LUML is an agent-native MLOps platform that orchestrates the full machine learning lifecycle: experiment tracking, model packaging, registry, deployment, monitoring, and AI-mediated code generation. The monorepo contains multiple services: a FastAPI backend (multi-tenant platform API with PostgreSQL), a Vue 3 frontend (SPA with Pinia, PrimeVue), a satellite runtime for edge deployment and monitoring (Docker/Kubernetes), an SDK for experiment tracking and model packaging (supporting sklearn, XGBoost, LangGraph, LLM evaluation), a Prisma service for agent-driven code generation (Claude/Codex orchestrator with git worktree isolation), a local experiment tracker (lumlflow) with both a Textual TUI and web interface, and WASM-based in-browser ML workers (prompt optimization, tabular forecasting). The architecture is microservice-oriented with a shared SDK and wire contracts. The platform is production-grade with comprehensive testing (unit, integration, E2E), CI/CD pipelines, OpenAPI contracts, and a well-defined RBAC permission matrix. It demonstrates high craftsmanship in areas like monitoring drift computation (PSI, multivariate), WebWorker RPC proxies, and LLM judge scoring, though the sheer size and number of packages introduce some coordination complexity.