Hasura GraphQL Engine is an open-source, metadata-driven GraphQL server that provides instant, real-time GraphQL APIs over multiple databases (PostgreSQL, MSSQL, BigQuery, CockroachDB, Citus, and custom sources via Data Connectors). The repository is a large monorepo containing a Rust-based v3 engine, a Go CLI for project management (migrations, metadata, seeds), a React/TypeScript admin console, and community boilerplates for authentication, event triggers, and remote schemas. The system exposes a single GraphQL endpoint that supports queries, mutations, subscriptions, streaming subscriptions, event triggers, cron triggers, remote joins, actions, and Apollo Federation. It relies on external databases, webhook endpoints for auth and event handling, and optionally OpenTelemetry collectors for observability. The architecture is modular: the v3 engine handles metadata resolution, GraphQL schema generation, and execution planning; the CLI manages project lifecycle; and the console provides a GUI for configuration. The codebase is actively maintained with frequent releases, but shows signs of technical drift due to legacy frontend code (Redux vs React Query), dual version support (v2/v3), and multiple programming languages (Rust, Go, TypeScript, Haskell, Python).
OpenCTI is an open-source cyber threat intelligence platform that ingests, manages, and analyzes threat data using the STIX 2.0/2.1 standard. The system is a monorepo containing a Node.js/TypeScript GraphQL backend (opencti-graphql), a React/Relay frontend (opencti-front), a Python client library (client-python), and a Python worker service (opencti-worker). The backend uses Elasticsearch/OpenSearch as its primary data store, Redis for caching and pub/sub, RabbitMQ for connector queues, and PostgreSQL for migrations. The frontend provides full CRUD UIs, dashboards, workspaces, playbook automation, and workflow management. Key entry points include the GraphQL API (resolvers per entity type), the frontend React app with Relay data fetching, and the Python client for programmatic access. Data flows involve ingestion via CSV/JSON/TAXII/RSS feeds, manual creation, connector enrichment, and inference rules that automatically derive relationships. External dependencies include Elasticsearch, Redis, RabbitMQ, MinIO/S3 for file storage, and optional AI services. The platform supports enterprise features (playbooks, PIR, XTM Hub) gated by license checks.
This is a Nuxt 3 module that integrates Apollo GraphQL client into Nuxt applications. It provides a flexible configuration system for defining multiple Apollo clients with HTTP and WebSocket endpoints, authentication token management, and SSR support. The module auto-imports composables like useAsyncQuery, useQuery, useMutation, and useSubscription, and provides hooks for custom authentication and error handling. It also supports hot module replacement for Apollo client configuration files. The architecture uses Nuxt's module system (defineNuxtModule) to hook into the build process. It reads client configurations from nuxt.config or external files, generates runtime templates, and sets up a Nuxt plugin that creates ApolloClient instances with appropriate links (HTTP, WebSocket, auth, error). The composables layer provides SSR-friendly async query wrappers and authentication helpers. WebSocket support includes a restartable client for reconnection. Key entry points are src/module.ts (module setup), src/runtime/plugin.ts (client creation and plugin), and src/runtime/composables.ts (user-facing composables). External dependencies include @apollo/client, @vue/apollo-composable, graphql-ws, and @nuxt/kit.
This repository implements the GraphQL specification for Swift, providing a complete parsing, validation, and execution engine. It is a library intended to be consumed by server‑side Swift frameworks (e.g., Graphiti, graphql‑vapor) or any Swift project requiring GraphQL support. The architecture follows a layered design: a lexical analyzer and recursive‑descent parser produce an AST (Sources/GraphQL/Language/), a rich type system (Sources/GraphQL/Type/) supports all GraphQL types and directives, a validation layer (Sources/GraphQL/Validation/) implements over 20 spec‑required rules, and an execution engine (Sources/GraphQL/Execution/) resolves queries concurrently. Subscriptions are supported via Swift async sequences (Sources/GraphQL/Subscription/). The library also includes schema construction from SDL, schema printing, and a flexible Map value type for dynamic GraphQL values. In production, a client calls the top‑level `graphql()` function with a schema, a query string, and optional variables. The query is parsed, validated against the schema, and then executed field‑by‑field. Resolvers (provided by the schema) produce results that are serialized into a `GraphQLResult` with proper error handling. The library handles all GraphQL features: object types, interfaces, unions, enums, input objects, custom scalars, directives (`@deprecated`, `@specifiedBy`, `@oneOf`), and persisted queries. The test suite mirrors the reference JavaScript implementation and covers the Star Wars schema, introspection, federation, and edge cases. The codebase is well‑structured and follows best practices for Swift libraries: it uses Swift concurrency, Sendable annotations, and the `swift‑collections` package for ordered dictionaries. Several components are self‑contained and could be reused independently, such as the `Map` dynamic value type, the AST visitor infrastructure, and the printer. However, some deviations from the GraphQL spec remain (e.g., a handful of validation rules are commented out), and there is code duplication between `AnyCoder` and `MapCoder`. Force‑unwraps and some thread‑safety workarounds indicate areas that could be hardened. Overall, the library is production‑ready and actively maintained.
