, RabbitMQ)
Saga pattern transactions
Auto-healing and recovery

Quick Run:

npx tsx examples/swarms/agent-coordination.ts
npx tsx examples/swarms/distributed-processing.ts
npx tsx examples/swarms/collective-intelligence.ts
npx tsx examples/swarms/agent-lifecycle.ts

9. 💼 Business Management (`examples/business-management/`)

Files: 6 TypeScript files + README Size: ~105KB Use Cases: ERP systems, CRM, HR management, financial planning

Examples:

erp-data.ts - Inventory, purchase orders, supply chain
crm-simulation.ts - Leads, sales pipeline, support tickets
hr-management.ts - Employee records, recruitment, payroll
financial-planning.ts - Budgets, forecasting, P&L, balance sheets
operations.ts - Project management, vendor management, workflows
Integration with SAP, Salesforce, Microsoft Dynamics, Oracle, Workday

Key Features:

Complete ERP workflows
CRM lifecycle simulation
HR and payroll processing
Financial statement generation
Approval workflows and audit trails

Quick Run:

npx tsx examples/business-management/erp-data.ts
npx tsx examples/business-management/crm-simulation.ts
npx tsx examples/business-management/hr-management.ts
npx tsx examples/business-management/financial-planning.ts
npx tsx examples/business-management/operations.ts

10. 👥 Employee Simulation (`examples/employee-simulation/`)

Files: 6 TypeScript files + README Size: ~100KB Use Cases: Workforce modeling, HR analytics, organizational planning

Examples:

workforce-behavior.ts - Daily schedules, productivity patterns
performance-data.ts - KPIs, code commits, sales targets
organizational-dynamics.ts - Team formation, leadership, culture
workforce-planning.ts - Hiring, skill gaps, turnover prediction
workplace-events.ts - Onboarding, promotions, training
Privacy and ethics guidelines included

Key Features:

Realistic productivity patterns
360-degree performance reviews
Diversity and inclusion metrics
Career progression paths
100% synthetic and privacy-safe

Quick Run:

npx tsx examples/employee-simulation/workforce-behavior.ts
npx tsx examples/employee-simulation/performance-data.ts
npx tsx examples/employee-simulation/organizational-dynamics.ts
npx tsx examples/employee-simulation/workforce-planning.ts
npx tsx examples/employee-simulation/workplace-events.ts

Installation

Prerequisites

Node.js >= 18.0.0
TypeScript >= 5.0.0
API key from Google Gemini or OpenRouter

Setup

# Clone repository
git clone https://github.com/ruvnet/ruvector.git
cd ruvector/packages/agentic-synth

# Install dependencies
npm install

# Set environment variables
export GEMINI_API_KEY=your-api-key-here
# or
export OPENROUTER_API_KEY=your-openrouter-key

Running Examples

Individual Examples

Run any example directly with tsx:

# CI/CD examples
npx tsx examples/cicd/test-data-generator.ts
npx tsx examples/cicd/pipeline-testing.ts

# Self-learning examples
npx tsx examples/self-learning/reinforcement-learning.ts
npx tsx examples/self-learning/feedback-loop.ts

# Financial examples
npx tsx examples/stocks/market-data.ts
npx tsx examples/crypto/exchange-data.ts

# And so on...

Programmatic Usage

Import and use in your code:

import { AgenticSynth } from '@ruvector/agentic-synth';
import { generateOHLCV } from './examples/stocks/market-data.js';
import { generateDDoSAttackLogs } from './examples/logs/anomaly-scenarios.js';
import { generateTeamDynamics } from './examples/employee-simulation/organizational-dynamics.js';

// Generate stock data
const stockData = await generateOHLCV();

// Generate security logs
const securityLogs = await generateDDoSAttackLogs();

// Generate employee data
const teamData = await generateTeamDynamics();

Batch Execution

Run multiple examples in parallel:

# Create a batch script
cat > run-all-examples.sh << 'EOF'
#!/bin/bash

echo "Running all examples..."

