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mock data generator using OpenRouter API with JSON mode support","maintainers":[{"name":"bramatom","email":"bramatom@gmail.com"}],"readme":"# OpenRouter AI Mock Generator\n\n[![npm version](https://badge.fury.io/js/%40bramato%2Fopenrouter-mock-generator.svg)](https://www.npmjs.com/package/@bramato/openrouter-mock-generator)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![TypeScript](https://img.shields.io/badge/TypeScript-5.3.0-blue.svg)](https://www.typescriptlang.org/)\n[![OpenRouter Agents](https://img.shields.io/badge/OpenRouter--Agents-v1.2.1-green.svg)](https://github.com/bramato/openrouter-agents)\n\n**Generate realistic mock data with AI-powered intelligence and professional image processing.**\n\nTransform your development workflow with an advanced mock data generator that combines state-of-the-art language models with intelligent image processing. Perfect for frontend development, testing, prototyping, and creating compelling demos with realistic content.\n\n> šŸšØ **BREAKING CHANGES in v2.0.0**: This version introduces a new architecture powered by [OpenRouter Agents](https://github.com/bramato/openrouter-agents) for enhanced AI capabilities and specialized agents. See [Migration Guide](#-migration-from-v1x-to-v200) below.\n\n## Why Choose This Generator?\n\n### šŸŽÆ **Intelligent Data Generation**\n- Analyzes your JSON structure and generates contextually appropriate data\n- Supports complex nested objects and arrays\n- Maintains data relationships and patterns from your examples\n\n### šŸ–¼ļø **Professional Image Processing**\n- Replaces placeholder images with AI-generated, contextually relevant images\n- Automatic cloud storage integration (AWS S3, DigitalOcean Spaces)\n- Smart optimization reduces image generation costs by up to 30%\n\n### šŸš€ **Developer-First Experience**\n- Simple CLI commands get you started in minutes\n- Comprehensive TypeScript API for programmatic usage\n- Interactive configuration wizard handles complex setup\n\n### šŸ¤– **NEW v2.0.0: Powered by OpenRouter Agents**\n- **Specialized AI Agents**: Uses dedicated MockDataAgent for intelligent data generation\n- **Enhanced Architecture**: Modular design with better separation of concerns\n- **Future-Ready**: Access to the entire OpenRouter Agents ecosystem\n- **Backward Compatible**: Same CLI and API interface you know and love\n\n---\n\n## Quick Installation\n\n```bash\nnpm install @bramato/openrouter-mock-generator\n```\n\n## Getting Started\n\n### Step 1: Initial Setup\n\nRun the interactive configuration wizard to set up your API keys and preferences:\n\n```bash\nnpx ai-init\n```\n\nThe wizard will configure:\n- **OpenRouter API Key** (Required) - For AI text generation\n- **Model Selection** - Choose optimal models for your use case\n- **Image Processing** (Optional) - Hugging Face API + Cloud storage setup\n\n### Step 2: Generate Your First Mock Data\n\n```bash\n# Try the included example file (users, products, orders)\nnpx ai-generate-mock example.json --count 20\n\n# Basic generation with placeholder images\nnpx ai-generate-mock your-sample.json --count 50\n\n# Professional generation with AI images (requires setup)\nnpx ai-generate-mock products.json --images --count 25\n```\n\n### Step 3: Customize Your Output\n\n```bash\n# Add custom preferences for more targeted data\nnpx ai-generate-mock products.json \\\n --preferences \"Luxury Italian fashion brands with premium pricing\" \\\n --count 30 \\\n --images\n```\n\n**That's it!** Your mock data is generated and ready to use in your application.