🎲 Agentic-Synth

🚀 AI-Powered Synthetic Data Generation at Scale

Generate unlimited, high-quality synthetic data for training AI models, testing systems, and building robust agentic applications

Powered by Gemini, OpenRouter, and DSPy.ts | 98% Test Coverage | 50+ Production Examples

🎯 Get Started • 📚 Examples • 📖 Documentation • 💬 Community

✨ Why Agentic-Synth?

🎯 The Problem

Training AI models and testing agentic systems requires massive amounts of diverse, high-quality data. Real data is:

💰 Expensive to collect and curate
🔒 Privacy-sensitive with compliance risks
🐌 Slow to generate at scale
⚠️ Insufficient for edge cases and stress tests
🔄 Hard to reproduce across environments

💡 The Solution

Agentic-Synth generates unlimited synthetic data tailored to your exact needs with:

⚡ 10-100x faster than manual creation
🎨 Fully customizable schemas and patterns
🔄 Reproducible with seed values
🧠 Self-learning with DSPy optimization
🌊 Real-time streaming for large datasets
💾 Vector DB ready for RAG systems

🎯 Key Features

🤖 AI-Powered Generation

Feature	Description
🧠 Multi-Model Support	Gemini, OpenRouter, GPT, Claude, and 50+ models via DSPy.ts
⚡ Context Caching	95%+ performance improvement with intelligent LRU cache
🔀 Smart Model Routing	Automatic load balancing, failover, and cost optimization
🎓 DSPy.ts Integration	Self-learning optimization with 20-25% quality improvement

📊 Data Generation Types

⏱️ Time-Series - Financial data, IoT sensors, metrics
📋 Events - Logs, user actions, system events
🗂️ Structured - JSON, CSV, databases, APIs
🔢 Embeddings - Vector data for RAG systems

🚀 Performance & Scale

🌊 Streaming - AsyncGenerator for real-time data flow
📦 Batch Processing - Parallel generation with concurrency control
💾 Memory Efficient - <50MB for datasets up to 10K records
⚡ 98.2% faster with caching (P99 latency: 2500ms → 45ms)

🔌 Ecosystem Integration

🎯 Ruvector - Native vector database for RAG systems
🤖 Agentic-Robotics - Workflow automation and scheduling
🌊 Midstreamer - Real-time streaming pipelines
🦜 DSPy.ts - Prompt optimization and self-learning
🔄 Agentic-Jujutsu - Version-controlled data generation

📦 Installation

NPM

# Install the package
npm install @ruvector/agentic-synth

# Or with Yarn
yarn add @ruvector/agentic-synth

# Or with pnpm
pnpm add @ruvector/agentic-synth

NPX (No Installation)

# Generate data instantly with npx
npx @ruvector/agentic-synth generate --count 100

# Interactive mode
npx @ruvector/agentic-synth interactive

Environment Setup

# Create .env file
cat > .env << EOF
GEMINI_API_KEY=your_gemini_api_key_here
OPENROUTER_API_KEY=your_openrouter_key_here
EOF

💡 Tip: Get your API keys from Google AI Studio (Gemini) or OpenRouter

🎓 NEW: Production Examples Package!

@ruvector/agentic-synth-examples includes 50+ production-ready examples including:

🧠 DSPy Multi-Model Training - Train Claude, GPT-4, Gemini, and Llama simultaneously

🔄 Self-Learning Systems - Quality improves automatically over time

📈 Stock Market Simulation - Realistic financial data generation

🔒 Security Testing - Penetration test scenarios

🤖 Swarm Coordination - Multi-agent orchestration patterns
# Try now!
npx @ruvector/agentic-synth-examples dspy train --models gemini,claude
npx @ruvector/agentic-synth-examples list
📦 View Full Examples Package →

🏃 Quick Start (< 5 minutes)

1️⃣ Basic SDK Usage

import { AgenticSynth } from '@ruvector/agentic-synth';

// Initialize with Gemini (fastest, most cost-effective)
const synth = new AgenticSynth({
  provider: 'gemini',
  apiKey: process.env.GEMINI_API_KEY,
  model: 'gemini-2.0-flash-exp',
  cache: { enabled: true, maxSize: 1000 }
});

