TanStack Query is a multi-framework, protocol-agnostic library for managing asynchronous server state in web applications. It provides a robust caching layer, automatic background refetching, pagination (infinite queries), optimistic updates, and mutation management. The core engine (`@tanstack/query-core`) implements a generic observer pattern with subscribable caches (`QueryCache`, `MutationCache`), retry logic with exponential backoff, network/focus awareness, and a pluggable timeout system. Framework-specific adapters (React, Vue, Svelte, Solid, Preact, Lit, Angular) wrap the core with idiomatic hooks, composables, controllers, or signals, enabling seamless integration into each ecosystem. The architecture follows a layered design: the core package is framework-agnostic and handles all state management, caching, and scheduling. Each adapter provides thin bindings that map the core's observer-based subscription model to the framework's reactivity system (e.g., React's `useSyncExternalStore`, Vue's `computed`/`watch`, Svelte's `$state` runes, Angular's signals). Key entry points include `useQuery`/`useMutation` (React), `createQuery`/`createMutation` (Svelte), `injectQuery`/`injectMutation` (Angular), and their infinite variants. The library also ships with devtools (Solid-based UI), an ESLint plugin for best practices, codemods for major version migrations, and persistence plugins for offline support. Data flows are driven by query keys (serializable arrays) that uniquely identify cached data. Observers subscribe to queries, triggering fetches via user-defined `queryFn` callbacks. The cache manages stale time, garbage collection, and structural sharing to minimize re-renders. Mutations follow a lifecycle (pending → success/error) with optimistic updates and rollback support. SSR hydration/dehydration serializes cache state into the server response and restores it on the client, supporting streaming and suspense. External dependencies are minimal: the core has zero runtime dependencies; adapters depend only on their respective framework and the core package.
Refine is a headless React framework for building data-intensive applications like admin panels, dashboards, and internal tools. It provides a UI-agnostic core with adapters for Ant Design, Material UI, Mantine, and Chakra UI, plus data providers for REST, GraphQL, Supabase, Strapi, and other backends. The architecture centers around provider patterns (dataProvider, authProvider, etc.) and React Query-based hooks for data fetching and mutations. Key features include automatic CRUD generation via inferencer, undoable notifications, access control, i18n, and live updates. The monorepo structure manages numerous packages and examples, demonstrating a mature ecosystem with active development and comprehensive documentation.
SWR is a React Hooks library for data fetching that implements the stale-while-revalidate cache strategy. It provides a set of hooks (useSWR, useSWRInfinite, useSWRImmutable, useSWRSubscription, useSWRMutation) that enable efficient client-side data fetching with automatic caching, deduplication, revalidation on focus, interval polling, and optimistic updates. The library is designed as a standalone npm package with a modular internal architecture, featuring a middleware system for extensibility, a configurable cache provider, and built-in support for React Suspense and server-side rendering. It is widely used in production and actively maintained by Vercel.
react-query-key-manager is a small, dependency-free TypeScript library that provides type-safe, composable, and collision-free query key management for @tanstack/react-query. It exposes two main functions: `key()` wraps a function returning a query key array, augmenting it to support both direct invocation and zero-argument composition calls; `defineQueryKeys()` creates namespaced key collections with recursive nesting, compile-time validation that all functions are wrapped with `key()`, and runtime duplicate-registration detection in development. The library uses advanced TypeScript techniques (const type parameters, mapped types, conditional types) to enforce API usage at build time and to produce precise IntelliSense types. The code is concise (~60 lines of core logic), well-structured, and fully self-contained. It solves a common pain point in large React Query applications—preventing magic string keys, collisions, and poor type safety—through a clever combination of type-level constraints and runtime utilities. The design philosophy emphasizes zero overhead and copy-pasteability, making it easy to vendor into any project. It is framework-agnostic and works with TypeScript 5.0+ and React Query v4/v5. However, the library is in an early stage (version 0.0.2) and lacks a test suite. The registry and keyNames are module-level singletons, which could cause issues in extreme edge cases like multiple library copies, but for typical usage it is solid. Overall, it is a well-crafted utility that delivers on its promises with a clean, type-safe API.
Fetch is a Scala library for simple and efficient data access, built on cats-effect. It provides a monadic abstraction (`Fetch[F, A]`) that automatically optimizes data fetching through batching, caching, deduplication, and parallel execution. Users define data sources by implementing the `DataSource` trait, specifying how to fetch individual identities or batches. The library then composes fetches using standard monadic and applicative combinators, transparently batching requests to the same source and running independent fetches concurrently. It supports custom caches, optional fetches, error handling, and debugging via a separate `fetch-debug` module. The architecture is a pure library with no runtime dependencies beyond cats-effect, making it suitable for both JVM and Scala.js. Key entry points include `Fetch.apply`, `Fetch.run`, `Fetch.runLog`, and `Fetch.runCache`. External dependencies are minimal: cats-effect, cats-core, and scalatest for testing.