TunaCode (Beta)

Your agentic CLI developer.

Overview

TunaCode is an agentic CLI-based AI tool inspired by Claude Code, Copilot, Windsurf and Cursor. It's meant to be an open source alternative to these tools, providing a similar experience but with the flexibility of using different LLM providers (Anthropic, OpenAI, Google Gemini, OpenRouter) while keeping the agentic workflow.

TunaCode is currently in beta and under active development. Please report issues or share feedback!

Features

• No vendor lock-in. Use whichever LLM provider you prefer.
• MCP support
• Use /undo when AI breaks things.
• Easily switch between models in the same session.
• JIT-style system prompt injection ensures TunaCode doesn't lose the plot.
• Per-project guide. Adjust TunaCode's behavior to suit your needs.
• CLI-first design. Ditch the clunky IDE.
• Cost and token tracking.
• Per command or per session confirmation skipping.

Roadmap

• Tests 😅
• More LLM providers, including OpenRouter and Ollama

Quick Start

Install TunaCode.

pip install tunacode-cli

On first run, you'll be asked to configure your LLM providers.

tunacode

Configuration

After initial setup, TunaCode saves a config file to ~/.config/tunacode.json. You can open and edit this file as needed. Future updates will make editing easier directly from within TunaCode.

OpenRouter Support

To use OpenRouter models, add an OPENROUTER_API_KEY to the env section of your configuration file. TunaCode will set the environment variable so the OpenAI client can communicate with OpenRouter:

{
  "env": {
    "OPENROUTER_API_KEY": "<YOUR_KEY>"
  }
}

Then run TunaCode with the OpenRouter base URL:

OPENAI_BASE_URL="https://openrouter.ai/api/v1" tunacode

You can now switch to OpenRouter models using:

/model openrouter:mistralai/devstral-small

MCP Support

TunaCode supports Model Context Protocol (MCP) servers. You can configure MCP servers in your ~/.config/tunacode.json file:

{
  "mcpServers": {
    "fetch": {
      "command": "uvx",
      "args": ["mcp-server-fetch"]
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "<YOUR_TOKEN>"
      }
    }
  }
}

MCP servers extend the capabilities of your AI assistant, allowing it to interact with additional tools and data sources. Learn more about MCP at modelcontextprotocol.io.

Available Commands

• /help - Show available commands
• /yolo - Toggle "yolo" mode (skip tool confirmations)
• /clear - Clear message history
• /compact - Summarize message history and clear old messages
• /model - List available models
• /model <num> - Switch to a specific model (by index)
• /branch <name> - Create and switch to a new Git branch
• /undo - Undo most recent changes
• /dump - Show current message history (for debugging)
• exit - Exit the application

Customization

TunaCode supports the use of a "guide". This is a TUNACODE.md file in the project root that contains instructions for TunaCode. Helpful for specifying tech stack, project structure, development preferences etc.

Requirements

• Python 3.10 or higher
• Git (for undo functionality)

Installation

Using pip

pip install tunacode-cli

From Source

1. Clone the repository
2. Install dependencies: pip install . (or pip install -e . for development)

Development

# Install development dependencies
make install

# Run linting
make lint

# Run tests
make test

Release Process

When preparing a new release:

1. Update version numbers in:

   • pyproject.toml
   • src/tunacode/constants.py (APP_VERSION)

2. Commit the version changes:

   git add pyproject.toml src/tunacode/constants.py
   git commit -m "chore: bump version to X.Y.Z"

3. Create and push a tag:

   git tag vX.Y.Z
   git push origin vX.Y.Z

4. Create a GitHub release:

   gh release create vX.Y.Z --title "vX.Y.Z" --notes "Release notes here"

5. Merge to main branch and push to trigger PyPI release (automated)

Commit Convention

This project follows the Conventional Commits specification for commit messages:

• feat: - New features
• fix: - Bug fixes
• docs: - Documentation changes
• style: - Code style changes (formatting, etc.)
• refactor: - Code refactoring
• perf: - Performance improvements
• test: - Test additions or modifications
• chore: - Maintenance tasks (version bumps, etc.)
• build: - Build system changes
• ci: - CI configuration changes

Links

• PyPI Package
• GitHub Issues
• GitHub Repository

License

MIT

Acknowledgments

This project is a fork of sidekick-cli. Thank you to the sidekick-cli team for creating the foundation that made TunaCode possible! 🙏