Reduce AI costs by up to 70% with SynthLang's efficient prompt optimization. Experience up to 233% faster processing while maintaining effectiveness.
Transform your AI interactions with mathematically-structured prompts. Symbolic Scribe brings academic rigor to prompt engineering, helping you create more precise, reliable, and powerful AI interactions.
🔄 Translator Engine
- Advanced prompt parsing and tokenization
- Intelligent structure analysis and context identification
- Pattern recognition and syntax transformation
- Real-time format validation and error detection
- Metadata extraction and processing
⚡️ Performance Optimization
- Token reduction up to 70% through advanced compression
- Processing speed improvements up to 233%
- Real-time token counting and model-specific calculations
- Semantic analysis and duplicate detection
- Context merging and density optimization
🧪 Testing Framework
- Comprehensive OpenRouter integration
- Response quality validation
- Performance monitoring (<500ms translation time)
- Success rate tracking and error management
- Usage pattern analysis
🔧 Technical Architecture
- React + TypeScript frontend with Vite
- Tailwind CSS for responsive design
- OpenRouter API integration
- Local-first architecture for privacy
- WebAssembly modules for performance
- Horizontal scaling capability
- Advanced caching strategies
🎯 System Requirements
- Response time < 500ms for translations
- 99.9% uptime for API services
- < 100ms latency for token counting
- Real-time cost calculation
- Concurrent request handling
- Load balancing and request queuing
🔒 Security Features
- Encrypted API key storage
- Request validation and access control
- Comprehensive audit logging
- Data encryption at rest and in transit
- Automated security testing
✨ Mathematical Precision - Use formal frameworks for structured prompts
🧮 Academic Rigor - Leverage set theory, topology, and abstract algebra
🔒 Enhanced Security - Built-in threat modeling and safety constraints
📱 Modern Interface - Sleek, responsive design that works everywhere
🚀 Instant Testing - Real-time preview with multiple AI models
- Set Theory Templates: Model complex relationships and hierarchies
- Category Theory: Define abstract transformations and mappings
- Abstract Algebra: Structure group operations and symmetries
- Topology: Explore continuous transformations and invariants
- Complex Analysis: Handle multi-dimensional relationships
- Information Security: Model threat vectors and attack surfaces
- Ethical Analysis: Structure moral frameworks and constraints
- AI Safety: Define system boundaries and safety properties
- Domain Adaptation: Apply mathematical rigor to any field
- Interactive Console: Terminal-style interface with modern aesthetics
- Real-time Preview: Test prompts with multiple AI models
- Template Library: Pre-built frameworks for common use cases
- Mobile Responsive: Full functionality on all device sizes
- Local Storage: Secure saving of prompts and preferences
- Encrypted local storage of API keys
- Optional environment variable configuration
- No server-side key storage
- Automatic key validation
- Client-side only processing
- No external data transmission except to OpenRouter API
- No tracking or analytics
- Configurable model selection
- Installation
git clone https://github.com/ruvnet/SynthLang.git
cd SynthLang
npm install- Configuration
cp .env.sample .env
# Edit .env with your OpenRouter API key- Development
npm run dev- Production Build
npm run build
npm run previewSynthLang includes a powerful command-line interface for prompt engineering, framework translation, and optimization capabilities.
pip install synthlang- Translate - Convert natural language to SynthLang format:
synthlang translate --source "your prompt" --framework synthlang- Optimize - Improve prompt efficiency:
synthlang optimize "path/to/prompt.txt"- Evolve - Use genetic algorithms to improve prompts:
synthlang evolve "initial_prompt"- Classify - Analyze and categorize prompts:
synthlang classify "prompt_text"For detailed documentation on CLI usage and features, see:
- Select a mathematical framework template
- Choose your target domain
- Define your variables and relationships
- Generate structured prompts
- Navigate to Templates page
- Select a base template
- Modify variables and relationships
- Save for future use
- Use the Preview function to test prompts
- Select different models for comparison
- Refine based on responses
- Export final versions
- Client-side only architecture
- No persistent server storage
- Encrypted API key storage
- Input sanitization
- Regular API key rotation
- Use environment variables in production
- Monitor API usage
- Review generated prompts for sensitive data
We welcome contributions! Please see our Contributing Guide for details.
- Fork the repository
- Create a feature branch
- Install dependencies
- Make your changes
- Run tests
- Submit a PR
- Documentation:
/docspage in app - Issues: GitHub issue tracker
- Community: Discord server (coming soon)
src/
├── core/
│ ├── translator/ # Prompt translation engine
│ ├── optimizer/ # Token optimization system
│ └── tester/ # Testing framework
├── services/
│ ├── openRouter/ # OpenRouter integration
│ ├── storage/ # State management
│ └── analytics/ # Performance metrics
└── interfaces/
├── web/ # Web interface
└── api/ # API endpoints
🔨 Code Organization
- Modular architecture with clear separation of concerns
- Consistent naming conventions and comprehensive documentation
- Type safety and robust error handling
- Extensive test coverage (unit, integration, performance)
- CI/CD pipeline with automated testing and deployment
- Comprehensive monitoring and logging
🚀 Planned Enhancements
- Advanced optimization algorithms
- Extended model support
- Enhanced analytics capabilities
- Automated optimization suggestions
- Custom testing scenarios
- Batch processing improvements
- Community features and integrations
MIT License - see LICENSE file for details
- OpenRouter for AI model access
- shadcn/ui for component library
- Tailwind CSS for styling
- Vite for build tooling