Worked on enhancing reliability and observability for the BerriAI/litellm repository by addressing a critical bug related to error response handling. Focused on backend development and API integration using Python, the work ensured that provider-specific headers are preserved in error responses, which improves debugging and accelerates issue triage when LLM provider requests fail. The approach involved careful error handling and comprehensive testing to validate that existing API compatibility and successful request behavior remained unaffected. By retaining relevant headers across all error paths, the changes reduced the time required to identify provider-related failures and contributed to the overall stability of the system.