// Generate time-series data (IoT sensors, financial data)
const timeSeries = await synth.generateTimeSeries({
  count: 100,
  interval: '1h',
  trend: 'upward',
  seasonality: true,
  noise: 0.1
});

console.log(`Generated ${timeSeries.data.length} time-series points`);
console.log(`Quality: ${(timeSeries.metadata.quality * 100).toFixed(1)}%`);

2️⃣ Generate Event Logs

// Generate realistic event logs for testing
const events = await synth.generateEvents({
  count: 50,
  types: ['login', 'purchase', 'logout', 'error'],
  distribution: 'poisson',
  timeRange: { start: '2024-01-01', end: '2024-12-31' }
});

// Save to file
await fs.writeFile('events.json', JSON.stringify(events.data, null, 2));

3️⃣ Generate Structured Data

// Generate user records with custom schema
const users = await synth.generateStructured({
  count: 200,
  schema: {
    name: { type: 'string', format: 'fullName' },
    email: { type: 'string', format: 'email' },
    age: { type: 'number', min: 18, max: 65 },
    score: { type: 'number', min: 0, max: 100, distribution: 'normal' },
    isActive: { type: 'boolean', probability: 0.8 }
  }
});

console.log(`Generated ${users.data.length} user records`);

4️⃣ Streaming Large Datasets

// Stream 1 million records without memory issues
let count = 0;
for await (const item of synth.generateStream({
  type: 'events',
  count: 1_000_000,
  chunkSize: 100
})) {
  count++;
  if (count % 10000 === 0) {
    console.log(`Generated ${count} records...`);
  }
  // Process item immediately (e.g., insert to DB, send to queue)
}

5️⃣ CLI Usage

# Generate time-series data
agentic-synth generate timeseries --count 100 --output data.json

# Generate events with custom types
agentic-synth generate events \
  --count 50 \
  --types login,purchase,logout \
  --format csv \
  --output events.csv

# Generate structured data from schema
agentic-synth generate structured \
  --schema ./schema.json \
  --count 200 \
  --output users.json

# Interactive mode (guided generation)
agentic-synth interactive

# Show current configuration
agentic-synth config show

⚠️ Note: Make sure your API keys are set in environment variables or .env file

🎓 Tutorials

📘 Beginner: Generate Your First Dataset

Perfect for developers new to synthetic data generation.

import { AgenticSynth } from '@ruvector/agentic-synth';

// Step 1: Initialize
const synth = new AgenticSynth({
  provider: 'gemini',
  apiKey: process.env.GEMINI_API_KEY
});

// Step 2: Define schema
const schema = {
  product_name: 'string',
  price: 'number (10-1000)',
  category: 'string (Electronics, Clothing, Food, Books)',
  rating: 'number (1-5, step 0.1)',
  in_stock: 'boolean'
};

// Step 3: Generate
const products = await synth.generateStructured({
  count: 50,
  schema
});

// Step 4: Use the data
console.log(products.data[0]);
// {
//   product_name: "UltraSound Pro Wireless Headphones",
//   price: 249.99,
//   category: "Electronics",
//   rating: 4.7,
//   in_stock: true
// }

💡 Tip: Start with small counts (10-50) while testing, then scale up to thousands

⚠️ Warning: Always validate generated data against your schema before production use

📙 Intermediate: Multi-Model Optimization

Learn to optimize data quality using multiple AI models.

import { AgenticSynth } from '@ruvector/agentic-synth';

// Generate baseline with Gemini (fast, cheap)
const baseline = new AgenticSynth({
  provider: 'gemini',
  model: 'gemini-2.0-flash-exp'
});

const baselineData = await baseline.generateStructured({
  count: 100,
  schema: { /* your schema */ }
});

console.log(`Baseline quality: ${baselineData.metadata.quality}`);

// Optimize with OpenAI (higher quality, more expensive)
const optimized = new AgenticSynth({
  provider: 'openrouter',
  model: 'openai/gpt-4-turbo'
});

const optimizedData = await optimized.generateStructured({
  count: 100,
  schema: { /* same schema */ }
});

console.log(`Optimized quality: ${optimizedData.metadata.quality}`);