This repository is a comprehensive personal R&D platform implementing a polyglot microservices architecture. It hosts a personal website/web application (React/Vite), multiple API servers in different languages (Rust/Axum, Go/Gin/gRPC, Python/FastAPI, Node.js/Express), mobile applications (iOS/Android/React Native), and desktop applications (Qt). The platform includes extensive data processing infrastructure (Kafka, Pulsar, NATS, Spark, Flink), machine learning capabilities (model training/serving/fine-tuning), monitoring and observability tools, and infrastructure as code (Terraform/OpenTofu, Kubernetes). Services communicate via messaging systems and APIs, with a clear separation of concerns and standardized patterns for configuration, health checks, logging, and error handling across language implementations.
Tabby is a self-hosted AI coding assistant that provides code completion, chat, and code search capabilities through multiple interfaces: a web dashboard (Next.js/React), IDE plugins (VS Code, IntelliJ, Eclipse, Vim), and a Language Server Protocol (LSP) agent. The backend is primarily written in Rust, using Axum for HTTP services, Tantivy for indexing, and llama.cpp for local model inference. It supports enterprise features like SSO (OAuth, LDAP), team management, access policies, and background job scheduling for repository indexing, web crawling, and third-party integration (GitHub/GitLab). The system is designed to be self-contained, running without external cloud dependencies, and can operate on consumer-grade GPUs. The architecture follows a monorepo pattern with Rust crates for core services (inference, indexing, git operations, download) and TypeScript/JavaScript for the agent and UI. The web dashboard uses GraphQL (Juniper) for API communication, while IDE plugins communicate via LSP. Data flows include: code completion requests from editors → agent → backend model server → response with postprocessing; chat queries → retrieval-augmented generation (RAG) pipeline using indexed code and documentation; and background jobs for syncing repositories, crawling web docs, and indexing commits/issues/PRs. External dependencies include SQLite (via SQLx), llama.cpp, tree-sitter, and various cloud provider APIs for OAuth. The codebase demonstrates strong engineering practices with modular design, extensive testing (unit tests, golden tests), and reusable patterns such as a background job abstraction, config merging system, and a postprocessing pipeline for code completions. However, the polyglot nature (Rust, TypeScript, Java, Kotlin) and multiple client implementations introduce maintenance complexity and potential drift. The repository is actively maintained with frequent releases and a large community (33k+ stars).
This is a Laravel service provider package that integrates the full-featured Aimeos e-commerce system into Laravel applications. It provides route definitions (admin backend via JQAdm, GraphQL API, JSON REST API, MCP bridge, frontend catalog/checkout/account pages), controllers that delegate to Aimeos HTML/JSONAPI clients, CLI commands for setup, account management, job execution, and cache clearing, and a set of service container bindings (aimeos.shop, aimeos.context, aimeos.config, etc.) that wire Laravel's session, mail, filesystem, and database managers into Aimeos' context. The architecture is that of a thin integration layer: the actual e-commerce logic (products, orders, customers, pricing, basket, checkout, shipping, payments, subscriptions, SEO, multi-vendor, multi-warehouse) lives in the separate aimeos-core and ai-* packages. This package handles Laravel-specific concerns like routing, authentication, configuration merging, and view rendering. It also includes a MCP (Model Context Protocol) bridge that exposes Aimeos admin tools through Laravel's optional MCP SDK, a resolver controller for URL-based content routing (catalog categories, products, CMS pages), and a set of facades for frontend controllers. The code is well-structured, follows PSR-4, uses scoped service bindings, and has comprehensive test coverage.
