🚀 Coro Code

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Coro Code is a high-performance AI coding agent written in Rust with a rich terminal UI. Formerly known as Trae Agent Rust, it remains compatible with the original tool spec while focusing on speed, reliability, and great UX.

✨ Highlights

• 🚀 High Performance: Written in Rust for speed and memory safety
• 🎨 Rich Terminal UI: Beautiful, interactive interface with real-time updates
• 🔧 Easy Configuration: Support for multiple LLM providers with flexible config options
• 🛠️ Powerful Tools: Built-in bash execution, file operations, and extensible tool system
• 🔄 Environment Variables: Comprehensive support for API keys, base URLs, and model configuration
• 📦 Cross-Platform: Works seamlessly on macOS, Linux, and Windows

🚀 Quick Start

📋 Prerequisites

• 🦀 Rust stable (1.70+)
• 🔑 An API key (OpenAI recommended; Anthropic/Google coming soon)

📦 Install

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

▶️ Run

# Interactive mode (recommended)
coro

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

Configuration

Option A: Environment variables

# OpenAI
export OPENAI_API_KEY="your_openai_api_key"
export OPENAI_MODEL="gpt-4o"

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

# Or use generic overrides for any protocol
export CORO_BASE_URL="https://api.custom.com"
export CORO_MODEL="custom-model"

Option B: Configuration file

Create a coro.json file:

{
  "protocol": "openai",
  "base_url": "https://api.deepseek.com",
  "api_key": "your-api-key",
  "model": "deepseek-chat",
  "params": {
    "max_tokens": 131072,
    "temperature": 0.7,
    "top_p": 0.9
  }
}

Usage

# Interactive mode
coro

# Direct command
coro "Help me refactor this function"

# With specific config
coro --config custom.json "Analyze this codebase"

🤖 Supported Models

Provider	Models	Status
🟢 OpenAI	gpt-4o, gpt-4o-mini	✅ Ready
🟡 Anthropic	claude-3.5 family	🚧 Coming
🔵 Google	gemini-1.5 family	🚧 Coming

🔧 Environment Variables Reference

Variable	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	Generic base URL override (any protocol)	https://api.custom.com
CORO_PROTOCOL	Force specific protocol	openai, anthropic
CORO_MODEL	Generic model override (any protocol)	gpt-4o, claude-3-5-sonnet

🗺️ Roadmap

Status Legend: ✅ Completed | 🚧 In Progress | 📋 Planned

🚀 Phase 1: Core Experience

Priority	Status	Feature	Description
🔥 High	🚧	First-time Setup Management	Guided wizard (detect/create openai.json or env vars), API key validation, default models & examples
🔥 High	✅	Refactor Config Loading Logic	Unified priority (CLI args > env vars > JSON file), friendly error messages & diagnostics, optional hot reload
🔥 High	📋	Tool Call Permission System	Tool/command/directory whitelist, interactive confirmation, privilege escalation & sensitive operation warnings

🎨 Phase 2: User Experience Enhancement

Priority	Status	Feature	Description
🟡 Medium	📋	CORO.md Custom Prompts Support	Project/subdirectory level overrides, scenario templates (bugfix/refactor/docs/test)
🟡 Medium	🚧	UI Layout Optimization & Unification	Header/Status/Input style consistency, keyboard shortcuts & interaction consistency optimization
🟡 Medium	📋	Trajectory Replay & Export	Trajectory visualization, one-click replay, export to JSON/Markdown
🎨 Low	📋	Logo Design (gemini-cli style)	Visual identity design

🤖 Phase 3: Intelligence & Performance

Priority	Status	Feature	Description
🟡 Medium	📋	Multi-model & Auto Routing	Auto model selection by task type, failure auto-downgrade & retry strategies
🟡 Medium	📋	Context Optimization & Caching	File summary caching, duplicate reference deduplication, token budget control
🟡 Medium	✅	Token Compression	Intelligent context compression, selective token reduction, adaptive context windows
🔵 Low	📋	MCP Extension Ecosystem	Common provider presets & templates, one-click start/stop external tools

🌐 Phase 4: Platform & Ecosystem

Priority	Status	Feature	Description
🔵 Low	📋	Core WASM Support	Browser/plugin environment ready, isomorphic tool interface & minimal runtime
🔵 Low	📋	Cross-platform Enhancement	macOS/Linux/Windows/WSL detail adaptation & stability improvements
🔵 Low	📋	Plugin Tool System	Third-party tool registration spec, version & dependency declaration

🛡️ Phase 5: Security & Quality

Priority	Status	Feature	Description
🟡 Medium	📋	Security & Rate Limiting	Sandbox mode (restricted bash/network switches), concurrency & rate limiting
🔵 Low	📋	Testing & Benchmarks	End-to-end test cases, performance benchmarks & comparison reports

🛠️ Development

Context Export/Restore (Persistence)

The core supports exporting the conversation and execution context to JSON and restoring it later:

use coro_core::agent::{AgentBuilder, PersistedAgentContext};

// Export
let json = agent.export_context_json()?;                    // as JSON string
agent.export_context_to_file(".coro/context.json")?;       // or to file

// Restore
agent.restore_context_from_json(&json)?;                    // from JSON
agent.restore_context_from_file(".coro/context.json")?;    // or from file

// Work with the structured snapshot directly:
let snap = agent.export_context_snapshot()?;
let json2 = snap.to_json()?;
let snap2 = PersistedAgentContext::from_json(&json2)?;
agent.restore_context_from_snapshot(snap2)?;

Notes:

• Snapshot contains conversation_history, AgentExecutionContext, and optional AgentConfig.
• On restore, saved config (if present) is applied; missing tool-result pairs are handled automatically on next execution.
• No need to manually re-inject a system prompt; the agent handles that as needed.

Pre-commit Hooks

We strongly recommend setting up pre-commit hooks to maintain code quality. The repository includes scripts to automatically install hooks that run formatting, linting, and tests 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

The pre-commit hook will automatically run:

• Code formatting (cargo fmt --check)
• Linting (cargo clippy)
• Tests (cargo test)

For more details, see scripts/README.md.

Contributing

1. Fork the repository
2. Create a feature branch
3. Set up pre-commit hooks (recommended)
4. Make your changes
5. Ensure all tests pass
6. Submit a pull request

📄 License

Dual licensed under your choice of:

• Apache-2.0 (LICENSE-APACHE)
• MIT (LICENSE-MIT)

🙏 Acknowledgments

• Trae Agent for the original Python implementation and spec
• iocraft for the beautiful terminal UI framework
• OpenAI, Anthropic, and Google for model APIs
• Rust community for the amazing ecosystem

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