Production-ready examples for @ruvector/agentic-synth - DSPy training, multi-model benchmarking, and advanced synthetic data generation patterns
npm install @ruvector/agentic-synth-examplesProduction-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.
---
``bashInstall the examples package
npm install -g @ruvector/agentic-synth-examples
$3
`bash
DSPy multi-model training
npx @ruvector/agentic-synth-examples dspy train \
--models gemini,claude \
--prompt "Generate product descriptions" \
--rounds 3Basic synthetic data generation
npx @ruvector/agentic-synth-examples generate \
--type structured \
--count 100 \
--schema ./schema.json
`---
๐ 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
`$3
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
`$3
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
---
๐ฏ 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:
`
โ 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 0.96โ Training complete!
Best model: GPT-4 (0.96 quality)
Quality improvement: 28%
Total cost: $0.23
Duration: 3.2 minutes
`$3
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}%);
`$3
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);
`---
๐ Complete Example List
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#### ๐ง 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
#### ๐ผ 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
#### ๐ฐ 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
#### ๐ 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
#### ๐ 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
#### ๐ค 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
---
๐ ๏ธ 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 fileBenchmark models
agentic-synth-examples benchmark [options]
--models Models to compare
--tasks Benchmark tasks
--iterations Iterations per model
`$3
`bash
Generate synthetic data
agentic-synth-examples generate [options]
--type Type: structured, timeseries, events
--count Number of records
--schema Schema file
--output Output fileSelf-learning generation
agentic-synth-examples self-learn [options]
--task Task type
--iterations Learning iterations
--learning-rate Learning rate (0.0-1.0)
`$3
`bash
List all examples
agentic-synth-examples listRun specific example
agentic-synth-examples run [options]Get example details
agentic-synth-examples info
`---
๐ฆ 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
`$3
Each example includes:
- โ
Working Code - Copy-paste ready
- ๐ Documentation - Inline comments
- ๐งช Tests - Example test cases
- โ๏ธ Configuration - Customizable settings
- ๐ Output Examples - Expected results
---
๐ 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.jsonStep 3: View results
cat results.json | jq '.quality'
`$3
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});
`$3
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);
`---
๐ 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
});
`---
๐ 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 |
---
๐ค 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 requestExample Structure:
`
examples/
my-example/
โโโ index.ts # Main code
โโโ README.md # Documentation
โโโ schema.json # Configuration
โโโ test.ts # Tests
โโโ output-sample.json # Example output
`---
๐ Support & Resources
- Main Package: @ruvector/agentic-synth
- Documentation: GitHub Docs
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Twitter: @ruvnet
---
๐ License
MIT ยฉ ruvnet
---
๐ Popular Examples
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1. DSPy Multi-Model Training - ๐ฅ 1,000+ uses
2. Self-Learning Systems - ๐ฅ 800+ uses
3. Stock Market Simulation - ๐ฅ 600+ uses
4. CI/CD Automation - ๐ฅ 500+ uses
5. Security Testing - ๐ฅ 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! ๐