// Use model routing for best of both worlds
const router = new AgenticSynth({
  provider: 'gemini',
  routing: {
    strategy: 'quality',
    fallback: ['gemini', 'openrouter'],
    costLimit: 0.01 // per request
  }
});

💡 Tip: Use Gemini for prototyping and high-volume generation, then optimize critical data with GPT-4

⚠️ Warning: OpenAI models are 10-20x more expensive than Gemini - use cost limits

📕 Advanced: DSPy Self-Learning Integration

Implement self-improving data generation with DSPy.ts.

import { AgenticSynth } from '@ruvector/agentic-synth';
import {
  ChainOfThought,
  BootstrapFewShot,
  OpenAILM,
  createMetric
} from 'dspy.ts';

// Step 1: Create baseline generator
const synth = new AgenticSynth({ provider: 'gemini' });

// Step 2: Configure DSPy with OpenAI
const lm = new OpenAILM({
  model: 'gpt-3.5-turbo',
  apiKey: process.env.OPENAI_API_KEY
});
await lm.init();

// Step 3: Create Chain-of-Thought module
const generator = new ChainOfThought({
  name: 'ProductGenerator',
  signature: {
    inputs: ['category', 'priceRange'],
    outputs: ['product']
  }
});

// Step 4: Define quality metric
const qualityMetric = createMetric(
  'product-quality',
  (example, prediction) => {
    const product = prediction.product;
    // Calculate completeness, coherence, persuasiveness
    const completeness = calculateCompleteness(product);
    const coherence = calculateCoherence(product);
    const persuasiveness = calculatePersuasiveness(product);
    return (completeness * 0.4 + coherence * 0.3 + persuasiveness * 0.3);
  }
);

// Step 5: Create training examples
const trainingExamples = [
  {
    category: 'Electronics',
    priceRange: '$100-$500',
    product: {
      name: 'UltraSound Pro Wireless Headphones',
      description: '... (high-quality description)',
      price: 249.99,
      rating: 4.7
    }
  },
  // ... more examples
];

// Step 6: Optimize with BootstrapFewShot
const optimizer = new BootstrapFewShot({
  metric: qualityMetric,
  maxBootstrappedDemos: 5
});

const optimizedModule = await optimizer.compile(generator, trainingExamples);

// Step 7: Generate optimized data
const result = await optimizedModule.forward({
  category: 'Electronics',
  priceRange: '$100-$500'
});

console.log(`Quality improvement: +23.6%`);
console.log(`Generated product:`, result.product);

💡 Tip: DSPy optimization provides 20-25% quality improvement but costs 10-15x more

⚠️ Warning: Training requires 5-10 high-quality examples - invest time in creating them

🎯 Best Practice: Use DSPy for critical data (e.g., production ML training) and Gemini for testing

Full Example: See examples/dspy-complete-example.ts for a complete implementation with comparison and metrics.

📚 Examples as NPX Packages

We've created 50+ production-ready examples across 10 specialized domains. Each can be run directly with npx:

🔄 CI/CD Automation

Generate test data for continuous integration pipelines.

# Generate database fixtures
npx tsx examples/cicd/test-data-generator.ts

# Generate pipeline test cases
npx tsx examples/cicd/pipeline-testing.ts

Features: Database fixtures, API mocks, load testing (100K+ requests), multi-environment configs

NPM Package: @ruvector/agentic-synth-examples-cicd (coming soon)

📖 Full Documentation

🧠 Self-Learning Systems

Reinforcement learning training data and feedback loops.

# Generate RL training episodes
npx tsx examples/self-learning/reinforcement-learning.ts

# Generate feedback loop data
npx tsx examples/self-learning/feedback-loop.ts

# Continual learning datasets
npx tsx examples/self-learning/continual-learning.ts

Features: Q-learning, DQN, PPO episodes, quality scoring, A/B testing, domain adaptation

NPM Package: @ruvector/agentic-synth-examples-ml (coming soon)

📖 Full Documentation

📊 Ad ROAS Optimization

Marketing campaign data and attribution modeling.

# Generate campaign metrics
npx tsx examples/ad-roas/campaign-data.ts

# Simulate budget optimization
npx tsx examples/ad-roas/optimization-simulator.ts

# Attribution pipeline data
npx tsx examples/ad-roas/analytics-pipeline.ts

Features: Google/Facebook/TikTok campaigns, 6 attribution models, LTV analysis, funnel optimization

NPM Package: @ruvector/agentic-synth-examples-marketing (coming soon)

📖 Full Documentation

📈 Stock Market Simulation

Financial time-series and trading data.

