Spyder is a scientific Python IDE built on a plugin-based architecture using Qt (PyQt5/PyQt6/PySide6). It provides a multi-language code editor with Language Server Protocol (LSP) integration for completions, diagnostics, and formatting, an IPython/Jupyter console with custom kernel management, a variable explorer for inspecting NumPy/Pandas objects, a debugger, profiler, and a help system that renders documentation with MathJax for mathematical notation. The main entry point is `spyder.app.start:main`, which initializes the main window and loads plugins discovered via setuptools entry points under the `spyder.plugins` group. Each plugin (e.g., Editor, IPythonConsole, VariableExplorer) is a self-contained module with its own configuration page, container widget, and status bar integration. External dependencies include `spyder-kernels` (custom Jupyter kernels), `python-lsp-server` (LSP backend), `qtconsole` (Qt-based console widget), and `spyder-remote-services` (remote kernel execution via JupyterHub). Data flows: user edits code in the editor, which communicates with the LSP server for completions and diagnostics; running code sends it to the IPython kernel, which executes and returns results displayed in the console and variable explorer. The architecture is a monolith with a formal plugin API that supports lifecycle hooks, dependency management, and configuration observers.
Elyra is a JupyterLab extension suite that provides a visual pipeline editor for building, validating, and executing multi-step AI/ML workflows on Kubeflow Pipelines, Apache Airflow, or locally. It includes enhanced script editors (Python, R, Scala) with kernel management and debugging, a code snippet manager, and a metadata-driven system for runtime configurations, runtime images, and component catalogs. The backend (Python) handles pipeline parsing, validation, code generation via Jinja2 templates, and submission to remote orchestrators, while the frontend (TypeScript/React) delivers a rich UI with drag-and-drop, property editing, and real-time validation. The architecture is modular with pluggable runtime processors, component catalog connectors, and metadata storage backends, making it extensible for custom runtimes and data sources. Although primarily an application, it serves the MLOps domain by orchestrating data science notebooks and scripts as reproducible pipelines.