Open Source ChromeOS Libraries - Page 45
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Libraries for ChromeOS
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Go QueryString
go-querystring is Go library for encoding structs into URL query
go-querystring is a lightweight Go library developed by Google for encoding Go structs into URL query strings. It simplifies the process of constructing URLs with typed parameters, offering a clean, type-safe approach to building query strings programmatically. The library’s query package exposes a single Values() function that converts struct fields (tagged with url tags) into properly encoded query parameters. It is particularly useful for APIs or HTTP clients that represent query parameters as structs—such as in the go-github client library. Each struct field can be annotated with a url:"name" tag to specify the query key. The encoder supports standard Go data types (strings, numbers, booleans, slices, etc.) and handles formatting and escaping automatically. -
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Go canvas
Canvas is a Go drawing library based on OpenGL
Canvas is a high-performance 2D rendering library for C++ that mimics the HTML5 Canvas API. It is built using OpenGL for hardware acceleration and offers a simple, familiar interface for developers looking to draw shapes, images, and text in desktop applications or games. Canvas focuses on ease of use, speed, and flexibility, making it suitable for GUI overlays, visualizations, and real-time 2D rendering tasks. Its API is intentionally similar to JavaScript’s Canvas API, lowering the barrier for web developers transitioning into C++ graphics work. -
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Go gRPC Middleware
Golang gRPC Middlewares: interceptor chaining, auth, logging, retries
gRPC Go has support for "interceptors", i.e. middleware that is executed either on the gRPC Server before the request is passed onto the user's application logic, or on the gRPC client either around the user call. It is a perfect way to implement common patterns: auth, logging, tracing, metrics, validation, retries, rate limiting, and more, which can be great generic building blocks that make it easy to build multiple microservices. Especially for observability signals (logging, tracing, metrics) interceptors offer semi-auto-instrumentation that improves the consistency of your observability and allows great correlation techniques (e.g. exemplars and trace ID in logs). Demo-ed in examples. This repository offers ready-to-use middleware that implements gRPC interceptors with examples. In some cases, dedicated projects offer great interceptors, so this repository skips those, and we link them in the interceptors list. -
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GoNB
GoNB, a Go Notebook Kernel for Jupyter
Go is a compiled language, but with very fast compilation, that allows one to use it in a REPL (Read-Eval-Print-Loop) fashion, by inserting a "Compile" step in the middle of the loop -- so it's a Read-Compile-Run-Print-Loop — while still feeling very interactive. GoNB leverages that compilation speed to implement a full-featured (at least it's getting there) Jupyter notebook kernel. As a side benefit it works with packages that use CGO — although it won't parse C code in the cells, so it can't be used as a C kernel. It already includes many goodies: cache between cell of results, contextual help and auto-complete (with gopls), compilation error context (by mousing over), bash command execution, images, html, etc. See the tutorial. It's been heavily used by the author (in developing GoMLX, a machine learning framework for Go), but should still be seen as experimental — if we hear success stories from others, we can change this. -
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Golden Layout
A multi window layout manager for webapps
Golden Layout is a JavaScript layout manager designed for building complex, multi-panel web applications with draggable and resizable components. It enables developers to arrange UI elements into flexible layouts that can be dynamically reconfigured by users through drag-and-drop interactions. The system is particularly useful for dashboards, IDE-like interfaces, and data-heavy applications where multiple panels need to coexist and be rearranged. Golden Layout manages component positioning, visibility, and resizing through a structured layout tree, allowing precise control over how elements are displayed. It also supports saving and restoring layouts, enabling persistence of user configurations across sessions. The library integrates with popular frontend frameworks such as Angular and Vue, and includes support for virtual components to bridge framework-specific rendering models. -
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Google Cloud Dataflow Template Pipelines
Cloud Dataflow Google-provided templates for solving data tasks