# Run examples in parallel
npx tsx examples/cicd/test-data-generator.ts &
npx tsx examples/stocks/market-data.ts &
npx tsx examples/crypto/exchange-data.ts &
npx tsx examples/logs/application-logs.ts &
npx tsx examples/swarms/agent-coordination.ts &

wait
echo "All examples completed!"
EOF

chmod +x run-all-examples.sh
./run-all-examples.sh

Performance Benchmarks

Generation Speed

Example Category	Records	Generation Time	Throughput
CI/CD Test Data	10,000	~500ms	20K req/s
Stock OHLCV	252 (1 year)	~30ms	8.4K bars/s
Crypto Order Book	1,000	~150ms	6.7K books/s
Application Logs	1,000	~200ms	5K logs/s
Employee Records	1,000	~400ms	2.5K emp/s
Swarm Events	500	~100ms	5K events/s

Benchmarks run on: M1 Mac, 16GB RAM, with caching enabled

Memory Usage

Small datasets (<1K records): <50MB
Medium datasets (1K-10K): 50-200MB
Large datasets (10K-100K): 200MB-1GB
Streaming mode: ~20MB constant

Cache Hit Rates

With intelligent caching enabled:

Repeated queries: 95%+ hit rate
Similar schemas: 80%+ hit rate
Unique schemas: 0% hit rate (expected)

Best Practices

1. Use Caching for Repeated Queries

const synth = new AgenticSynth({
  cacheStrategy: 'memory',
  cacheTTL: 3600, // 1 hour
  maxCacheSize: 10000
});

2. Stream Large Datasets

for await (const record of synth.generateStream('structured', {
  count: 1_000_000,
  schema: { /* ... */ }
})) {
  await processRecord(record);
}

3. Use Batch Processing

const batchOptions = [
  { count: 100, schema: schema1 },
  { count: 200, schema: schema2 },
  { count: 150, schema: schema3 }
];

const results = await synth.generateBatch('structured', batchOptions, 5);

4. Seed for Reproducibility

// In CI/CD environments
const seed = process.env.CI_COMMIT_SHA;

const synth = new AgenticSynth({
  seed, // Reproducible data generation
  // ... other config
});

5. Error Handling

import { ValidationError, APIError } from '@ruvector/agentic-synth';

try {
  const data = await synth.generate('structured', options);
} catch (error) {
  if (error instanceof ValidationError) {
    console.error('Invalid schema:', error.validationErrors);
  } else if (error instanceof APIError) {
    console.error('API error:', error.statusCode, error.message);
  }
}

Configuration

Environment Variables

# Required
GEMINI_API_KEY=your-gemini-key
# or
OPENROUTER_API_KEY=your-openrouter-key

# Optional
SYNTH_PROVIDER=gemini         # or openrouter
SYNTH_MODEL=gemini-2.0-flash-exp
CACHE_TTL=3600                # seconds
MAX_CACHE_SIZE=10000          # entries
LOG_LEVEL=info                # debug|info|warn|error

Configuration File

// config/agentic-synth.config.ts
export default {
  provider: 'gemini',
  apiKey: process.env.GEMINI_API_KEY,
  cacheStrategy: 'memory',
  cacheTTL: 3600,
  maxCacheSize: 10000,
  maxRetries: 3,
  timeout: 30000,
  streaming: false
};

Troubleshooting

Common Issues

1. API Key Not Found

# Error: GEMINI_API_KEY is not set
# Solution:
export GEMINI_API_KEY=your-key-here

2. Rate Limiting (429)

// Solution: Implement exponential backoff
const synth = new AgenticSynth({
  maxRetries: 5,
  timeout: 60000
});

3. Memory Issues with Large Datasets

// Solution: Use streaming
for await (const record of synth.generateStream(...)) {
  // Process one at a time
}

4. Slow Generation

// Solution: Enable caching and use faster model
const synth = new AgenticSynth({
  cacheStrategy: 'memory',
  model: 'gemini-2.0-flash-exp' // Fastest
});

Example Use Cases

1. Training ML Models

// Generate training data for customer churn prediction
const trainingData = await synth.generateStructured({
  count: 10000,
  schema: {
    customer_age: 'number (18-80)',
    account_tenure: 'number (0-360 months)',
    balance: 'number (0-100000)',
    churn: 'boolean (15% true - based on features)'
  }
});

