Mentis-CLI 🧠

> An Agentic, Multi-Model CLI Coding Assistant.

Mentis is a powerful terminal-based AI coding assistant that lives in your command line. It supports multiple LLM providers (Gemini, Ollama, OpenAI), agentic file operations, MCP (Model Context Protocol), and more.

✨ Features

* 🤖 Multi-Model Support: Switch seamlessly between Gemini, Ollama (Local), OpenAI, and GLM-4.6 (Z.AI Coding).
* 🛠️ Agentic Capabilities: Mentis can read, write, list files, and search your codebase to understand context.
* 🌐 Web Intelligence: Hybrid search for documentation and error fixing.
* 🗺️ Smart Context: Automatically maps your repository structure.
* 🧩 Interactive & Robust CLI:
* History: Navigate previous commands with Up/Down arrows (persistent across sessions).
* Cancellation: Press Esc to instantly stop generation.
* Markdown: Beautiful syntax highlighting in terminal output.
* 💰 Cost Awareness: Real-time token usage and cost tracking.
* 🧠 Persistent Memory: Auto-saves sessions (/resume) and checkpoints (/checkpoint).
* 🔌 MCP Support: Full Model Context Protocol client.
* 🔍 Codebase Search: Built-in grep tool.
* 🐚 Shell Integration: Run shell commands and commit changes directly.

🚀 Installation

$3

You can install Mentis globally directly from the NPM registry:

``bash
npm install -g @indiccoder/mentis-cli
`

$3

`bash
git clone https://github.com/CoderVLSI/Mentis-CLI.git
cd Mentis-CLI
npm install
npm run build
npm link
`

⚙️ Configuration

Start Mentis by typing:
`bash
mentis
`

$3

Type /model inside the CLI to launch the interactive configuration wizard:
1. Select Provider: Gemini, Ollama, or OpenAI.
2. Select Model: e.g., gemini-2.5-flash, llama3.
3. Enter Credentials: API Key (for cloud) or Base URL (for local).

Credentials are stored securely in ~/.mentisrc.

$3

Mentis includes web search capabilities for documentation and error solving. To enable it:

1. Get a free Tavily API key at https://tavily.com
2. Add to your .env file in your project directory:
`
TAVILY_API_KEY=your_key_here
`

Alternatively, you can use Exa via MCP:
`bash
export EXA_API_KEY=your_key_here
mentis
/mcp connect "Exa Search"
`

📖 Usage

$3

* /plan: Switch to high-level planning mode (Architecture, requirements).
* /build: Switch to code generation mode (Implementation).

$3

| Command | Description |
| :--- | :--- |
| /help | Show available commands |
| /model | Configure AI provider and model (Interactive) |
| /resume | Resume the last session |
| /checkpoint | Manage saved sessions (save, load, list) |
| /search | Search for code in the current directory |
| /mcp connect | Connect an MCP Server (e.g., npx -y @modelcontextprotocol/server-time) |
| /run | Execute a shell command |
| /commit [msg] | Stage and commit changes to Git |
| /add | Add a specific file to context |
| /drop | Remove a file from context |
| /clear | Clear chat history |
| /exit | Save and exit |

$3

`text
Mentis > /plan
[PLAN] > Analyze this project structure and suggest improvements.

Mentis > /build
[BUILD] > Implement the FolderManager class in src/utils.ts.
``

🔒 Safety

Mentis asks for explicit approval before writing or modifying any files, keeping you in control.

🤝 Contributing

Pull requests are welcome!

📄 License

ISC