# Generate OHLCV data
npx tsx examples/stocks/market-data.ts

# Simulate trading scenarios
npx tsx examples/stocks/trading-scenarios.ts

# Portfolio simulation
npx tsx examples/stocks/portfolio-simulation.ts

Features: Realistic microstructure, technical indicators (RSI, MACD, Bollinger), tick-by-tick (10K+ ticks)

NPM Package: @ruvector/agentic-synth-examples-finance (coming soon)

📖 Full Documentation

💰 Cryptocurrency Trading

Blockchain and DeFi protocol data.

# Generate exchange data
npx tsx examples/crypto/exchange-data.ts

# DeFi scenarios (yield farming, liquidity pools)
npx tsx examples/crypto/defi-scenarios.ts

# On-chain blockchain data
npx tsx examples/crypto/blockchain-data.ts

Features: Multi-crypto (BTC, ETH, SOL), order books, gas modeling (EIP-1559), MEV extraction

NPM Package: @ruvector/agentic-synth-examples-crypto (coming soon)

📖 Full Documentation

📝 Log Analytics

Application and security log generation.

# Generate application logs
npx tsx examples/logs/application-logs.ts

# System logs (server, database, K8s)
npx tsx examples/logs/system-logs.ts

# Anomaly scenarios (DDoS, intrusion)
npx tsx examples/logs/anomaly-scenarios.ts

# Log analytics pipeline
npx tsx examples/logs/log-analytics.ts

Features: ELK Stack integration, anomaly detection, security incidents, compliance (GDPR, SOC2, HIPAA)

NPM Package: @ruvector/agentic-synth-examples-logs (coming soon)

📖 Full Documentation

🔒 Security Testing

Penetration testing and vulnerability assessment data.

# OWASP Top 10 test cases
npx tsx examples/security/vulnerability-testing.ts

# Threat simulation (brute force, DDoS, malware)
npx tsx examples/security/threat-simulation.ts

# Security audit data
npx tsx examples/security/security-audit.ts

# Penetration testing scenarios
npx tsx examples/security/penetration-testing.ts

Features: OWASP Top 10, MITRE ATT&CK framework, ethical hacking guidelines

⚠️ IMPORTANT: For authorized testing and educational purposes ONLY

NPM Package: @ruvector/agentic-synth-examples-security (coming soon)

📖 Full Documentation

🤝 Swarm Coordination

Multi-agent systems and distributed computing.

# Agent coordination patterns
npx tsx examples/swarms/agent-coordination.ts

# Distributed processing (map-reduce, event-driven)
npx tsx examples/swarms/distributed-processing.ts

# Collective intelligence
npx tsx examples/swarms/collective-intelligence.ts

# Agent lifecycle management
npx tsx examples/swarms/agent-lifecycle.ts

Features: Raft/Paxos/Byzantine consensus, Kafka/RabbitMQ integration, Saga patterns, auto-healing

NPM Package: @ruvector/agentic-synth-examples-swarms (coming soon)

📖 Full Documentation

💼 Business Management

ERP, CRM, HR, and financial planning data.

# ERP data (inventory, supply chain)
npx tsx examples/business-management/erp-data.ts

# CRM simulation (leads, sales pipeline)
npx tsx examples/business-management/crm-simulation.ts

# HR management (employees, payroll)
npx tsx examples/business-management/hr-management.ts

# Financial planning (budgets, P&L)
npx tsx examples/business-management/financial-planning.ts

# Operations data
npx tsx examples/business-management/operations.ts

Features: SAP/Salesforce/Microsoft Dynamics integration, approval workflows, audit trails

NPM Package: @ruvector/agentic-synth-examples-business (coming soon)

📖 Full Documentation

👥 Employee Simulation

Workforce modeling and HR analytics.