DataflowTemplates is the source repository for Google-provided Dataflow templates that are intended to solve large-scale in-cloud data processing tasks without requiring users to build everything from scratch in a full development environment. The repository is centered on templated pipelines powered by Google Cloud Dataflow and Apache Beam, making it easier to run common integration and movement jobs such as data import, export, backup, restore, and bulk API operations. Its structure shows support for multiple generations of templates, including v1 and v2 implementations, as well as related metadata, YAML assets, plugins, and Python components that support broader template execution and maintenance. This design makes the project more than a sample set, because it acts as the implementation base for official Google-provided templates used in real cloud data workflows. -
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Google Cloud Platform Go Samples
Sample apps and code written for Google Cloud
Google Cloud Platform Go Samples repository is a comprehensive collection of Go-based code examples that demonstrate how to build applications and services using Google Cloud Platform. It provides developers with practical implementations that cover a wide spectrum of cloud functionalities, including storage, compute, networking, and machine learning services. Each sample is designed to be easily reusable, allowing developers to copy code directly into their own projects as a starting point for development. The repository includes both simple quickstart examples and more advanced application patterns, often accompanied by documentation guides that explain how to deploy and run them in different environments. It also showcases integrations with services such as Pub/Sub, BigQuery, Cloud Functions, and Vertex AI, helping developers understand how to orchestrate distributed systems in Go. -
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Google Cloud Platform Java Samples
Java and Kotlin Code samples used on cloud.google.com
Java and Kotlin Code samples used on cloud.google. This repository holds sample code written in Java that demonstrates the Google Cloud Platform. Every file containing source code must include copyright and license information. This includes any JS/CSS files that you might be serving out to browsers. -
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Google Cloud Platform Node.js Samples
Node.js samples for Google Cloud Platform products
Google Cloud Platform Node.js Samples repository is a large set of Node.js code examples that demonstrate how to build, deploy, and manage applications using Google Cloud Platform services. It mirrors the structure and purpose of the Python and Go sample repositories, providing developers with practical implementations that complement official documentation. The repository includes examples for a wide variety of services, such as Cloud Run, App Engine, storage systems, and APIs, along with full tutorial applications like the Bookshelf app that showcase end-to-end workflows. Each sample includes setup instructions, dependency installation steps, and execution commands, making it easy for developers to run and modify the code locally. The project also supports modern JavaScript and TypeScript workflows, reflecting current development practices in the Node.js ecosystem. -
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Google Cloud Platform Python Samples
Code samples used on cloud.google
Google Cloud Platform Python Samples repository is a large, curated collection of Python code examples that demonstrate how to use a wide range of Google Cloud services in real-world scenarios. It serves as a practical companion to official documentation, providing runnable snippets that illustrate how to authenticate, configure environments, and interact with APIs across products such as storage, AI services, and data processing tools. The repository is organized into product-specific directories, allowing developers to quickly locate examples relevant to their use case and adapt them into production workflows. It emphasizes hands-on learning by guiding users through setup steps such as creating virtual environments, installing dependencies, and running scripts locally. These samples are designed to accelerate development by showing best practices for connecting services, handling data, and managing cloud resources programmatically. -
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Google Cloud Vision API examples
Sample code for Google Cloud Vision
The cloud-vision repository is a sample code collection for the Google Cloud Vision API that shows developers how to implement image analysis tasks across a wide range of languages and platforms. It contains examples organized by language and environment, including Go, Java, Node.js, PHP, Python, Ruby, .NET, Android, iOS, and even a Chrome extension, which makes it especially valuable as a cross-platform learning resource. The repository demonstrates concrete image understanding use cases, such as landmark detection and mobile photo analysis with label and face detection, so developers can see how Vision API outputs are consumed in real interfaces and workflows. Although the repository has been marked as deprecated in favor of language-specific repositories for new work, it still serves as a broad reference hub for legacy examples and multi-language implementation patterns. -