\n\n## CLI Reference\n\n### Core Commands\n\n| Command | Description | Example |\n|---------|-------------|---------|\n| `ai-init` | Interactive setup wizard | `npx ai-init` |\n| `ai-generate-mock` | Generate mock data from JSON sample | `npx ai-generate-mock data.json --count 50` |\n\n### Command Options\n\n| Option | Short | Description | Example |\n|--------|-------|-------------|---------|\n| `--count` | `-c` | Number of items to generate | `--count 100` |\n| `--output` | `-o` | Output file path | `--output results.json` |\n| `--preferences` | `--pref` | Custom generation instructions | `--pref \"Italian luxury brands\"` |\n| `--array-path` | `-p` | Target specific JSON array | `--array-path \"data.products\"` |\n| `--images` | | Force enable AI image processing | `--images` |\n| `--no-images` | | Force disable AI image processing | `--no-images` |\n| `--analyze` | `-a` | Analyze structure without generating | `--analyze` |\n\n### Common Usage Patterns\n\n```bash\n# Standard data generation\nnpx ai-generate-mock products.json --count 50\n\n# Professional images with custom preferences\nnpx ai-generate-mock catalog.json \\\n --images \\\n --preferences \"High-end electronics with professional photography\" \\\n --count 25\n\n# Target specific array in complex JSON\nnpx ai-generate-mock complex-data.json \\\n --array-path \"store.inventory.products\" \\\n --count 100 \\\n --output inventory.json\n\n# Analyze before generating\nnpx ai-generate-mock data.json --analyze\n```\n\n## TypeScript/JavaScript API\n\n### Basic Implementation (v2.0.0+)\n\n```typescript\nimport { MockGeneratorService } from '@bramato/openrouter-mock-generator';\n\n// MockGeneratorService now uses OpenRouter Agents internally\nconst generator = new MockGeneratorService();\n\nconst result = await generator.generateMockData({\n inputFile: 'products.json',\n outputFile: 'generated_products.json',\n count: 50,\n preferences: 'Italian fashion brands with euro pricing'\n});\n\nif (result.success) {\n console.log(`āœ… Generated ${result.generatedCount} items`);\n console.log(`šŸ¤– Powered by MockDataAgent from openrouter-agents`);\n} else {\n console.error(`āŒ Generation failed: ${result.error}`);\n}\n```\n\n### Advanced Configuration with OpenRouter Agents\n\n```typescript\nimport { MockGeneratorService } from '@bramato/openrouter-mock-generator';\nimport { AgentManager } from 'openrouter-agents';\n\n// Direct access to the underlying Agent ecosystem (v2.0.0+)\nconst agentManager = new AgentManager({\n openRouter: {\n apiKey: 'your-api-key',\n baseURL: 'https://openrouter.ai/api/v1'\n }\n});\n\n// Get the specialized MockDataAgent\nconst mockAgent = agentManager.getAgent('mock-data');\n\n// Traditional API still works (uses agents internally)\nconst generator = new MockGeneratorService(\n undefined, // Use default OpenRouter config\n true // Enable image processing\n);\n\nconst result = await generator.generateMockData({\n inputFile: 'catalog.json',\n outputFile: 'enhanced_catalog.json',\n count: 25,\n preferences: 'Luxury electronics with professional product photography',\n enableImageProcessing: true\n});\n```\n\n### Using MockDataAgent Directly (New v2.0.0 Feature)\n\n```typescript\nimport { MockDataAgent } from 'openrouter-agents';\n\n// Create specialized mock data agent\nconst agent = new MockDataAgent({\n name: 'product-generator',\n openRouter: {\n apiKey: process.env.OPENROUTER_API_KEY!,\n model: 'anthropic/claude-3.5-sonnet'\n },\n temperature: 0.7\n});\n\n// Generate structured data\nconst response = await agent.process({\n input: {\n template: { name: 'string', price: 'number', category: 'string' },\n count: 10,\n preferences: 'Italian luxury fashion'\n },\n options: { forceJson: true }\n});\n\nconsole.log(response.data); // Structured JSON array\n```\n\n### Standalone Image Processing\n\nTransform existing mock data by replacing placeholder images with AI-generated ones:\n\n```typescript\nimport { PostProcessingOrchestrator } from '@bramato/openrouter-mock-generator';\nimport fs from 'fs';\n\nconst processor = new PostProcessingOrchestrator('your-hf-api-key', {\n verbose: true,\n enableOptimization: true,\n uploadToCloud: true\n});\n\n// Load existing mock data\nconst mockData = JSON.parse(fs.readFileSync('existing-data.json', 'utf8'));\n\n// Process images\nconst result = await processor.processData(mockData);\n\nif (result.success) {\n // Save enhanced data with real images\n fs.writeFileSync('enhanced-data.json', \n JSON.stringify(result.processedData, null, 2)\n );\n console.log('āœ… Image processing complete');\n}\n```\n\n## Configuration\n\n### Required Environment Variables\n\n```bash\n# OpenRouter API (Required)\nOPENROUTER_API_KEY=your-openrouter-api-key\n```\n\n### Optional Configuration\n\n#### AI Model Settings\n```bash\nOPENROUTER_DEFAULT_MODEL=anthropic/claude-3.5-sonnet\nOPENROUTER_MOCK_GENERATOR_MODEL=openai/gpt-4o-mini\n```\n\n#### AI Image Processing\n```bash\nHUGGINGFACE_API_KEY=your-huggingface-token\nSTORAGE_PROVIDER=aws # or 'digitalocean'\n```\n\n#### AWS S3 Storage\n```bash\nAWS_ACCESS_KEY_ID=your-access-key\nAWS_SECRET_ACCESS_KEY=your-secret-key\nAWS_REGION=us-east-1\nAWS_S3_BUCKET_NAME=your-bucket-name\nAWS_S3_ENDPOINT=custom-endpoint # optional\n```\n\n#### DigitalOcean Spaces Storage\n```bash\nDO_SPACES_ACCESS_KEY=your-access-key\nDO_SPACES_SECRET_KEY=your-secret-key\nDO_SPACES_REGION=fra1\nDO_SPACES_NAME=your-space-name\nDO_API_TOKEN=your-api-token # optional\n```\n\n> **šŸ’” Tip:** Use the `npx ai-init` wizard to set up these variables interactively.\n\n## Supported AI Models\n\n**Automatic JSON-mode compatibility** - Only models that support structured JSON output are used:\n\n| Provider | Models | Best For |\n|----------|--------|----------|\n| **OpenAI** | GPT-4o, GPT-4o Mini, GPT-4 Turbo, GPT-3.5 Turbo | General purpose, consistent output |\n| **Anthropic** | Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku | Complex reasoning, detailed content |\n| **Google** | Gemini Pro, Gemini Flash | Fast generation, multilingual |\n| **Meta** | Llama models via OpenRouter | Cost-effective, good performance |\n\n> The system automatically selects the best model based on your configuration and task complexity.\n\n---\n\n## Real-World Examples\n\n### šŸš€ Complete Example: Multi-Entity System\n\nTry the included example file to see the full power of the generator:\n\n```bash\n# Generate realistic data for users, products, and orders\nnpx ai-generate-mock example.json --count 20 --images\n\n# Or target a specific entity type\nnpx ai-generate-mock example.json --count 50 --array-path users\nnpx ai-generate-mock example.json --count 30 --array-path products --images\n```\n\n**Using the included `example.json`** which contains:\n- **šŸ‘„ User profiles** with addresses, preferences, and social data\n- **šŸ“± Products** with specifications, ratings, and image galleries \n- **šŸ“¦ Orders** with shipping, payment, and tracking information\n\n**Generated results include:**\n- Professional user avatars and product photography\n- Contextual data relationships (orders link to existing users/products)\n- Italian locale with realistic names and addresses\n- Complex nested objects and arrays\n- Consistent image seeds for repeatable results\n\n**Sample generated user:**\n```json\n{\n \"id\": 2,\n \"name\": \"Giulia Bianchi\",\n \"email\": \"giulia.bianchi@example.com\",\n \"age\": 28,\n \"avatar\": \"https://picsum.photos/200/200?random=giulia\",\n \"address\": {\n \"street\": \"Corso Buenos Aires 45\",\n \"city\": \"Milano\", \n \"zipCode\": \"20124\",\n \"country\": \"Italia\"\n },\n \"preferences\": {\n \"newsletter\": true,\n \"theme\": \"light\",\n \"language\": \"it\"\n },\n \"profile\": {\n \"bio\": \"UX Designer specializzata in interfacce mobile innovative\",\n \"website\": \"https://giuliabianchi.design\",\n \"social\": {\n \"linkedin\": \"https://linkedin.com/in/giuliabianchi\",\n \"github\": \"https://github.com/giuliabianchi\"\n }\n }\n}\n```\n\n### šŸ›ļø E-commerce Product Catalog\n\n```bash\nnpx ai-generate-mock products.json \\\n --images \\\n --preferences \"Luxury Italian fashion from Milano boutiques\" \\\n --count 30\n```\n\n**Generated content includes:**\n- Professional product photography\n- Brand-appropriate thumbnails and galleries\n- Contextual banner images for promotions\n\n### šŸ• Restaurant Menu System\n\n```bash\nnpx ai-generate-mock menu.json \\\n --images \\\n --preferences \"Traditional Italian regional cuisine with wine pairings\" \\\n --count 20\n```\n\n**Produces:**\n- High-quality food photography\n- Restaurant ambiance images\n- Chef and staff portraits\n\n### šŸ‘„ User Management System\n\n```bash\nnpx ai-generate-mock users.json \\\n --images \\\n --preferences \"Professional Italian business people from major cities\" \\\n --count 100\n```\n\n**Creates:**\n- Professional headshot portraits\n- Company logos and branding\n- Office environment backgrounds\n\n---\n\n## Use Cases\n\n### šŸ”§ **Development & Testing**\n- **Frontend Development**: Realistic data for UI components and layouts\n- **API Testing**: Comprehensive datasets for endpoint validation \n- **Database Seeding**: Meaningful sample data for development environments\n- **Demo Applications**: Professional-grade content for presentations\n\n### šŸŽØ **Design & Prototyping**\n- **UI/UX Design**: Contextual content for design systems and mockups\n- **Client Presentations**: Realistic prototypes with professional imagery\n- **A/B Testing**: Varied content sets for testing different approaches\n- **Design Validation**: Real-world data scenarios for user testing\n\n### šŸ“ˆ **Marketing & Content**\n- **Campaign Development**: Sample content for marketing materials\n- **Portfolio Showcase**: Professional examples for service demonstrations\n- **Content Strategy**: Diverse datasets for testing content approaches\n- **Brand Guidelines**: Consistent imagery and messaging examples\n\n## Custom Preferences Guide\n\nEnhance your generated data with specific instructions to guide the AI:\n\n### Industry-Specific Examples\n\n```bash\n# E-commerce & Retail\n--preferences \"Luxury Italian fashion brands with Milano designers and euro pricing\"\n\n# Food & Hospitality \n--preferences \"Traditional Italian regional cuisine with seasonal ingredients and wine pairings\"\n\n# Professional Services\n--preferences \"Italian business professionals from Rome, Milan, and Naples with LinkedIn-style profiles\"\n\n# Real Estate\n--preferences \"Historic Italian properties in city centers with Renaissance and Baroque architecture\"\n\n# Technology\n--preferences \"Italian tech companies and startups with modern, innovation-focused branding\"\n\n# Travel & Tourism\n--preferences \"Italian cultural events, festivals, and tourist attractions with regional authenticity\"\n```\n\n### Best Practices\n- **Be Specific**: Include geographical, cultural, or industry context\n- **Set Tone**: Specify whether content should be formal, casual, luxury, budget-friendly, etc.\n- **Add Details**: Mention specific attributes like pricing ranges, demographics, or styles\n- **Use Examples**: Reference well-known brands or concepts for clearer direction\n\n---\n\n## Advanced AI Image Processing\n\nTransform placeholder images into professional, contextually-appropriate visuals automatically.