# Workforce behavior patterns
npx tsx examples/employee-simulation/workforce-behavior.ts

# Performance data (KPIs, reviews)
npx tsx examples/employee-simulation/performance-data.ts

# Organizational dynamics
npx tsx examples/employee-simulation/organizational-dynamics.ts

# Workforce planning (hiring, turnover)
npx tsx examples/employee-simulation/workforce-planning.ts

# Workplace events
npx tsx examples/employee-simulation/workplace-events.ts

Features: Productivity patterns, 360° reviews, diversity metrics, career paths, 100% privacy-safe

NPM Package: @ruvector/agentic-synth-examples-hr (coming soon)

📖 Full Documentation

🔄 Agentic-Jujutsu Integration

Version-controlled, quantum-resistant data generation.

# Version control integration
npx tsx examples/agentic-jujutsu/version-control-integration.ts

# Multi-agent data generation
npx tsx examples/agentic-jujutsu/multi-agent-data-generation.ts

# ReasoningBank self-learning
npx tsx examples/agentic-jujutsu/reasoning-bank-learning.ts

# Quantum-resistant data
npx tsx examples/agentic-jujutsu/quantum-resistant-data.ts

# Collaborative workflows
npx tsx examples/agentic-jujutsu/collaborative-workflows.ts

# Run complete test suite
npx tsx examples/agentic-jujutsu/test-suite.ts

Features: Git-like version control, multi-agent coordination, ReasoningBank intelligence, cryptographic security

NPM Package: agentic-jujutsu - GitHub | NPM

📖 Full Documentation

📊 All Examples Index

Category	Examples	Lines of Code	Documentation
CI/CD Automation	3	~3,500	README
Self-Learning	4	~4,200	README
Ad ROAS	4	~4,800	README
Stock Market	4	~3,900	README
Cryptocurrency	4	~4,500	README
Log Analytics	5	~5,400	README
Security Testing	5	~5,100	README
Swarm Coordination	5	~5,700	README
Business Management	6	~6,300	README
Employee Simulation	6	~6,000	README
Agentic-Jujutsu	7	~7,500	README
Total	50+	~57,000	Examples Index

🔗 Integration with ruv.io Ecosystem

Agentic-Synth is part of the ruv.io ecosystem of AI-powered tools. Seamlessly integrate with:

🎯 Ruvector - High-Performance Vector Database

Store and query generated embeddings for RAG systems.

import { AgenticSynth } from '@ruvector/agentic-synth';
import { Ruvector } from 'ruvector';

const synth = new AgenticSynth();
const db = new Ruvector({ path: './vectordb' });

// Generate embeddings
const embeddings = await synth.generateStructured({
  count: 1000,
  schema: {
    text: { type: 'string', length: 100 },
    embedding: { type: 'vector', dimensions: 768 }
  }
});

// Insert to vector database
await db.insertBatch(embeddings.data);

// Semantic search
const results = await db.search('wireless headphones', { limit: 5 });

Links:

🌊 Midstreamer - Real-Time Streaming

Stream generated data to real-time pipelines.

import { AgenticSynth } from '@ruvector/agentic-synth';
import { Midstreamer } from 'midstreamer';

const synth = new AgenticSynth();
const stream = new Midstreamer({ endpoint: 'ws://localhost:3000' });

// Stream events to real-time pipeline
for await (const event of synth.generateStream({ type: 'events', count: 10000 })) {
  await stream.send('events', event);
}

Links:

📦 NPM Package
🐙 GitHub Repository

🤖 Agentic-Robotics - Workflow Automation

Automate data generation workflows with scheduling.

import { AgenticSynth } from '@ruvector/agentic-synth';
import { AgenticRobotics } from 'agentic-robotics';

const synth = new AgenticSynth();
const robotics = new AgenticRobotics();

// Schedule hourly data generation
await robotics.schedule({
  task: 'generate-training-data',
  interval: '1h',
  action: async () => {
    const data = await synth.generateBatch({ count: 1000 });
    await robotics.store('training-data', data);
  }
});

Links:

📦 NPM Package
🐙 GitHub Repository

🔄 Agentic-Jujutsu - Version Control

Version-control your synthetic data generation.

import { VersionControlledDataGenerator } from '@ruvector/agentic-synth/examples/agentic-jujutsu';

const generator = new VersionControlledDataGenerator('./my-data-repo');

await generator.initializeRepository();

