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Coro Code: Revolutionizing AI-Powered Coding with Rust-Backed Performance

高效码农

Coro Code: The High-Performance AI Coding Assistant Built with Rust

demo

Have you ever wished for a capable assistant while coding—something that could understand your needs and help you write, modify, or even optimize code? Meet Coro Code (previously known as Trae Agent Rust), a high-performance AI coding agent written in Rust that comes with a rich terminal interface designed to deliver speed, stability, and an enjoyable coding experience.

What is Coro Code?

Coro Code is an AI-powered coding assistant developed in Rust. It interacts with you through the terminal and assists with various coding tasks—whether it’s fixing bugs, refactoring existing code, or generating new functional modules. It’s designed to be a reliable partner in your development workflow.

Why Choose Coro Code?

  • Pure Rust Core: Benefits from Rust’s performance and safety—fast operation with minimal resource usage
  • Rich Terminal Interface: Interactive UI built on the iocraft framework with real-time status updates and smooth animations
  • Powerful Toolset: Supports multiple tools including bash, edit, json_edit, thinking, and more
  • Multi-Model Support: Currently supports OpenAI models, with Anthropic and Google model support coming soon
  • Smart File Search: Includes @path syntax, Git-aware operations, and fast file matching

How to Install Coro Code

Prerequisites

Before installing, make sure your system meets the following requirements:

  • Rust programming language stable edition (version 1.70 or higher)
  • Model API key (OpenAI recommended; Anthropic/Google support coming soon)

Installation Steps

Installing Coro Code is straightforward. Just run the following command:

cargo install --git https://github.com/Blushyes/coro-code --bin coro

This command downloads the source code from the GitHub repository and compiles it. Depending on your internet connection and system performance, this may take a few minutes.

Running Coro Code

Once installed, you can use Coro Code in two ways:

# Interactive mode (recommended)
coro

# Single task mode
coro "Fix the bug in main.rs"

Interactive mode offers the full terminal interface experience, while single task mode is suitable for quick, specific tasks.

Configuring Coro Code

Coro Code offers flexible configuration through either environment variables or a configuration file.

Environment Variable Configuration

OpenAI Configuration Example:

# Set OpenAI API key
export OPENAI_API_KEY="your_openai_api_key"

# Set the model to use
export OPENAI_MODEL="gpt-4o"

# Optional: Set custom base URL and model for OpenAI-compatible APIs
export OPENAI_BASE_URL="https://api.deepseek.com"
export OPENAI_MODEL="deepseek-chat"

# Or use universal overrides (works with any protocol)
export CORO_BASE_URL="https://api.custom.com"
export CORO_MODEL="custom-model"

JSON File Configuration

You can also create a coro.json file in your working directory:

{
  "protocol": "openai",
  "base_url": "https://api.deepseek.com",
  "api_key": "your-api-key",
  "model": "deepseek-chat",
  "max_token": 8192
}

Configuration priority is: CLI arguments > environment variables > JSON file. This flexible approach lets you easily switch settings between different projects and environments.

Supported Model Providers

Coro Code aims to support multiple mainstream AI models. The current status is:

Provider Models Status
OpenAI gpt-4o, gpt-4o-mini Supported
Anthropic claude-3.5 series Planned
Google gemini-1.5 series Planned

Environment Variable Reference

To help you configure Coro Code effectively, here’s a detailed explanation of supported environment variables:

Variable Name Description Example
OPENAI_API_KEY OpenAI API key sk-...
OPENAI_BASE_URL Custom base URL for OpenAI-compatible APIs https://api.deepseek.com
OPENAI_MODEL Custom model for OpenAI-compatible APIs gpt-4o, deepseek-chat
ANTHROPIC_API_KEY Anthropic API key sk-ant-...
ANTHROPIC_BASE_URL Custom base URL for Anthropic API https://api.anthropic.com
ANTHROPIC_MODEL Custom model for Anthropic API claude-3-5-sonnet-20241022
GOOGLE_API_KEY Google AI API key AIza...
GOOGLE_BASE_URL Custom base URL for Google AI API https://generativelanguage.googleapis.com
GOOGLE_MODEL Custom model for Google AI API gemini-pro, gemini-1.5-pro
AZURE_OPENAI_API_KEY Azure OpenAI API key ...
AZURE_OPENAI_BASE_URL Azure OpenAI endpoint https://your-resource.openai.azure.com
AZURE_OPENAI_MODEL Custom model for Azure OpenAI gpt-4, gpt-35-turbo
CORO_BASE_URL Universal base URL override (any protocol) https://api.custom.com
CORO_PROTOCOL Force specific protocol openai, anthropic
CORO_MODEL Universal model override (any protocol) gpt-4o, claude-3-5-sonnet

Coro Code Development Roadmap

The Coro Code project follows a clear development plan divided into five main phases:

