poml

Why we included this project

Prompts tend to collapse into tangled, single-line strings once they pass a certain complexity, and POML exists to bring that authoring back under control. It uses an HTML-like syntax with semantic elements such as role and task markers, plus components for embedding documents, tables, and images directly into a prompt instead of stitching text together by hand. Styling is separated from the core prompt logic through a CSS-like system, so you can adjust output verbosity or format without rewriting the instructions underneath, a genuine relief when a model proves sensitive to phrasing. A built-in templating engine with variables, conditionals, and loops handles data-driven prompts, and official Python and Node SDKs plus a VS Code extension make it practical to slot into real application workflows. Teams building production LLM features who have grown frustrated with maintaining large prompt files will find this a structured alternative worth evaluating.