strawberry-django is a mature, production-grade library that integrates the Strawberry GraphQL framework with Django. It automatically generates GraphQL types, fields, queries, and mutations from Django models, reducing boilerplate significantly. The library provides a powerful query optimizer (DjangoOptimizerExtension) that prevents N+1 queries by introspecting GraphQL selections and applying `select_related`, `prefetch_related`, `only()`, and `annotate()` automatically. It also includes declarative filtering, ordering, pagination (offset and cursor-based Relay connections), a full mutation system with nested input handling and error unions, permission extensions (IsAuthenticated, HasPerm, etc.), authentication helpers, Apollo Federation support, and Django Channels integration for subscriptions. The architecture is modular: core decorators (`@type`, `@input`, `@field`, `@connection`, `@mutation`) build on a flexible field class hierarchy (`StrawberryDjangoField` with mixins for pagination, ordering, filters). The optimizer is the standout feature, using an `OptimizerStore` to collect hints per field and apply them to querysets, with sophisticated prefetch merging and window-function pagination for nested relations. The library is well-tested with over 100 test files covering sync/async, polymorphism, relay, mutations, and edge cases, and includes a comprehensive documentation site. However, it has some technical debt: monkey-patching of Django's `QuerySet._clone`, deprecated legacy ordering/filtering APIs, and tight coupling to Strawberry GraphQL internals (requiring frequent compatibility shims). The codebase is actively maintained with frequent releases and a clear changelog, but the upstream dependency churn introduces medium drift risk.
Coral is an open-source, multi-tenant comment moderation platform developed by Vox Media. It provides a full-stack application with embedded commenting widgets (stream), a comprehensive admin panel for moderation, configuration, and community management, an authentication system supporting local, OAuth2/OIDC, Google, Facebook, SSO, and account management pages. The system architecture consists of a Node.js/Express server with Apollo GraphQL, MongoDB for persistence, Redis for caching and pub/sub, and Bull for job queues. The client uses React with Relay for GraphQL data fetching, and is organized into several bundles: stream (the embeddable comment widget), admin, auth, account, install, count, and embed. The core data flow involves comments being submitted via GraphQL mutations, processed through a modular pipeline (spam detection, word list filtering, toxicity analysis via Perspective API, external moderation phases, and pre-moderation rules), stored in MongoDB, and then distributed via GraphQL subscriptions and notifications (email, Slack, webhooks). The codebase is extensive and well-tested, though it exhibits some legacy patterns (recompose HOCs, partial migration from class components to hooks, duplicated utility code between client and server scripts).
Nellavio is a production-grade, open-source Next.js dashboard starter designed for building modern SaaS products, internal tools, and admin panels. It provides 90+ reusable UI components, 60+ chart variations, authentication (Better Auth) with RBAC, i18n (next-intl), theme switching, and extensive data visualization pages (analytics, orders, customers, products, calendar, charts showcase). The architecture follows App Router with clear separation: server components for data fetching (Apollo Client GraphQL with mock fallback), client components for interactive views, Zustand for global state, and TanStack Table for data tables. The codebase is well-tested with Vitest, Storybook, and CI/CD (GitHub Actions). Despite some hardcoded demo data and recent migrations (Clerk→Better-Auth, Tremor→Recharts), the engineering practices are strong, making it a solid foundation for bootstrapping dashboard-centric applications.
Mythic is a cross-platform, post-exploitation, red teaming command-and-control (C2) framework. It enables operators to manage remote agents (callbacks), generate payloads, issue tasks, exfiltrate files, harvest credentials, and automate workflows through an event-driven system. The architecture is a multi-container Docker deployment: a Go backend (using Gin, RabbitMQ, PostgreSQL, gRPC) handles agent communication, task orchestration, and data persistence; a React frontend (Material-UI, Apollo Client) provides a rich operator interface with real-time updates via GraphQL subscriptions (backed by Hasura); and a CLI tool (Mythic_CLI) manages container lifecycle, configuration, and service installation. Key entry points include the agent message webhook (HTTP POST/GET), web controllers for CRUD operations, and RabbitMQ RPC handlers for internal service communication. External dependencies include RabbitMQ, PostgreSQL, Hasura GraphQL engine, Docker, and gRPC. The system is designed for extensibility through payload type containers, C2 profiles, translation containers, and custom browser scripts.
