Developed an extensible subclass loading mechanism for the BerriAI/litellm repository, enabling seamless integration of custom LLM subclasses through Python entry points specified in pyproject.toml. This work focused on enhancing the framework’s flexibility by implementing entry-point discovery and integrating it directly with the core loader, laying the groundwork for future plugin-based extensions. The approach leveraged Python’s dynamic import capabilities and emphasized robust software architecture and testing practices to ensure maintainability. By streamlining the process for third-party model integration, the contribution addressed a key extensibility challenge and prepared the codebase for scalable, modular enhancements in subsequent development cycles.