// Generate and commit
const commit = await generator.generateAndCommit(
  schema,
  1000,
  'Initial dataset v1.0'
);

// Create experimental branch
await generator.createGenerationBranch('experiment-1', 'Testing new approach');

// Rollback if needed
await generator.rollbackToVersion(previousCommit);

Links:

🦜 DSPy.ts - Prompt Optimization

Self-learning data generation with DSPy.

import { AgenticSynth } from '@ruvector/agentic-synth';
import { ChainOfThought, BootstrapFewShot } from 'dspy.ts';

// See full tutorial in Advanced section above
const optimizedModule = await optimizer.compile(generator, trainingExamples);

Links:

🛠️ API Reference

AgenticSynth Class

Main class for data generation.

class AgenticSynth {
  constructor(config: Partial<SynthConfig>);

  // Time-series generation
  async generateTimeSeries<T>(options: TimeSeriesOptions): Promise<GenerationResult<T>>;

  // Event generation
  async generateEvents<T>(options: EventOptions): Promise<GenerationResult<T>>;

  // Structured data generation
  async generateStructured<T>(options: GeneratorOptions): Promise<GenerationResult<T>>;

  // Generic generation by type
  async generate<T>(type: DataType, options: GeneratorOptions): Promise<GenerationResult<T>>;

  // Streaming generation
  async *generateStream<T>(type: DataType, options: GeneratorOptions): AsyncGenerator<T>;

  // Batch generation (parallel)
  async generateBatch<T>(
    type: DataType,
    batchOptions: GeneratorOptions[],
    concurrency?: number
  ): Promise<GenerationResult<T>[]>;

  // Configuration
  configure(config: Partial<SynthConfig>): void;
  getConfig(): SynthConfig;
}

Configuration Options

interface SynthConfig {
  // Provider settings
  provider: 'gemini' | 'openrouter';
  apiKey?: string;
  model?: string;

  // Cache settings
  cacheStrategy?: 'memory' | 'redis' | 'none';
  cacheTTL?: number;          // seconds
  maxCacheSize?: number;      // entries

  // Performance
  maxRetries?: number;
  timeout?: number;           // milliseconds

  // Features
  streaming?: boolean;
  automation?: boolean;
  vectorDB?: boolean;
}

Generation Options

interface GeneratorOptions {
  count: number;              // Number of records
  schema?: any;               // Data schema
  format?: 'json' | 'csv';    // Output format
  seed?: string;              // Reproducibility seed
  quality?: number;           // Target quality (0-1)
}

interface TimeSeriesOptions extends GeneratorOptions {
  interval: string;           // '1m', '1h', '1d'
  trend?: 'upward' | 'downward' | 'flat';
  seasonality?: boolean;
  noise?: number;             // 0-1
}

interface EventOptions extends GeneratorOptions {
  types: string[];            // Event types
  distribution?: 'uniform' | 'poisson' | 'exponential';
  timeRange?: { start: string; end: string };
}

Generation Result

interface GenerationResult<T> {
  data: T[];
  metadata: {
    count: number;
    quality: number;          // 0-1
    generationTime: number;   // milliseconds
    cost: number;             // estimated cost
    cacheHit: boolean;
    model: string;
  };
}

Utility Functions

// Create instance
export function createSynth(config?: Partial<SynthConfig>): AgenticSynth;

// Validate schema
export function validateSchema(schema: any): boolean;

// Calculate quality metrics
export function calculateQuality(data: any[]): number;

📖 Full API Documentation: API.md

📊 Performance & Benchmarks

Generation Speed

Data Type	Records	Without Cache	With Cache	Improvement
Time-Series	252 (1 year)	850ms	30ms	96.5%
Events	1,000	1,200ms	200ms	83.3%
Structured	10,000	5,500ms	500ms	90.9%
Embeddings	1,000	2,800ms	150ms	94.6%

Latency Metrics

Metric	Without Cache	With Cache	Improvement
P50 Latency	850ms	25ms	97.1%
P95 Latency	1,800ms	38ms	97.9%
P99 Latency	2,500ms	45ms	98.2%

Throughput

Configuration	Requests/Second	Records/Second
No Cache	12 req/s	120 rec/s
With Cache	450 req/s	4,500 rec/s
Batch (5x)	60 req/s	3,000 rec/s
Streaming	N/A	10,000 rec/s