FoalTS is a full-featured Node.js/TypeScript web framework structured as a monorepo with modular packages (core, CLI, JWT, TypeORM, GraphQL, storage, social auth, etc.). The framework provides declarative routing via decorators, a hook system for middleware/validation/authorization, dependency injection, and built-in support for OpenAPI, GraphQL, WebSockets, and file storage. Under the hood it uses Express, AJV, and TypeORM, with session stores for Redis, MongoDB, and SQL databases. The CLI generates projects, controllers, entities, and REST APIs, and includes upgrade commands across major versions. The codebase demonstrates solid engineering: thorough unit/e2e tests (over 2100 tests), documentation-as-tests, and a consistent pattern of abstract services (Store, Disk, Logger) resolved via configuration. Key reusable patterns include the hook/decorator system for validation and authentication, the abstract OAuth2 provider base class, and the CLI generator template engine. However, some deprecations (e.g., hashPassword→PasswordService) and outdated Docker images in development config indicate moderate technical drift. The project is actively maintained with frequent releases, daily CI on Node 22/24, and a well-documented upgrade path.
graphql-hooks is a lightweight React GraphQL client library that provides a set of hooks for querying, mutating, and subscribing to GraphQL endpoints. It is designed as a minimal yet feature-rich alternative to Apollo Client, with a focus on small bundle size (22.9 kB gzipped). The library is organized as a Lerna monorepo containing the core package (`graphql-hooks`), an in-memory cache (`graphql-hooks-memcache`), SSR utilities (`graphql-hooks-ssr`), and a Babel plugin for compile-time GraphQL query extraction (`babel-plugin-extract-gql`). In production, the system operates as a client-side GraphQL executor within React applications. The primary entry points are React hooks: `useQuery`, `useMutation`, `useManualQuery`, and `useSubscription`. These hooks internally use `GraphQLClient` to send HTTP or WebSocket requests, passing through an optional middleware pipeline. The client supports caching via a pluggable interface (defaulting to `graphql-hooks-memcache` with LRU eviction and TTL), automatic refetching after mutations, file uploads via the GraphQL multipart spec, and server-side rendering by pre-fetching query results and injecting initial state into the HTML. Key data flows begin when a React component calls a hook. The hook interacts with `GraphQLClient`, which applies middleware (e.g., Automatic Persisted Queries or custom interceptors) before sending the request. Responses are cached and returned to the component. Mutations can optionally trigger refetches of related queries via an event emitter. The architecture promotes separation of concerns via middleware, a clean cache API, and support for multiple transport layers (HTTP, WebSocket, Full-WS). External dependencies include React (peer), `graphql-ws` or `subscriptions-transport-ws` for subscriptions, and `formdata-node` for file uploads in Node environments.
This repository provides syntax highlighting for GraphQL files in Xcode. It consists of an Xcode plugin that defines a file type for .graphql files and a language specification that provides syntax coloring. The plugin adapts to the deprecation of the Xcode plugin mechanism by focusing on the file type and language specification components, which still work in recent Xcode versions. The core component, GraphQL.xclangspec, is a well-crafted language definition that covers the full GraphQL syntax.
This is an npm library that provides the GitHub GraphQL schema in JSON and IDL formats, along with a validation function and TypeScript type definitions. It is used by other Octokit libraries to validate GraphQL queries against the official GitHub GraphQL API. The schema is automatically updated hourly via a GitHub Action that fetches the latest schema from GitHub and creates pull requests for changes. The library is simple but robust, relying on the `graphql` package for validation and `graphql-codegen` for TypeScript types. It is not a data research project; it is a straightforward software library.
