Gemma Chat Review: Open Source Local AI Coding Agent for Mac

I've been working on a fitness tracking app called FitTrack lately — lots of React components, real-time dashboards, and auth pages. While I use GitHub Copilot daily, there are times I wish I had an AI assistant that didn't need an internet connection. No cloud latency, no data leaving my machine, no subscription. That's why I got excited when I discovered Gemma Chat: an open-source, local-first coding agent built for Apple Silicon that runs Google's Gemma 4 model entirely offline. It's not just a chatbot — it can write multi-file projects, show live previews, and even take voice commands.

What is Gemma Chat?

Gemma Chat is an open-source Electron application that brings Google's Gemma 4 model family to your Mac, running natively via Apple's MLX framework. Unlike cloud-based assistants, everything happens on your machine: code generation, chat, file operations, and even speech-to-text via in-browser Whisper. After an initial model download (~3 GB for the recommended variant), you can code from an airplane, a remote cabin, or anywhere without internet.

The app offers two modes: Build Mode, where you describe a project and it writes files (HTML, CSS, JS, multi-file) with a live preview canvas that updates as the model streams output; and Chat Mode, a conversational AI with tool use (web search, URL fetch, calculator, bash). You can hot-swap between four Gemma variants: the fast 1.5 GB E2B, the balanced 3 GB E4B, the powerful 8 GB 27B MoE, and the max-quality 18 GB 31B.

Why I starred it

As a developer building FitTrack, I often prototype UI ideas quickly — sometimes offline during commutes or at coffee shops with spotty Wi-Fi. I needed an AI that could generate a component scaffold or a simple dashboard layout without reaching for the cloud. Gemma Chat fits that niche perfectly. It's also a testament to how far open models have come: Gemma 4 can produce coherent multi-file projects with no outside help. I starred it because it shows that local AI is not just a gimmick — it's a practical tool for real development work, especially when privacy or connectivity is a concern.

How it works

Architecturally, Gemma Chat is an Electron + Vite + React app that bootstraps a Python virtual environment on first launch to run MLX-LM — the model server. The main process manages an agent loop: it streams tokens from the local MLX server, parses XML actions (like <write_file>, <bash>) from the model output, executes them in a sandboxed workspace, and feeds results back. Up to 40 rounds per user prompt. The preview server serves files from the workspace, refreshing every ~450ms to show updates in real-time.

Key concepts:

• Model Runtime: MLX-LM auto-installed into a local venv. You choose from four Gemma models.
• Agent Loop: XML-based tool protocol works better for smaller models than JSON function calling.
• Workspace: Per-conversation sandboxed filesystem with a local HTTP server for previews.
• Voice Input: Uses transformers.js with in-browser Whisper for local speech-to-text.
• Tool Use: In Chat Mode, the model can fetch URLs, search web, calculate, and run bash.

Quick start

You'll need macOS on Apple Silicon (M1 or later), Python 3.10–3.13, and Node 20+. Install Python via Homebrew if needed: brew install python@3.13. Then clone and run:

git clone https://github.com/ammaarreshi/gemma-chat.git
cd gemma-chat
npm install
npm run dev

On first launch, the app creates a Python venv, installs MLX-LM, downloads the recommended Gemma 4 E4B model (~3 GB), and you're ready. To build a distributable .dmg, run npm run dist.

Real-world example

Let's build a simple personal homepage with a dark theme and a profile card. In Build Mode, type this prompt:

"Create a single-page personal website with a dark gradient background, a circular profile photo placeholder, your name, a short bio, and three social media icon links. Make it responsive and use smooth CSS transitions."

The model will stream back XML actions:

<action name="write_file">
<path>index.html</path>
<content>
<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>Personal Homepage</title>
  <style>
    * { margin: 0; padding: 0; box-sizing: border-box; }
    body {
      min-height: 100vh;
      background: linear-gradient(135deg, #0f0c29, #302b63, #24243e);
      font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
      display: flex;
      justify-content: center;
      align-items: center;
      color: #fff;
    }
    .card {
      background: rgba(255,255,255,0.1);
      backdrop-filter: blur(10px);
      border-radius: 20px;
      padding: 40px;
      text-align: center;
      max-width: 400px;
      width: 90%;
      transition: transform 0.3s, box-shadow 0.3s;
    }
    .card:hover {
      transform: translateY(-10px);
      box-shadow: 0 20px 40px rgba(0,0,0,0.4);
    }
    .avatar {
      width: 120px;
      height: 120px;
      border-radius: 50%;
      background: #ddd;
      margin: 0 auto 20px;
      display: flex;
      align-items: center;
      justify-content: center;
      font-size: 60px;
      color: #555;
    }
    h1 { margin-bottom: 10px; font-size: 2rem; }
    .bio { margin-bottom: 20px; opacity: 0.8; }
    .social a {
      display: inline-block;
      margin: 0 10px;
      color: #fff;
      text-decoration: none;
      font-size: 24px;
      transition: color 0.3s;
    }
    .social a:hover { color: #a0a0ff; }
  </style>
</head>
<body>
  <div class="card">
    <div class="avatar">&#128100;</div>
    <h1>Your Name</h1>
    <p class="bio">Full-stack developer passionate about building cool things. Available for freelance projects.</p>
    <div class="social">
      <a href="#" title="GitHub">&#128187;</a>
      <a href="#" title="Twitter">&#128172;</a>
      <a href="#" title="LinkedIn">&#128188;</a>
    </div>
  </div>
</body>
</html>
</content>
</action>

The preview updates live as the file is written. You can then ask for changes: "Make the avatar a real image placeholder" and the model will edit the file accordingly.

Pros and cons

Alternatives

• Ollama: Run any GGUF model locally with a REST API, supports many hardware platforms. Great for general LLM use, but no built-in coding agent UI.
• Continue.dev: Open-source AI code assistant that integrates with VS Code and JetBrains. Supports local models via Ollama or LM Studio, but requires an editor extension.
• LM Studio: Desktop app to download and run local models (via llama.cpp). Offers a chat interface and API server, but no project-based coding agent or live preview.

My verdict — should you use it?

If you're on an Apple Silicon Mac and care about privacy or offline capabilities, Gemma Chat is a compelling tool for quick prototypes and learning. It's not yet ready to replace cloud assistants for complex production code, but it's a fantastic proof-of-concept that showcases the power of local AI. Skip it if you need cross-platform support or require the reliability of larger models. For offline vibe coding sessions, it's already a joy to use.