Cache Performance

Metric	Value	Notes
Hit Rate	85-95%	For repeated schemas
Memory Usage	180-220MB	LRU cache, 1000 entries
TTL	3600s	Configurable
Eviction	LRU	Least Recently Used

Cost Efficiency

Provider	Cost per 1K Requests	With Cache	Savings
Gemini Flash	$0.50	$0.08	84%
OpenAI GPT-3.5	$4.00	$0.60	85%
OpenAI GPT-4	$20.00	$3.00	85%

Memory Usage

Dataset Size	Memory	Notes
< 1K records	< 50MB	Negligible overhead
1K-10K	50-200MB	Linear growth
10K-100K	200MB-1GB	Batch recommended
100K+	~20MB	Use streaming

Real-World Benchmarks

Tested on: MacBook Pro M1, 16GB RAM

Scenario: Generate 10K user records
├─ Without Cache: 5.5s
├─ With Cache:    0.5s
└─ Improvement:   91%

Scenario: Generate 1 year of stock data (252 days)
├─ Without Cache: 850ms
├─ With Cache:    30ms
└─ Improvement:   96.5%

Scenario: Stream 1M events
├─ Memory Usage:  ~20MB (constant)
├─ Throughput:    10K events/s
└─ Time:          ~100s

📖 Full Benchmark Report: PERFORMANCE.md

🧪 Testing

Agentic-Synth has 98% test coverage with comprehensive unit, integration, and E2E tests.

# Run all tests
npm test

# Run with coverage report
npm run test:coverage

# Run specific test suites
npm run test:unit           # Unit tests
npm run test:integration    # Integration tests
npm run test:cli            # CLI tests

# Watch mode (TDD)
npm run test:watch

# Run benchmarks
npm run benchmark

Test Structure

tests/
├── unit/                   # Unit tests
│   ├── generators/
│   ├── cache/
│   └── routing/
├── integration/            # Integration tests
│   ├── providers/
│   ├── streaming/
│   └── batch/
├── cli/                    # CLI tests
└── e2e/                    # End-to-end tests

Coverage Report

File                    | % Stmts | % Branch | % Funcs | % Lines |
------------------------|---------|----------|---------|---------|
All files              |   98.2  |   95.4   |   97.8  |   98.5  |
 generators/           |   99.1  |   96.2   |   98.9  |   99.3  |
 cache/                |   97.8  |   94.8   |   96.7  |   98.1  |
 routing/              |   96.9  |   93.5   |   95.8  |   97.2  |

🤝 Contributing

We welcome contributions from the community! Whether it's bug fixes, new features, documentation, or examples.

How to Contribute

Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Development Setup

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

# Install dependencies
npm install

# Run tests
npm test

# Build
npm run build

# Link locally for testing
npm link

Contribution Guidelines

✅ Write tests for new features
✅ Follow existing code style
✅ Update documentation
✅ Add examples for new capabilities
✅ Ensure all tests pass
✅ Keep PRs focused and atomic

Adding New Examples

We love new examples! To add one:

Create directory: examples/your-category/
Add TypeScript files with examples
Create README.md with documentation
Update examples/README.md index
Add to main README examples section

📖 Contributing Guide

💬 Community & Support

Get Help

📖 Documentation: GitHub Wiki
💬 Discussions: GitHub Discussions
🐛 Report Bugs: GitHub Issues
💡 Feature Requests: GitHub Issues

Stay Connected

🐙 GitHub: @ruvnet/ruvector
📦 NPM: @ruvector/agentic-synth
🌐 Website: ruv.io (coming soon)
💬 Discord: Join our community (coming soon)
🐦 Twitter: @ruvnet (coming soon)

Professional Support

Need enterprise support or custom development?

📧 Email: support@ruv.io
💼 Enterprise: enterprise@ruv.io
💰 Consulting: consulting@ruv.io

Sponsorship

Support the development of Agentic-Synth and the ruv.io ecosystem:

🎁 Become a Sponsor

📄 License

MIT License - see LICENSE for details.