Photonix is a self-hosted, full-stack photo management application with AI-powered classification and search. The backend is a Django monolith exposing a GraphQL API, with background workers for importing, thumbnail generation, raw processing, and ML classification (color, objects, faces, location, style, events). The frontend is a React 19 single-page app built with TanStack Router, Apollo Client, Zustand state management, and a dark-themed shadcn/ui component library. The system supports multi-user libraries, invitations, map view, albums, and fine-grained search with facets. The architecture relies on a PostgreSQL database, Redis for task coordination and locks, and supervisor to manage numerous background processes. While the project shows solid engineering and a modern tech stack, it carries some technical debt from vendored ML models, TensorFlow 1.x compatibility, and hardcoded download URLs, and is explicitly not yet feature-complete.
This repository is a comprehensive library of custom GraphQL scalar types, intended to supplement the base GraphQL specification with precise, type-safe scalars for common data formats (e.g., UUID, DateTime, EmailAddress, PhoneNumber, IBAN, GeoJSON, ISBN, etc.). It exports over 80 scalar definitions as `GraphQLScalarType` instances, along with corresponding SDL type definitions, mock generators, and JSON Schema extensions. The architecture is clean and uniform: each scalar is implemented as a self-contained module with `serialize`, `parseValue`, and `parseLiteral` methods, leveraging a shared validation pattern via `createGraphQLError`. The library integrates seamlessly with GraphQL codegen via `extensions.codegenScalarType` and provides a `RegularExpression` factory for creating custom regex-based scalars. Production usage is as a dependency for GraphQL servers (Apollo, Yoga, etc.) where precise input validation and output formatting are required. The codebase is well-tested with per-scalar unit tests, integration tests for date/time scalars, and bundle-size checks. While the majority of scalars are simple regex or range validations, several modules (GeoJSON, IBAN, iso-date) demonstrate non-trivial validation logic and are safely reusable. The repository also includes a Next.js documentation website and automated release tooling via Changesets.
Animite is an unofficial Android client for AniList (with partial MyAnimeList support) for tracking anime and manga. The application is built with a modern Android stack: Kotlin, Jetpack Compose, Material3, Apollo Kotlin (GraphQL), Ktor (REST), DataStore, Hilt, and Coil. It features a multi-module architecture with clear separation of concerns: API layers, core UI components, navigation, and feature modules (explore, profile, media, settings, social, anime, manga, banner). The app supports browsing, searching, filtering, detailed media pages with dynamic theming (time-of-day based banner), user profile management, and list editing with drag-and-drop reordering. The codebase demonstrates strong modularity and reusable UI patterns, though it relies heavily on experimental Compose APIs and has a few code quality concerns (e.g., hardcoded credentials, large screen files, alpha version instability).
This repository provides two custom GraphQL scalar types, `JSON` and `JSONObject`, for use with GraphQL.js. The library is implemented in a single source file that defines `GraphQLJSON` and `GraphQLJSONObject` as instances of `GraphQLScalarType`. The `JSON` scalar accepts any JSON-serializable value (scalar, array, object) while `JSONObject` restricts to plain objects. The implementation handles serialization, variable parsing, and literal parsing from GraphQL queries, including recursive handling of nested objects and arrays. The code is well-structured, includes comprehensive tests covering serialization, parseValue, and parseLiteral for both scalars, and provides TypeScript type definitions. The library is a clean, focused utility for GraphQL schemas that need to handle arbitrary JSON data.
This repository is a polyglot web converter application built with Next.js. It provides a large collection of code transformations (e.g., JSON to TypeScript, SVG to JSX, GraphQL to Resolvers, HTML to Pug, etc.) in a user-friendly two-pane editor interface. The architecture is a monolith with each transformation implemented as a separate page under `pages/`, all sharing a common `ConversionPanel` component that manages the input/output editors, web workers for heavy processing, and server-side API routes for a few transforms that require Node.js native modules. External dependencies include numerous npm libraries for the actual conversion logic (e.g., `json_typegen_wasm`, `@svgr/core`, `graphql-codegen`), the Monaco editor for code editing, and the evergreen-ui component library for the UI. The application uses web workers extensively (via a custom `workerWrapper` utility) to offload CPU-intensive conversions off the main thread, ensuring a responsive UI. Data flows from the user's input editor, through a transformer function (which may call a web worker or an API endpoint), and into the output editor, with results automatically formatted by Prettier. The codebase is well-structured with a consistent pattern for each converter, making it straightforward to add new transformations.