\n\n### šŸ”„ **Processing Pipeline**\n\n1. **Smart Detection** - Identifies placeholder image URLs (`https://picsum.photos/*`)\n2. **Context Analysis** - Analyzes surrounding JSON data for image context\n3. **Intelligent Optimization** - Groups similar images to minimize generation costs\n4. **AI Generation** - Creates professional images using FLUX.1-dev and Qwen-Image models\n5. **Cloud Integration** - Uploads to AWS S3 or DigitalOcean Spaces with CDN optimization\n6. **Seamless Replacement** - Updates JSON with new cloud-hosted URLs\n\n### šŸ“ø **Image Categories**\n\n| Type | Description | Use Cases |\n|------|-------------|-----------|\n| **Product Photos** | Professional product photography with proper lighting | E-commerce catalogs, inventory |\n| **Portrait & Avatars** | High-quality headshots and professional portraits | User profiles, team pages |\n| **Banners & Headers** | Wide-format promotional and branding images | Marketing materials, headers |\n| **Thumbnails** | Optimized small-format preview images | Card layouts, galleries |\n| **Logos & Branding** | Professional brand identity elements | Company profiles, partnerships |\n| **Backgrounds** | Contextual environment and texture images | UI backgrounds, hero sections |\n\n### āš” **Smart Optimizations**\n\n- **Cost Reduction**: Up to 30% savings through intelligent image reuse\n- **Context Awareness**: Images generated based on surrounding data context\n- **Batch Efficiency**: Concurrent processing with intelligent rate limiting\n- **Graceful Fallbacks**: Maintains placeholder images if generation fails\n- **CDN Integration**: Automatic content delivery network optimization\n\n\n## Troubleshooting\n\n### Common Issues & Solutions\n\n#### šŸš« **Image Processing Not Working**\n\n**Symptoms**: Images remain as placeholder URLs\n```bash\n# 1. Verify configuration\nnpx ai-init\n\n# 2. Test with minimal example\nnpx ai-generate-mock sample.json --images --count 1\n\n# 3. Check environment variables\necho $HUGGINGFACE_API_KEY\necho $STORAGE_PROVIDER\n```\n\n#### ā˜ļø **Cloud Storage Upload Failures**\n\n**Symptoms**: Generated images not accessible via URLs\n```bash\n# Verify cloud storage configuration\ncat .env | grep -E \"(STORAGE_PROVIDER|AWS_|DO_)\"\n\n# Test permissions\n# AWS: Check IAM permissions for S3 bucket access\n# DigitalOcean: Verify Spaces access keys and region\n```\n\n#### šŸ”‘ **API Authentication Errors**\n\n**OpenRouter Issues**:\n- Verify API key at [OpenRouter Dashboard](https://openrouter.ai/keys)\n- Check account balance and usage limits\n\n**Hugging Face Issues**:\n- Confirm token permissions at [HF Settings](https://huggingface.co/settings/tokens)\n- Ensure \"Make calls to the serverless Inference API\" is enabled\n\n#### šŸ“„ **JSON Processing Errors**\n\n**Symptoms**: \"Cannot parse input file\" or similar errors\n```bash\n# Analyze your JSON structure\nnpx ai-generate-mock your-file.json --analyze\n\n# Common fixes:\n# - Ensure valid JSON syntax\n# - Check for proper array structures \n# - Verify file encoding (UTF-8)\n```\n\n#### āš” **Rate Limiting & Performance**\n\n**Symptoms**: Slow generation or timeout errors\n- **Reduce count**: Use smaller `--count` values for testing\n- **Check quotas**: Review API usage limits on provider dashboards\n- **Optimize batching**: System auto-adjusts, but complex data may need smaller batches\n\n#### šŸ”§ **Environment Configuration**\n\n**Problem**: Environment variables not loading\n```bash\n# āœ… Correct format\nOPENROUTER_API_KEY=your-actual-key\n\n# āŒ Incorrect (spaces around =)\nOPENROUTER_API_KEY = your-actual-key\n```\n\n**Debug steps**:\n1. Check `.env` file exists in project root\n2. Verify no extra spaces or quotes around values\n3. Restart terminal/application after changes\n\n---\n\n## Frequently Asked Questions\n\n### **API Requirements**\n\n**Q: Do I need both OpenRouter and Hugging Face API keys?