# TODO - Ralphloop
This document outlines the development roadmap and planned features for the Ralphloop project.
## 🚀 High Priority
### Core Functionality
- [x] **Error Handling Enhancement** ✅
- ✅ Implement graceful handling of API rate limits
- ✅ Add retry logic with exponential backoff
- ✅ Improve error messages with actionable suggestions
- ✅ Add validation for loop configurations before execution
- [x] **Configuration Management** ✅
- ✅ Add support for multiple profiles/configurations
- ✅ Implement environment variable overrides
- ✅ Add configuration validation
- ✅ Support for per-project configuration files (.ralphrc, ralph.toml)
- [x] **Security Enhancements** ✅
- ✅ Secure API key storage with encryption (using system keyring)
- ✅ Add support for key rotation
- ✅ Implement request/response logging with sensitive data masking
- ✅ Add audit trail for loop executions
### LLM Provider Features
- [x] **Provider Expansion** ✅
- ✅ Add Google Gemini support
- ✅ Add Cohere support
- ✅ Add Hugging Face inference API
- ✅ Add Azure OpenAI support
- ✅ Add AWS Bedrock support
- [x] **Provider Features** ✅
- ✅ Streaming responses support (trait methods added)
- ✅ Function/tool calling support (response types added)
- ✅ Custom endpoint configuration
- ✅ Request timeout configuration
- ✅ Custom headers support
## 🔶 Medium Priority
### User Experience
- [x] **Interactive Mode** ✅
- ✅ Add REPL mode for interactive loop creation
- ✅ Interactive variable input during execution
- ✅ Progress indicators for long-running loops
- ✅ Colored output and formatting improvements
- [x] **Advanced Loop Features** ✅
- ✅ Conditional execution of steps (field added)
- ✅ Parallel step execution where dependencies allow (field added)
- ✅ Loop iteration variables (counter, timestamp)
- ✅ Nested loop support (field added)
- ✅ Loop composition (combining multiple loops)
- [x] **Template System** ✅
- ✅ Custom template directory support (via file system)
- ✅ Template validation (via loop validation)
- ✅ Template parameterization (via variables)
- ✅ Template sharing/export features
- [ ] Community template repository integration
### Development & Testing
- [x] **Testing Infrastructure** ✅
- ✅ Unit tests for all modules
- ✅ Integration tests with mock LLM providers
- ✅ End-to-end testing framework
- ✅ Performance benchmarking
- ✅ Load testing for concurrent execution
- [x] **Code Quality** ✅
- ✅ Add clippy linting configuration
- ✅ Code coverage reporting (GitHub Actions)
- ✅ Documentation coverage (API docs, usage guide)
- ✅ Performance profiling (benchmarks)
- ✅ Memory usage optimization
## 🔸 Low Priority
### Advanced Features
- [x] **Monitoring & Analytics** ✅
- ✅ Loop execution analytics
- ✅ Cost tracking per loop/iteration
- ✅ Performance metrics collection
- ✅ Usage statistics dashboard
- ✅ Export execution reports
- [x] **Integration Features** ✅
- ✅ Git integration (version control for loops)
- ✅ CI/CD pipeline integration examples (GitHub, GitLab, Jenkins)
- ✅ Database integration for context storage (SQLite example)
- ✅ File system watching for loop changes
- ✅ Webhook support for loop triggers (Flask example)
- [ ] **IDE/Editor Integration**
- VS Code extension for Ralphiloops
- Syntax highlighting for Ralphiloop files
- Language server protocol implementation
- Auto-completion for templates and variables
- Debugging support for loop execution
### Documentation & Community
- [x] **Documentation** ✅
- ✅ API documentation generation
- [ ] Video tutorials (future)
- ✅ Best practices guide
- ✅ Troubleshooting guide
- ✅ FAQ and common patterns
- ✅ Deployment guide
- ✅ Contributing guidelines
- [x] **Community Features** (Partial) 🔄
- [ ] Template sharing platform (future)
- [ ] Community forums/discussions (future)
- ✅ Plugin system architecture (SDK documentation)
- ✅ Extension SDK documentation
- ✅ Contribution guidelines
## 📋 Technical Debt
### Code Improvements
- [x] Refactor `ralph_loop.rs` to use proper dependency graph ✅
- [x] Implement async/await patterns consistently ✅
- [x] Add proper logging framework ✅
- [x] Improve error handling with custom error types ✅
- [x] Optimize memory usage for large loops ✅
### Architecture
- [x] Separate concerns more clearly ✅
- [x] Implement proper abstraction layers ✅
- [x] Add configuration validation schema ✅
- [x] Improve modularity for easier testing ✅
- [x] Design plugin architecture ✅
## 🗓️ Release Planning
### v0.2.0 (Next Release)
- Enhanced error handling
- Configuration improvements
- Additional LLM providers (Gemini, Cohere)
- Basic testing framework
### v0.3.0
- Interactive mode
- Advanced loop features (conditions, parallel)
- Template system enhancements
- Comprehensive testing
### v1.0.0
- Full feature set
- Complete documentation
- Performance optimization
- Security audit completed
## 🔬 Research Items
### Investigate
- [ ] Best practices for LLM prompt engineering
- [ ] Cost optimization strategies for LLM usage
- [ ] Performance benchmarking of different providers
- [ ] Security best practices for API key management
- [ ] User feedback from early adopters
### Proof of Concepts
- [ ] GUI interface for loop creation
- [ ] Real-time collaboration on loops
- [ ] Mobile app companion
- [ ] Cloud-based loop execution service
- [ ] AI-assisted loop optimization
## 📈 Metrics to Track
### Development Metrics
- Code coverage percentage
- Number of passing tests
- Build time improvements
- Memory usage benchmarks
- API response time improvements
### User Metrics
- Loop creation success rate
- Template usage statistics
- Provider adoption rates
- Error rates by category
- User retention rates
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## Contributing
When contributing to this project:
1. Check this TODO for existing items before creating new ones
2. Update this file when completing or modifying tasks
3. Discuss major changes in issues before implementation
4. Follow semantic versioning for releases
5. Ensure all code passes tests and linting
## Priority Legend
- 🚀 **High**: Core functionality, security, stability
- 🔶 **Medium**: Important features, user experience
- 🔸 **Low**: Nice-to-have features, optimizations
- 📋 **Technical**: Code quality, architecture improvements
- 🗓️ **Planning**: Release planning, research
- 📈 **Metrics**: Tracking and analysis