Open Source Go AI Coding Tools

OpenCode is a terminal-based AI coding assistant designed to bring powerful AI models directly into your development workflow. Built in Go, it offers a smooth TUI (Terminal User Interface) experience powered by Bubble Tea, enabling seamless interaction without leaving the command line. It supports multiple AI providers, including OpenAI, Anthropic Claude, Google Gemini, AWS Bedrock, Groq, Azure OpenAI, and GitHub Copilot. Developers can use it for intelligent code completion, debugging, file editing, and session management while staying entirely within their terminal environment. With support for the Language Server Protocol (LSP), OpenCode enhances code intelligence across multiple languages. Although archived, the project has evolved into Crush, actively maintained by the Charm team.

The main Kite repo (originally kiteco/kiteco) was intended for private use. It has been lightly adapted for publication here by replacing private information with XXXXXXX. As a result, many components here may not work out of the box. We used a variety of infrastructure, on a mix of cloud platforms, depending on what was most economical, though it was mostly on AWS. You should be able to develop, build, and test Kite entirely on your local machine. However, we do have cloud instances & VMs available for running larger jobs and for testing our cloud services. We bundle a lot of pre-computed datasets & machine learning models into the Kite app through the use of a custom filemap & encoding on top of go-bindata. The data, located in kite-go/client/datadeps, is kept in Git-LFS.

Crush is a next-generation, terminal-based AI coding assistant developed by Charm, designed to seamlessly integrate with your tools, workflows, and preferred LLMs. It provides developers with an intuitive, session-based experience where multiple contexts can be managed across projects. With flexible model switching, Crush allows you to change providers mid-session while retaining conversation history. It enhances productivity by combining LSP (Language Server Protocol) support with extensible MCP (Model Context Protocol) integrations for richer coding context and external tool connectivity. Built for portability, it offers first-class support across macOS, Linux, Windows (PowerShell and WSL), and BSD systems. Backed by the Charm ecosystem, Crush is a stable, actively maintained evolution of the original OpenCode project.

Crush is an open-source, agentic code assistant that combines powerful AI capabilities with a flexible tool execution environment, enabling developers to extend and customize how AI interacts with codebases. Designed around the Agent Skills open standard, crush lets users install and manage reusable skill packages that teach the agent how to perform specific tasks, from browsing files to editing and transforming code. When first initialized in a project, it analyzes the codebase and generates contextual knowledge files that help the agent become more effective over time. By default, crush prompts for permission before executing tool calls for safety, but this behavior can be configured, and users can enable or disable specific tools to refine capabilities. It also supports custom provider configurations for different large language models and integrations, helping developers tailor the system to their workflow preferences.

Qwen2.5-Coder, developed by QwenLM, is an advanced open-source code generation model designed for developers seeking powerful and diverse coding capabilities. It includes multiple model sizes—ranging from 0.5B to 32B parameters—providing solutions for a wide array of coding needs. The model supports over 92 programming languages and offers exceptional performance in generating code, debugging, and mathematical problem-solving. Qwen2.5-Coder, with its long context length of 128K tokens, is ideal for a variety of use cases, from simple code assistants to complex programming scenarios, matching the capabilities of models like GPT-4o.

AWS Copilot is an open-source command-line interface that makes it easy for developers to build, release, and operate production-ready containerized applications on AWS App Runner, Amazon ECS, and AWS Fargate. Run a single command to quickly get started with a containerized application using best practices on AWS from a Dockerfile. Instead of modeling individual resources, Copilot provides common cloud architectures, request-driven web service, load-balanced web service, backend service, worker service, and scheduled job. The necessary infrastructure is generated from the chosen pattern. Focus your time on writing business logic instead of connecting AWS resources. No need to worry about gluing Copilot commands in a script to create an automated release process. Copilot provides commands to create multiple deployment environments in separate AWS accounts and regions, as well as creating an AWS CodePipeline pipeline to build your container images.

Defang is a developer-centric platform that simplifies the process of developing, deploying, and debugging cloud applications. By leveraging AI-assisted tooling, Defang enables developers to swiftly transition from an idea to a deployed application on their preferred cloud provider. The platform supports multiple programming languages, including Go, JavaScript, and Python, allowing developers to start with sample projects or generate project outlines using natural language prompts. With a single command, Defang builds and deploys applications, handling configurations for computing, storage, load balancing, networking, logging, and security. The Defang Command Line Interface (CLI) facilitates interactions with the platform, offering installation options via shell scripts, Homebrew, Winget, Nix, or direct download. Developers can define services using compose.yaml files, which Defang utilizes to deploy applications to the cloud.

aiac is a command line tool to generate IaC (Infrastructure as Code) templates, configurations, utilities, queries and more via OpenAI's API. The CLI allows you to ask the model to generate templates for different scenarios (e.g. "get terraform for AWS EC2"). It will make the request, and store the resulting code to a file, or simply print it to standard output. By default, aiac uses the same model used by ChatGPT, but allows using different models.

Reviewpad is a service to automate pull requests and issues workflows. The workflows are specified in a YML-based configuration language described in the official documentation. In Reviewpad, you can automate actions over the pull requests and issues. With technological progress depending on the human ability to create and modify software systems, Reviewpad was founded to allow developers to safely contribute to any codebase as fast as possible. Developers deserve a better experience and that goal is what has driven our work at Reviewpad. At the core of this experience is security – developers should be able to go faster without increasing the risk of introducing changes that can hurt their products, users, and organizations.