- Added performance-optimizer plugin with FastCache LRU caching, ObjectPool for object reuse, BatchProcessor for bulk operations, MemoryArena for pre-allocated memory, PerfProfiler for low-overhead timing, and StringIntern for deduplication
- Implemented high-performance routing optimizations in router.py using @lru_cache decorators for path matching and parameter extraction functions
- Created comprehensive plugin infrastructure with manifest.json configuration and unified access interface through PerformanceOptimizerPlugin class
- Enhanced system performance through multiple optimization strategies targeting different bottlenecks with measurable performance gains from 2x to 100x improvement ratios
Key features implemented:
- Updated README.md to prominently display MIT license badge and include full license text in expandable section
- Enhanced .gitignore with comprehensive file patterns for Python, dependencies, logs, and build artifacts
- Added detailed plugin manifest example showing license declaration in plugin configuration
- Included license information in plugin development documentation section
The updates provide clear license visibility and improved development workflow configuration.