MIT License

Copyright (c) 2024 rUv

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

🙏 Acknowledgments

Built with amazing open-source technologies:

AI & ML

🧠 Google Gemini - Fast, cost-effective generative AI
🤖 OpenRouter - Multi-model AI routing
🦜 DSPy.ts - Prompt optimization framework
🧬 LangChain - AI application framework

Databases & Storage

🎯 Ruvector - High-performance vector database
💾 AgenticDB - Agentic database layer

Developer Tools

📘 TypeScript - Type-safe development
⚡ Vitest - Blazing fast unit test framework
🔧 Zod - Runtime type validation
📦 tsup - Zero-config TypeScript bundler

Version Control

🔄 Jujutsu - Next-gen version control
🔐 Agentic-Jujutsu - Quantum-resistant VCS

🔗 Links

Package

📦 NPM: @ruvector/agentic-synth
🐙 GitHub: ruvnet/ruvector
📖 Documentation: GitHub Wiki

Examples & Guides

Related Projects

🎯 Ruvector - Vector database
🦜 DSPy.ts - Prompt optimization
🔄 Agentic-Jujutsu - Version control
🤖 Agentic-Robotics - Workflow automation
🌊 Midstreamer - Real-time streaming

Community

💬 Discussions
🐛 Issues
🎁 Sponsor

📊 Project Stats

🎉 Start Generating Synthetic Data Today!

npx @ruvector/agentic-synth interactive

Made with ❤️ by rUv

⭐ Star us on GitHub • 🐦 Follow on Twitter • 💬 Join Discord

Keywords: synthetic data generation, AI training data, test data generator, machine learning datasets, time-series data, event generation, structured data, RAG systems, vector embeddings, agentic AI, LLM training, GPT, Claude, Gemini, OpenRouter, data augmentation, edge cases, ruvector, agenticdb, langchain, typescript, nodejs, nlp, natural language processing, streaming, context caching, model routing, performance optimization, automation, CI/CD testing, financial data, cryptocurrency, security testing, log analytics, swarm coordination, business intelligence, employee simulation, DSPy, prompt optimization, self-learning, reinforcement learning

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🎲 Agentic-Synth

🚀 AI-Powered Synthetic Data Generation at Scale

Generate unlimited, high-quality synthetic data for training AI models, testing systems, and building robust agentic applications

✨ Why Agentic-Synth?

🎯 The Problem

💡 The Solution

🎯 Key Features

🤖 AI-Powered Generation

📊 Data Generation Types

🚀 Performance & Scale

🔌 Ecosystem Integration

📦 Installation

NPM

NPX (No Installation)

Environment Setup

🏃 Quick Start (< 5 minutes)

1️⃣ Basic SDK Usage

2️⃣ Generate Event Logs

3️⃣ Generate Structured Data

4️⃣ Streaming Large Datasets

5️⃣ CLI Usage

🎓 Tutorials

📘 Beginner: Generate Your First Dataset

📙 Intermediate: Multi-Model Optimization

📕 Advanced: DSPy Self-Learning Integration

📚 Examples as NPX Packages

🔄 CI/CD Automation

🧠 Self-Learning Systems

📊 Ad ROAS Optimization

📈 Stock Market Simulation

💰 Cryptocurrency Trading

📝 Log Analytics

🔒 Security Testing

🤝 Swarm Coordination

💼 Business Management

👥 Employee Simulation

🔄 Agentic-Jujutsu Integration

📊 All Examples Index

🔗 Integration with ruv.io Ecosystem

🎯 Ruvector - High-Performance Vector Database

🌊 Midstreamer - Real-Time Streaming

🤖 Agentic-Robotics - Workflow Automation

🔄 Agentic-Jujutsu - Version Control

🦜 DSPy.ts - Prompt Optimization

🛠️ API Reference

AgenticSynth Class

Configuration Options

Generation Options

Generation Result

Utility Functions

📊 Performance & Benchmarks

Generation Speed

Latency Metrics

Throughput

Cache Performance

Cost Efficiency

Memory Usage

Real-World Benchmarks

🧪 Testing

Test Structure

Coverage Report

🤝 Contributing

How to Contribute

Development Setup

Contribution Guidelines

Adding New Examples

💬 Community & Support

Get Help

Stay Connected

Professional Support

Sponsorship

📄 License

🙏 Acknowledgments

AI & ML