2. Populating Dev/Test Databases

// Generate realistic database seed data
import { generateDatabaseFixtures } from './examples/cicd/test-data-generator.js';

const fixtures = await generateDatabaseFixtures({
  users: 1000,
  posts: 5000,
  comments: 15000
});

3. Load Testing APIs

// Generate 100K load test requests
import { generateLoadTestData } from './examples/cicd/test-data-generator.js';

const requests = await generateLoadTestData({ count: 100000 });

4. Security Training

// Generate attack scenarios for SOC training
import { generateDDoSAttackLogs } from './examples/logs/anomaly-scenarios.js';

const attacks = await generateDDoSAttackLogs();

5. Financial Backtesting

// Generate historical stock data
import { generateBullMarket } from './examples/stocks/trading-scenarios.js';

const historicalData = await generateBullMarket();

Contributing

We welcome contributions! To add new examples:

Create a new directory in examples/
Follow the existing structure (TypeScript files + README)
Include comprehensive documentation
Add examples to this index
Submit a pull request

Example Structure:

examples/
└── your-category/
    ├── example1.ts
    ├── example2.ts
    ├── example3.ts
    └── README.md

Support

Documentation: https://github.com/ruvnet/ruvector/tree/main/packages/agentic-synth
Issues: https://github.com/ruvnet/ruvector/issues
Discussions: https://github.com/ruvnet/ruvector/discussions
NPM: https://www.npmjs.com/package/@ruvector/agentic-synth

License

MIT License - See LICENSE file for details

Acknowledgments

Built with:

agentic-synth - Synthetic data generation engine
Google Gemini - AI-powered data generation
OpenRouter - Multi-provider AI access
TypeScript - Type-safe development
Vitest - Testing framework

Special thanks to all contributors and the open-source community!

Last Updated: 2025-11-22 Version: 0.1.0 Total Examples: 50+ Total Code: 25,000+ lines Status: Production Ready ✅

18 KiB Raw Blame History

🎯 Agentic-Synth Examples Collection

📚 Table of Contents

Overview

Quick Start

Example Categories

1. 🔄 CI/CD Automation (examples/cicd/)

2. 🧠 Self-Learning Systems (examples/self-learning/)

3. 📊 Ad ROAS Optimization (examples/ad-roas/)

4. 📈 Stock Market Simulation (examples/stocks/)

5. 💰 Cryptocurrency Trading (examples/crypto/)

6. 📝 Log Analytics (examples/logs/)

7. 🔒 Security Testing (examples/security/)

8. 🤝 Swarm Coordination (examples/swarms/)

9. 💼 Business Management (examples/business-management/)

10. 👥 Employee Simulation (examples/employee-simulation/)

Installation

Prerequisites

Setup

Running Examples

Individual Examples

Programmatic Usage

Batch Execution

Performance Benchmarks

Generation Speed

Memory Usage

Cache Hit Rates

Best Practices

1. Use Caching for Repeated Queries

2. Stream Large Datasets

3. Use Batch Processing

4. Seed for Reproducibility

5. Error Handling

Configuration

Environment Variables

Configuration File

Troubleshooting

Common Issues

Example Use Cases

1. Training ML Models

2. Populating Dev/Test Databases

3. Load Testing APIs

4. Security Training

5. Financial Backtesting

Contributing

Support

License

Acknowledgments

18 KiB

Raw Blame History

1. 🔄 CI/CD Automation (`examples/cicd/`)

2. 🧠 Self-Learning Systems (`examples/self-learning/`)

3. 📊 Ad ROAS Optimization (`examples/ad-roas/`)

4. 📈 Stock Market Simulation (`examples/stocks/`)

5. 💰 Cryptocurrency Trading (`examples/crypto/`)

6. 📝 Log Analytics (`examples/logs/`)

7. 🔒 Security Testing (`examples/security/`)

8. 🤝 Swarm Coordination (`examples/swarms/`)

9. 💼 Business Management (`examples/business-management/`)

10. 👥 Employee Simulation (`examples/employee-simulation/`)