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Google IPs
Public IP address ranges associated with Google infrastructure
Google-IPs aggregates public IP address ranges that are associated with Google’s infrastructure, collecting them in machine-readable formats useful for routing and firewall rules. The list is helpful for administrators who need to whitelist Google endpoints, analyze traffic, or tune proxies and CDN configurations. By centralizing ranges that are otherwise spread across announcements and registries, it saves time and reduces misconfiguration risk. The repository typically includes CIDR blocks and scripts or instructions for consuming them on various platforms. It’s useful in both enterprise networks and homelab setups where precise rules are preferred over broad allowances. Because IP allocations evolve, the project aims to make refreshing and automating the data straightforward. -
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Google Kubernetes Engine (GKE) Samples
Sample applications for Google Kubernetes Engine (GKE)
Google Kubernetes Engine (GKE) Samples repository is a comprehensive collection of sample applications and reference implementations designed to demonstrate how to build, deploy, and manage workloads on Google Kubernetes Engine (GKE). It serves as a practical companion to official GKE tutorials, providing real, runnable code that illustrates how containerized applications are packaged, deployed, and scaled within Kubernetes clusters. The repository is organized into multiple categories such as AI and machine learning, autoscaling, networking, observability, security, and cost optimization, allowing developers to explore specific use cases and architectural patterns. It includes both simple quickstart examples, like basic “hello world” applications, and more advanced scenarios such as migrating monolithic applications to microservices, implementing service meshes, and configuring custom autoscaling metrics. -
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GopenPGP V3
A high-level OpenPGP library
GopenPGP V3 is a high-level OpenPGP cryptographic library developed by ProtonMail that provides a user-friendly API for common encryption and signing operations in Go, abstracting the complexity of the underlying OpenPGP standards and golang crypto primitives. This library lets developers perform key generation, message encryption and decryption, digital signing, and signature verification with straightforward functions that hide much of the boilerplate and nuance typically required when working directly with OpenPGP implementations. Built on top of a forked version of the Go crypto library, gopenpgp supports current OpenPGP RFC standards and includes examples for working with password-based encryption and PGP keys, as well as detached and inline signatures. Because it’s designed for broad use, the library also targets go-mobile compatibility, meaning it can support mobile app use cases alongside server and desktop tooling. -
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Gossamer
Go Implementation of the Polkadot Host
Gossamer is a modular blockchain framework implemented in Go, serving as an execution environment for the Polkadot runtime. It allows developers to build and run nodes for various blockchain protocols within the Polkadot ecosystem. Gossamer's design emphasizes modularity and flexibility, enabling customization and integration of different services such as networking, block production, and JSON-RPC servers. -
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GraalPy
A Python 3 implementation built on GraalVM
GraalPy is a high-performance implementation of the Python language for the JVM built on GraalVM. GraalPy is a Python 3.11 compliant runtime. It has first-class support for embedding in Java and can turn Python applications into fast, standalone binaries. GraalPy is ready for production running pure Python code and has experimental support for many popular native extension modules. -
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Grabana
User-friendly Go library for building Grafana dashboards
Grabana provides a developer-friendly way of creating Grafana dashboards. Whether you prefer writing code or YAML, if you are looking for a way to version your dashboard configuration or automate the tedious and error-prone creation of dashboards, this library is meant for you. -
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Gradle Advanced Build Version Plugin
A plugin to generate the Android version code and version name
A plugin to generate the Android version code and version name automatically based on git commits number, date and Semantic Versioning. -
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Gradle Bintray Plugin
The Gradle Bintray Plugin allows you to publish artifacts to Bintray
