@ruvector/agentic-synth-examples

Production-ready examples and tutorials for @ruvector/agentic-synth


Complete, working examples showcasing advanced features of agentic-synth including DSPy.ts integration, multi-model training, self-learning systems, and production patterns.

---

🚀 Quick Start

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``bash


Install the examples package

npm install -g @ruvector/agentic-synth-examples

Or run directly with npx

npx @ruvector/agentic-synth-examples --help
`

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`bash


DSPy multi-model training

npx @ruvector/agentic-synth-examples dspy train \
--models gemini,claude \
--prompt "Generate product descriptions" \
--rounds 3

Basic synthetic data generation

npx @ruvector/agentic-synth-examples generate \
--type structured \
--count 100 \
--schema ./schema.json
`

---

📚 What's Included

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Advanced multi-model training with automatic optimization

- DSPy Learning Sessions - Self-improving AI training loops
- Multi-Model Benchmarking - Compare Claude, GPT-4, Gemini, Llama
- Prompt Optimization - BootstrapFewShot and MIPROv2 algorithms
- Quality Tracking - Real-time metrics and convergence detection
- Cost Management - Budget tracking and optimization

Run it:
`bash
npx @ruvector/agentic-synth-examples dspy train \
--models gemini,claude,gpt4 \
--optimization-rounds 5 \
--convergence 0.95
`

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Systems that improve over time through feedback loops

- Adaptive Generation - Quality improves with each iteration
- Pattern Recognition - Learns from successful outputs
- Cross-Model Learning - Best practices shared across models
- Performance Monitoring - Track improvement over time

Run it:
`bash
npx @ruvector/agentic-synth-examples self-learn \
--task "code-generation" \
--iterations 10 \
--learning-rate 0.1
`

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Real-world integration examples

- CI/CD Integration - Automated testing data generation
- Ad ROAS Optimization - Marketing campaign simulation
- Stock Market Simulation - Financial data generation
- Log Analytics - Security and monitoring data
- Employee Performance - HR and business simulations

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Semantic search and embeddings

- Ruvector Integration - Vector similarity search
- AgenticDB Integration - Agent memory and context
- Embedding Generation - Automatic vectorization
- Similarity Matching - Find related data

---

🎯 Featured Examples

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Train multiple AI models concurrently and find the best performer:

`typescript
import { DSPyTrainingSession, ModelProvider } from '@ruvector/agentic-synth-examples/dspy';

const session = new DSPyTrainingSession({
models: [
{ provider: ModelProvider.GEMINI, model: 'gemini-2.0-flash-exp', apiKey: process.env.GEMINI_API_KEY },
{ provider: ModelProvider.CLAUDE, model: 'claude-sonnet-4', apiKey: process.env.CLAUDE_API_KEY },
{ provider: ModelProvider.GPT4, model: 'gpt-4-turbo', apiKey: process.env.OPENAI_API_KEY }
],
optimizationRounds: 5,
convergenceThreshold: 0.95
});

// Event-driven progress tracking
session.on('iteration', (result) => {
console.log(Model: ${result.modelProvider}, Quality: ${result.quality.score});
});

session.on('complete', (report) => {
console.log(Best model: ${report.bestModel});
console.log(Quality improvement: ${report.qualityImprovement}%);
});

// Start training
await session.run('Generate realistic customer reviews', signature);
`

Output:
`
✓ Training started with 3 models
Iteration 1: Gemini 0.72, Claude 0.68, GPT-4 0.75
Iteration 2: Gemini 0.79, Claude 0.76, GPT-4 0.81
Iteration 3: Gemini 0.85, Claude 0.82, GPT-4 0.88
Iteration 4: Gemini 0.91, Claude 0.88, GPT-4 0.94
Iteration 5: Gemini 0.94, Claude 0.92, GPT-4 0.96

✓ Training complete!
Best model: GPT-4 (0.96 quality)
Quality improvement: 28%
Total cost: $0.23
Duration: 3.2 minutes
`

