Open Source Go Programming Languages

CUE is an open source data constraint language which aims to simplify tasks involving defining and using data. CUE merges the notion of schema and data. The same CUE definition can simultaneously be used for validating data and act as a template to reduce boilerplate. Schema definition is enriched with fine-grained value definitions and default values. At the same time, data can be simplified by removing values implied by such detailed definitions. The merging of these two concepts enables many tasks to be handled in a principled way. The CUE scripting layer defines declarative scripting, expressed in CUE, on top of data. This solves three problems: working around the closedness of CUE definitions (we say CUE is hermetic), providing an easy way to share common scripts and workflows for using data, and giving CUE the knowledge of how data is used to optimize validation.

Go (Golang) is an open-source programming language designed for simplicity, reliability, and efficiency. Created by Google, it is well-suited for building scalable software, from small utilities to large distributed systems. Go features a clean syntax, strong typing, and built-in concurrency support through goroutines, making it ideal for modern multi-core processors. The language comes with a powerful standard library and tools for testing, formatting, and package management, streamlining development workflows. Go’s compiler produces fast, statically linked binaries with minimal dependencies, ensuring portability and performance. Backed by a large, active community, Go continues to evolve with regular updates and extensive documentation.

starlark-go is the official Go implementation of the Starlark programming language, a Python-like, dynamically typed configuration and scripting language originally developed for the Bazel build system. Starlark combines Python’s expressiveness and readability with deterministic execution, making it ideal for defining build configurations, scripting automation, and embedding domain-specific logic in applications. The Go-based interpreter allows developers to run or embed Starlark programs directly within Go applications, extending them with user-defined scripts and configurable logic. Unlike Python, Starlark supports parallel execution across multiple threads and maintains deterministic behavior, making it suitable for scalable and reproducible workloads. It offers familiar syntax features such as dictionaries, lists, and first-class functions with lexical scoping.

Mangle is a programming language developed by Google for deductive database programming, serving as an advanced extension of Datalog. It is designed to unify and query data from multiple sources in a structured, declarative way while allowing developers to model complex relationships and domain knowledge beyond binary predicates. Mangle enhances traditional Datalog by introducing features such as aggregation, function calls, and optional type-checking, which make it more practical for modern data-intensive applications. Unlike SQL, Mangle’s declarative syntax naturally supports recursion and modular program structure, making it suitable for representing hierarchical or interconnected datasets like dependency graphs or knowledge graphs. The project’s implementation is provided as a Go library, enabling developers to embed Mangle directly into their applications or services, such as database-like gRPC systems.

Skylark, now known as Starlark, is an interpreter for a Python-like language implemented in Go. It is designed as a lightweight, deterministic, and embeddable configuration and scripting language ideal for use within larger applications. Skylark maintains Python’s familiar syntax and high-level data types while omitting features that could cause nondeterminism, such as concurrency and dynamic module imports. The interpreter supports first-class functions, dictionaries, lists, and comprehensions, allowing developers to define reusable logic and structured configuration data. Originally developed for Bazel, Google’s build tool, Skylark enables users to define build rules and macros that extend system functionality. Because it runs deterministically and isolates execution from system state, it’s well-suited for reproducible build systems and other sandboxed environments. The Go implementation focuses on parallel scalability and easy integration into Go-based projects.

OnixOS Arch is a Linux-based and open source distribution. It comes with its own system-based functional programming language (O Language). It offers some customized tools for developers.

Learn Go is a learning-focused repository built to teach Go from first principles through more advanced language and ecosystem features. It takes a course-like approach, starting with environment setup and the “Hello world” basics, then moving into variables, types, formatting, control flow, and functions. From there, it expands into Go’s practical structure for real projects, including modules, packages, and workspaces, plus useful commands for building and working with programs. The content is designed to make Go’s priorities tangible: simplicity, reliability, and efficiency, while still giving you enough depth to write idiomatic code. It also anchors learning in credible references, pointing to official documentation, the Go blog, and widely used learning resources. In practice, it serves as a guided path you can follow linearly or revisit as a reference when you hit common Go questions in day-to-day work.

OK? is a modern, dynamically typed programming language created with a strong opinion: to remove needless complexity and help programmers write code that matters. It eliminates many common language features (e.g., ternary operators) in favour of a consistent, minimal syntax: for example it uses only switch statements for control flow and has only one comparison operator. OK? also treats errors as plain values (strings/arrays) and removes inheritance in favour of what it calls “evolution over composition.” The language emphasises readability and pushing logic out into functions so cases remain simple. It includes concurrency via a map function that executes callbacks in parallel. The project is illustrative of Duffield’s vision: code should feel “magical to write” by removing what is unnecessary.