Codai is an AI code assistant designed to help developers efficiently manage their daily tasks through a session-based CLI, such as adding new features, refacto

Codai is an AI code assistant designed to help developers efficiently manage their daily tasks through a session-based CLI, such as adding new features, refactoring, and performing detailed code reviews. What makes codai stand out is its deep understanding of the entire context of your project, enabling it to analyze your code base and suggest improvements or new code based on your context. This AI-powered tool supports multiple LLM models, including GPT-4o, GPT-4, Ollama, and more. Features Intelligent Code Suggestions: Provide context-aware code completion and suggestions as you type. Add New Features or Test Cases: Suggest new features or generate test cases based on existing code, helping to enhance functionality and ensure code reliability. Code Refactoring: Provide specific suggestions to improve the structure and efficiency of existing code, making it cleaner and easier to maintain. Describe a Bug: Users can describe bugs in their code, allowing the AI to analyze the issue and provide targeted suggestions for resolution or debugging steps. Code Review Assistance: Analyze code and identify potential bugs, providing suggestions to refactor for cleaner and better-performing code. Direct Code Acceptance: After receiving code suggestions from the AI, users can directly accept changes, which will be applied immediately to the codebase. Documentation Generation: Automatically generate documentation based on the codebase, including function descriptions, parameter details, and usage examples. Multi-Language Support: Support for various programming languages (C#, Go, Python, Java, Javascript, Typescript) to cater to a wider range of developers. Customizable Config: Allow users to customize settings through a config file (e.g., changing AI provider, tuning temperature settings). Project Context Awareness: Maintain awareness of the entire project context to provide more accurate suggestions. Full Context Summarization: Summarize the full context of your codebase using Tree-sitter for accurate and efficient code analysis. RAG System Implementation: Implement a Retrieval-Augmented Generation system to improve the relevance and accuracy of code suggestions by retrieving relevant context from the project. Support variety of LLM models: Work with advanced LLM models like GPT-4o, GPT-4 and Ollama to get high-quality code suggestions and interactions. Edit Multiple Files at Once: Enable the AI to modify several files at the same time, making it easier to handle complex requests that need changes in different areas of the code. Token Management: Track and represent the token consumption for each request, providing transparency on how many tokens are used, which helps in managing API costs effectively.