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Generate code that improves based on test results:

`typescript
import { SelfLearningGenerator } from '@ruvector/agentic-synth-examples';

const generator = new SelfLearningGenerator({
task: 'code-generation',
learningRate: 0.1,
iterations: 10
});

generator.on('improvement', (metrics) => {
console.log(Quality: ${metrics.quality}, Tests Passing: ${metrics.testsPassingRate});
});

const result = await generator.generate({
prompt: 'Create a TypeScript function to validate email addresses',
tests: emailValidationTests
});

console.log(Final quality: ${result.finalQuality});
console.log(Improvement: ${result.improvement}%);
`

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Generate realistic financial data for backtesting:

`typescript
import { StockMarketSimulator } from '@ruvector/agentic-synth-examples';

const simulator = new StockMarketSimulator({
symbols: ['AAPL', 'GOOGL', 'MSFT'],
startDate: '2024-01-01',
endDate: '2024-12-31',
volatility: 'medium'
});

const data = await simulator.generate({
includeNews: true,
includeSentiment: true,
marketConditions: 'bullish'
});

// Output includes OHLCV data, news events, sentiment scores
console.log(Generated ${data.length} trading days);
`

---

📖 Complete Example List

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#### 🧠 Machine Learning & AI
1. dspy-training - Multi-model DSPy training with optimization
2. self-learning - Adaptive systems that improve over time
3. prompt-engineering - Automatic prompt optimization
4. quality-tracking - Real-time quality metrics and monitoring
5. model-benchmarking - Compare different AI models

#### 💼 Business & Analytics
6. ad-roas - Marketing campaign optimization
7. employee-performance - HR and workforce simulation
8. customer-analytics - User behavior and segmentation
9. revenue-forecasting - Financial prediction data
10. business-processes - Workflow automation data

#### 💰 Finance & Trading
11. stock-simulation - Realistic stock market data
12. crypto-trading - Cryptocurrency market simulation
13. risk-analysis - Financial risk scenarios
14. portfolio-optimization - Investment strategy data

#### 🔒 Security & Testing
15. security-testing - Penetration testing scenarios
16. log-analytics - Security and monitoring logs
17. anomaly-detection - Unusual pattern generation
18. vulnerability-scanning - Security test cases

#### 🚀 DevOps & CI/CD
19. cicd-automation - Pipeline testing data
20. deployment-scenarios - Release testing data
21. performance-testing - Load and stress test data
22. monitoring-alerts - Alert and incident data

#### 🤖 Agentic Systems
23. swarm-coordination - Multi-agent orchestration
24. agent-memory - Context and memory patterns
25. agentic-jujutsu - Version control for AI
26. distributed-learning - Federated learning examples

---

🛠️ CLI Commands

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`bash


DSPy training

agentic-synth-examples dspy train [options]
--models Comma-separated model providers
--rounds Optimization rounds (default: 5)
--convergence Quality threshold (default: 0.95)
--budget Cost budget in USD
--output Save results to file

Benchmark models

agentic-synth-examples benchmark [options]
--models Models to compare
--tasks Benchmark tasks
--iterations Iterations per model
`

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`bash


Generate synthetic data

agentic-synth-examples generate [options]
--type Type: structured, timeseries, events
--count Number of records
--schema Schema file
--output Output file

Self-learning generation

agentic-synth-examples self-learn [options]
--task Task type
--iterations Learning iterations
--learning-rate Learning rate (0.0-1.0)
`

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`bash


List all examples

agentic-synth-examples list

Run specific example

agentic-synth-examples run [options]

Get example details

agentic-synth-examples info
`

---

📦 Programmatic Usage

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Install as a dependency:

`bash
npm install @ruvector/agentic-synth-examples
`

Import and use:

`typescript
import {
DSPyTrainingSession,
SelfLearningGenerator,
MultiModelBenchmark
} from '@ruvector/agentic-synth-examples';

