Report Abuse

Basic Information

Codai is an AI-powered code assistant delivered as a session-based command-line tool for developers. It is designed to analyze a project's full context and help with everyday development tasks such as adding features, refactoring, writing tests, performing detailed code reviews and suggesting bug fixes. The tool summarizes project context using Tree-sitter so it can send concise signatures rather than full implementations to LLMs and retrieve full sections on demand to save tokens. Codai supports multiple LLM providers and models, allows zero-setup usage via an API key, and can be configured per project with a codai-config.yml file. It also provides a .codai-gitignore to exclude files from analysis. The repository includes installation instructions using go install and guidance for both cloud and local model usage.

Links

Categorization

App Details

Features
Codai offers context-aware code completions and the ability to add new features or test cases. It supports automated refactoring and code structure improvements and can describe and suggest bug fixes. The assistant provides code review assistance, generates documentation, and can accept and apply AI-generated code changes across multiple files. It tracks token consumption per request and summarizes full project context with Tree-sitter to reduce token usage. The tool works with many programming languages including C#, Go, Python, Java, JavaScript and TypeScript. It supports a variety of LLM providers and models including OpenAI, Azure, Anthropic, Gemini, Qwen, Mistral, DeepSeek and local models like Ollama. Configuration is adjustable via a YAML file or environment variables.
Use Cases
For developers Codai streamlines common coding workflows by maintaining an understanding of the entire codebase so suggestions are contextually relevant. The Tree-sitter based summarization reduces the amount of context sent to LLMs, saving tokens while enabling targeted retrieval of full implementations when needed. Its session-based CLI keeps interactions focused within a project and its configuration options let teams standardize provider, model and reasoning settings per repository. Support for multiple providers and local models gives flexibility for privacy or cost concerns. Features like multi-file edits, automated refactoring, code review assistance and token tracking help teams iterate faster, improve code quality and manage costs without leaving the terminal.

Please fill the required fields*