The Gradle Bintray Plugin allows you to publish artifacts to Bintray. The plugin supports three methods to create groups of artifacts: Configurations, Publications and Copying specific files using filesSpec. One of the methods should be used to group artifacts to be uploaded to Bintray. Using the Configurations approach is the easiest, since this option attempts to infer what artifacts to upload based on the Gradle project and dependencies that are defined. Publications gives more fine-grained control, especially when needing to publish metadata for publishing to Maven Central. Copying specific files can be used as a last option, which provides the ability to define custom rules using the Gradle's CopySpec task. In general, the first two options should be sufficient for your needs. -
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Gradle License Plugin
Gradle plugin that provides a task to generate a HTML license report
The Gradle License Plugin helps developers manage and audit software licenses for dependencies in Android and Java projects. It automatically detects third-party libraries and generates a report containing license details. This is particularly useful for ensuring compliance with open-source licenses, making it easier for teams to identify potential legal issues or attribution requirements before releasing software. -
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GradlePluginDevelop
Gradle execution process
A skeleton or demonstration project for developing Gradle plugins. It contains sample plugin code, project structure, examples of applying plugin logic, extension configuration, and build integration for Gradle plugin development. Gradle execution process, what is DSL, domain-specific language, common usage of Gradle, usage of Gradle advanced plug-ins, Gradle optimization for Android, using Javassist to the next floor, and problems encountered in Gradle development. Demonstration of plugin application in other modules. Plugin configuration and metadata. -
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Grafana Operator
An operator for Grafana that installs and manages Grafana instances
The Grafana Operator is a Kubernetes operator built to help you manage your Grafana instances and its resources in and outside of Kubernetes. Whether you’re running one Grafana instance or many, the Grafana Operator simplifies the processes of installing, configuring, and maintaining Grafana and its resources. Additionally, it's perfect for those who prefer to manage resources using infrastructure as code or using GitOps workflows through tools like ArgoCD and Flux CD. -
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GraphHopper Routing Engine
Open source routing engine for OpenStreetMap
GraphHopper invests in an active open source community. Our flagships are the GraphHopper routing engine and jsprit, the toolkit for solving rich vehicle routing problems. We promote a fair & diverse mindset. jsprit is a Java based toolkit for solving rich traveling salesman problems (TSP) and vehicle routing problems (VRP). It is lightweight, flexible, easy-to-use, and based on a single all-purpose meta-heuristic. The GraphHopper routing engine is fast and memory-efficient, using Java. It runs server side, offline on Android and iOS. It uses OpenStreetMap data, but can import other data sources as well. GraphHopper is a fast and memory-efficient routing engine released under Apache License 2.0. It can be used as a Java library or standalone web server to calculate the distance, time, turn-by-turn instructions and many road attributes for a route between two or more points. Beyond this "A-to-B" routing it supports "snap to road", Isochrone calculation, mobile navigation and more. -
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GraphQL Java Tools
A schema-first tool for graphql-java inspired by graphql-tools for JS
This library allows you to use the GraphQL schema language to build your graphql-java schema. Inspired by graphql-tools, it parses the given GraphQL schema and allows you to BYOO (bring your own object) to fill in the implementations. GraphQL Java Tools works extremely well if you already have domain POJOs that hold your data (e.g. for RPC, ORM, REST, etc) by allowing you to map these magically to GraphQL objects. GraphQL Java Tools aims for seamless integration with Java, but works for any JVM language. Try it with Kotlin. -
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GraphQL Juniper
GraphQL server library for Rust
GraphQL is a data query language developed by Facebook intended to serve mobile and web application frontends. Juniper makes it possible to write GraphQL servers in Rust that are type-safe and blazingly fast. We also try to make declaring and resolving GraphQL schemas as convenient as Rust will allow. Juniper does not include a web server - instead it provides building blocks to make integration with existing servers straightforward. It optionally provides a pre-built integration for the Actix, Hyper, Iron, Rocket, and Warp frameworks, including embedded Graphiql and GraphQL Playground for easy debugging. The best place to get started is the Juniper Book, which contains guides with plenty of examples, covering all features of Juniper. Juniper supports the full GraphQL query language according to the specification (October 2021), including interfaces, unions, schema introspection, and validations. It can also output the schema in the GraphQL Schema Language.
