Worked on the browser-use/browser-use repository to implement startup logging for active language models, focusing on improving traceability and debugging during agent initialization. The approach centered on adding clear, auditable instrumentation that records which language models are active at startup, providing a reliable source of truth for LLM activity. Using Python and leveraging skills in AI integration, backend development, and logging, the changes were designed with minimal surface-area impact to facilitate future enhancements and rollbacks. This targeted feature enhanced observability for language model usage without introducing unnecessary complexity, supporting better analytics and maintainability for ongoing development and operational monitoring.