// Your code here
`

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Each example includes:
- ✅ Working Code - Copy-paste ready
- 📝 Documentation - Inline comments
- 🧪 Tests - Example test cases
- ⚙️ Configuration - Customizable settings
- 📊 Output Examples - Expected results

---

🎓 Tutorials

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Goal: Train a model to generate product descriptions

`bash


Step 1: Set up API keys

export GEMINI_API_KEY="your-key"

Step 2: Run basic training

npx @ruvector/agentic-synth-examples dspy train \
--models gemini \
--prompt "Generate product descriptions for electronics" \
--rounds 3 \
--output results.json

Step 3: View results

cat results.json | jq '.quality'
`

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Goal: Compare 3 models and find the best

`typescript
import { MultiModelBenchmark } from '@ruvector/agentic-synth-examples';

const benchmark = new MultiModelBenchmark({
models: ['gemini', 'claude', 'gpt4'],
tasks: ['code-generation', 'text-summarization'],
iterations: 5
});

const results = await benchmark.run();
console.log(Winner: ${results.bestModel});
`

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Goal: Build a domain-specific learning system

`typescript
import { SelfLearningGenerator, FeedbackLoop } from '@ruvector/agentic-synth-examples';

class CustomLearner extends SelfLearningGenerator {
async evaluate(output) {
// Custom evaluation logic
return customQualityScore;
}

async optimize(feedback) {
// Custom optimization
return improvedPrompt;
}
}

const learner = new CustomLearner({
domain: 'medical-reports',
specialization: 'radiology'
});

await learner.trainOnDataset(trainingData);
`

---

🔗 Integration with Main Package

This examples package works seamlessly with @ruvector/agentic-synth:

`typescript
import { AgenticSynth } from '@ruvector/agentic-synth';
import { DSPyOptimizer } from '@ruvector/agentic-synth-examples';

// Use main package for generation
const synth = new AgenticSynth({ provider: 'gemini' });

// Use examples for optimization
const optimizer = new DSPyOptimizer();
const optimizedConfig = await optimizer.optimize(synth.getConfig());

// Generate with optimized settings
const data = await synth.generate({
...optimizedConfig,
count: 1000
});
`

---

📊 Example Metrics

| Example | Complexity | Runtime | API Calls | Cost Estimate |
|---------|------------|---------|-----------|---------------|
| DSPy Training | Advanced | 2-5 min | 15-50 | $0.10-$0.50 |
| Self-Learning | Intermediate | 1-3 min | 10-30 | $0.05-$0.25 |
| Stock Simulation | Beginner | <1 min | 5-10 | $0.02-$0.10 |
| Multi-Model | Advanced | 5-10 min | 30-100 | $0.25-$1.00 |

---

🤝 Contributing Examples

Have a great example to share? Contributions welcome!

1. Fork the repository
2. Create your example in examples/
3. Add tests and documentation
4. Submit a pull request

Example Structure:
`
examples/
my-example/
├── index.ts # Main code
├── README.md # Documentation
├── schema.json # Configuration
├── test.ts # Tests
└── output-sample.json # Example output
`

---

📞 Support & Resources

- Main Package: @ruvector/agentic-synth
- Documentation: GitHub Docs
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Twitter: @ruvnet

---

📄 License

MIT © ruvnet

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🌟 Popular Examples

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1. DSPy Multi-Model Training - 🔥 1,000+ uses
2. Self-Learning Systems - 🔥 800+ uses
3. Stock Market Simulation - 🔥 600+ uses
4. CI/CD Automation - 🔥 500+ uses
5. Security Testing - 🔥 400+ uses

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- Agentic Jujutsu Integration - Version control for AI agents
- Federated Learning - Distributed training examples
- Vector Similarity Search - Semantic matching patterns

---

Ready to get started?

`bash
npx @ruvector/agentic-synth-examples dspy train --models gemini
``

Learn by doing with production-ready examples! 🚀