Uyarlr

Uyarlr is a modular software framework for adaptive content rendering and data transformation. It provides a policy-driven engine that adapts output to user context, language, device capabilities, and accessibility requirements. The framework is designed to be extensible through plug-ins and supports multiple output formats, including HTML, JSON, and plain text.

Architecture: At its core is a lightweight runtime that evaluates adaptation rules written in a declarative policy language. It uses a pipeline model in which content passes through a sequence of transformers and renderers, allowing locale, accessibility, and device-specific adaptations to be composed from reusable modules.

Features: The rule engine supports context predicates and resource localization, with a plugin system for data sources, localization catalogs, and rendering backends. It emphasizes deterministic output and testability, offering dry-run modes and snapshot testing.

History and reception: Development began as an open-source initiative in the mid-2020s. After initial releases, it gained modest adoption among content-management and web-app teams seeking declarative adaptation. Critics point to a steep learning curve for complex rule sets.

Usage: Developers integrate uyarlr into applications via a lightweight runtime and configure adaptation rules in a YAML- or JSON-based policy file. Content authors describe context dependencies and output formats; the runtime then renders outputs appropriate for the target audience.