** \nA: OpenRouter is required for text generation. Hugging Face is optional - only needed for AI image processing.\n\n**Q: Are there free tiers available?** \nA: Yes! OpenRouter offers free credits for new accounts, and Hugging Face has a generous free tier with rate limits.\n\n### **Storage & Configuration**\n\n**Q: Can I use both AWS S3 and DigitalOcean Spaces?** \nA: No, you must choose one storage provider. Configure this during `npx ai-init` setup.\n\n**Q: What happens if cloud storage fails?** \nA: The system gracefully falls back to placeholder images, ensuring your mock data generation always succeeds.\n\n### **Image Processing**\n\n**Q: Can I process existing mock data with AI images?** \nA: Absolutely! Use the `PostProcessingOrchestrator` class programmatically, or re-run generation with `--images` flag.\n\n**Q: What image formats and sizes are supported?** \nA: Generates PNG images with contextually appropriate dimensions. Supports product photos, avatars, banners, thumbnails, and more.\n\n**Q: How does cost optimization work?** \nA: The system intelligently detects similar image contexts and reuses generated images, reducing generation costs by up to 30%.\n\n### **Technical Details**\n\n**Q: Which AI models work best?** \nA: The system auto-selects optimal models. GPT-4o Mini offers great balance of speed/quality, while Claude 3.5 Sonnet excels at complex data structures.\n\n**Q: Can I use this in production applications?** \nA: Yes! The tool is designed for professional use with proper error handling, rate limiting, and cloud integration.\n\n---\n\n## Architecture Overview\n\n### **Microservices Design**\n- **ImageUrlExtractor**: Identifies and categorizes image URLs in JSON data\n- **DescriptionGenerator**: Creates contextual AI prompts based on surrounding data\n- **ImageProcessingAnalyzer**: Optimizes generation strategy and detects duplicates\n- **UrlReplacer**: Seamlessly updates JSON with new cloud-hosted image URLs\n- **PostProcessingOrchestrator**: Coordinates all services with intelligent error handling\n\n### **Performance Features**\n- **Smart Batching**: Automatic optimization based on data complexity\n- **Concurrent Processing**: Parallel image generation with rate limiting\n- **CDN Integration**: Automatic content delivery network optimization\n- **Intelligent Caching**: Reuses similar images for cost efficiency\n\n## šŸ”„ Migration from v1.x to v2.0.0\n\n### What Changed\n\n**v2.0.0 introduces a new architecture** powered by [OpenRouter Agents](https://github.com/bramato/openrouter-agents), providing:\n\n- **Specialized AI Agents**: MockDataAgent optimized for data generation\n- **Better Modularity**: Cleaner separation between text generation and image processing\n- **Future Extensibility**: Access to the growing OpenRouter Agents ecosystem\n- **Enhanced Performance**: Improved error handling and response processing\n\n### Breaking Changes\n\nāŒ **Removed Dependencies**:\n- Internal `openrouter.ts` and `openrouter-api.ts` services (moved to openrouter-agents package)\n\nāœ… **Added Dependencies**:\n- `openrouter-agents: ^1.2.1` - Core agent functionality\n\n### Migration Steps\n\n1. **Update to v2.0.0**:\n ```bash\n npm install @bramato/openrouter-mock-generator@^2.0.0\n ```\n\n2. **No Code Changes Required**: \n - All existing CLI commands work unchanged\n - All existing TypeScript/JavaScript APIs remain compatible\n - Configuration and environment variables are identical\n\n3. **Optional: Explore New Features**:\n ```typescript\n // Access the underlying agent system (new in v2.0.0)\n import { MockDataAgent } from 'openrouter-agents';\n \n const agent = new MockDataAgent({\n name: 'my-generator',\n openRouter: { apiKey: 'your-key' }\n });\n ```\n\n### Compatibility Matrix\n\n| Feature | v1.x | v2.0.0 | Notes |\n|---------|------|--------|---------|\n| CLI Commands | āœ… | āœ… | Identical interface |\n| MockGeneratorService API | āœ… | āœ… | Same methods and options |\n| Image Processing | āœ… | āœ… | Enhanced performance |\n| Environment Variables | āœ… | āœ… | No changes required |\n| TypeScript Types | āœ… | āœ… | Improved with agent types |\n| **MockDataAgent Access** | āŒ | āœ… | **New:** Direct agent usage |\n| **Agent Ecosystem** | āŒ | āœ… | **New:** Access to all OpenRouter Agents |\n\n### Why Upgrade?