Phase One: Core Experience

This phase focuses on building stable and reliable core functionality:

  • First-time setup management: Guided setup that detects or creates configuration files, validates API keys, and provides default models and examples
  • Optimized configuration loading: Unified configuration priority (CLI arguments > environment variables > JSON files), friendlier error messages and diagnostics, with optional hot reloading
  • Tool Call permission system: Manage permissions via tool/command/directory whitelists, with interactive confirmation to prevent unauthorized or sensitive operations

Phase Two: Enhanced User Experience

This phase emphasizes improving the user experience:

  • CORO.md custom prompt support: Project/subdirectory-level overrides with scenario-based templates (bug fixes, refactoring, documentation, testing)
  • UI layout optimization and unification: Consistent Header/Status/Input styling with optimized keyboard shortcuts and interaction patterns
  • Traject replay and export: Visualize Trajectory, one-click replay, and export to JSON/Markdown
  • Visual identity design: Create a logo with a style similar to gemini-cli

Phase Three: Intelligence and Performance

This phase focuses on enhancing the tool’s intelligence and performance:

  • Multi-model and automatic routing: Automatically select models based on task type, with automatic fallback and retry strategies for failures
  • Context optimization and caching: File summary caching, duplicate reference removal, and Token budget control
  • Token compression: Intelligent context compression, selective Token reduction, and adaptive context windows
  • MCP extension ecosystem: Provide common Provider presets and templates with one-click start/stop for external tools

Phase Four: Platform and Ecosystem

This phase looks toward building a broader ecosystem:

  • WASM packaging for core: Enable browser/plugin environment usage with isomorphic tool interfaces and minimal runtime
  • Cross-platform enhancement: Optimize details for macOS/Linux/Windows/WSL compatibility and stability improvements
  • Plug-in tool system: Establish third-party tool registration standards with version and dependency declarations

Phase Five: Security and Quality

This phase concentrates on improving tool security and quality:

  • Security and rate limiting: Sandbox mode (restricted bash/network switching) with concurrency and rate limiting
  • Testing and benchmarking: End-to-end test samples with performance benchmarks and comparison reports

Contributing to Coro Code Development

If you’re interested in Coro Code and want to contribute to its development, here is some useful information.

Pre-commit Hooks

The project strongly recommends setting up pre-commit hooks to maintain code quality. The repository includes scripts to automatically install hooks that run formatting, code checking, and testing before each commit.

Choose the appropriate script for your platform:

# Linux/macOS
./scripts/setup-pre-commit-hooks.sh

# Windows PowerShell
.\scripts\setup-pre-commit-hooks.ps1

# Windows Command Prompt
scripts\setup-pre-commit-hooks.bat

Pre-commit hooks automatically run code formatting, checking, and testing to ensure submitted code meets project standards.

Contributing Code

The basic process for contributing to Coro Code development:

  1. Fork the repository to your GitHub account
  2. Create a feature branch for development
  3. Set up pre-commit hooks (recommended)
  4. Make code modifications and improvements
  5. Ensure all tests pass
  6. Submit a Pull Request to the main repository

The project welcomes various forms of contribution, including code implementation, documentation improvements, and issue reporting.

Frequently Asked Questions

Who is Coro Code suitable for?

Coro Code is suitable for developers of all levels, from beginners to experienced professionals. Whether you need help solving specific coding problems or want an AI assistant to improve coding efficiency, Coro Code can provide valuable assistance.

Do I need extensive Rust knowledge to use Coro Code?

No. Although Coro Code itself is written in Rust, using it doesn’t require deep Rust knowledge. Basic command line operation and API key configuration knowledge is sufficient.

Which programming languages does Coro Code support?

Coro Code itself isn’t limited to any specific programming language. It understands and processes code through interaction with AI models, so it theoretically supports all mainstream programming languages. Actual support depends on the capabilities of the AI model being used.

How can I ensure code security when using Coro Code?

Coro Code provides a permission control system that allows whitelisting by tool/command/directory, with interactive confirmation mechanisms to prevent unauthorized or sensitive operations. Additionally, maintaining code review habits while using the tool is recommended.

How is Coro Code different from other AI coding assistants?

Coro Code’s main differentiators are its Rust-based high-performance core, rich terminal interface experience, and more flexible configuration options. It particularly emphasizes response speed and stability while maintaining compatibility with original tool specifications.

Whether you’re a beginner learning to code or an experienced developer looking to improve productivity, Coro Code is worth trying. Its design philosophy is to be a truly practical and efficient coding assistant, not just a showcase of technical features.

With simple installation and configuration, you can start experiencing the convenience of AI-assisted programming. As the project continues to develop, Coro Code will add more powerful features to provide comprehensive support for developers.

If you’re interested in Coro Code, visit the project’s GitHub repository to learn more details and even contribute to the project. The power of the open-source community will make this tool increasingly powerful and user-friendly.

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