Developed distributed runtime support for the LangGraph CLI, enabling users to choose between combined queue worker and distributed execution modes to better accommodate scalable workloads. The work involved implementing CLI changes, enhancing configuration management, and generating Dockerfiles to support the new runtime architecture within the langchain-ai/langgraph repository. Using Python and Docker, the developer focused on reliability and deployment flexibility, ensuring the new features integrated smoothly with existing workflows. No major bugs were addressed during this period, as the emphasis remained on delivering robust feature functionality and improving onboarding for distributed workloads through careful testing and disciplined Git-based release practices.