\n\n- šŸŽÆ **Specialized Agents**: Purpose-built AI agents for better performance\n- šŸ”§ **Better Architecture**: More maintainable and extensible codebase\n- šŸš€ **Future Features**: Access to new agents as they're released\n- šŸ“ˆ **Enhanced Reliability**: Improved error handling and response processing\n- šŸ”— **Ecosystem Access**: Use code generators, translators, and more from the same package\n\n### Need Help?\n\nIf you encounter issues during migration:\n1. Check the [OpenRouter Agents documentation](https://github.com/bramato/openrouter-agents#readme)\n2. File an issue on [GitHub Issues](https://github.com/bramato/openrouter-mock-generator/issues)\n3. Review the [v2.0.0 changelog](#changelog) below\n\n---\n\n## šŸ“‹ Changelog\n\n### v2.0.0 (Latest) - Major Architecture Update\n**Release Date**: Current\n\n**šŸšØ BREAKING CHANGES:**\n- Migrated to OpenRouter Agents architecture\n- Removed internal OpenRouter service files\n- Added `openrouter-agents ^1.2.1` dependency\n\n**āœØ NEW FEATURES:**\n- **MockDataAgent Integration**: Specialized AI agent for data generation\n- **Agent Ecosystem Access**: Direct access to OpenRouter Agents platform\n- **Enhanced Architecture**: Improved modularity and extensibility\n- **Better Error Handling**: More robust processing with agent-based approach\n\n**šŸ”§ IMPROVEMENTS:**\n- Same CLI interface with improved backend performance\n- Enhanced TypeScript support with agent types\n- Better separation of concerns between text and image generation\n- Backward-compatible API maintains all existing functionality\n\n**šŸ“¦ DEPENDENCIES:**\n- Added: `openrouter-agents ^1.2.1`\n- Updated: Core dependencies for better compatibility\n\n### v1.3.2 (Previous) - Stability Improvements\n- Enhanced image processing pipeline\n- Improved error handling for cloud storage\n- Better configuration management\n- Performance optimizations\n\n---\n\n## License & Support\n\n**License**: MIT - Free for commercial and personal use\n\n**Author**: [bramato](https://github.com/bramato)\n\n### **Contributing**\nWe welcome contributions! Please feel free to submit issues, feature requests, or pull requests on GitHub.\n\n### **Support**\n- šŸ“– **Documentation**: This README covers all features and use cases\n- šŸ¤– **OpenRouter Agents**: Check the [OpenRouter Agents documentation](https://github.com/bramato/openrouter-agents#readme)\n- šŸ› **Bug Reports**: Please use GitHub Issues with detailed reproduction steps\n- šŸ’” **Feature Requests**: Share your ideas through GitHub Discussions\n- šŸ”§ **Configuration Help**: Use the interactive `npx ai-init` wizard\n\n### **Related Projects**\n- šŸ¤– **[OpenRouter Agents](https://github.com/bramato/openrouter-agents)**: The core AI agent platform powering this generator\n- šŸ“¦ **[NPM Package](https://www.npmjs.com/package/openrouter-agents)**: OpenRouter Agents on NPM\n- šŸŒ **[OpenRouter Platform](https://openrouter.ai)**: Access to 100+ AI models\n\n---\n\nā­ **If this tool accelerates your development workflow, please star both repositories:**\n- **[Mock Generator](https://github.com/bramato/openrouter-mock-generator)** - This project\n- **[OpenRouter Agents](https://github.com/bramato/openrouter-agents)** - The underlying AI platform","readmeFilename":"README.md"}