Create Your Local AI Coding Agent with Gemma 4

Top Highlights

1. The article guides you to set up a completely local AI development environment by installing Ollama, downloading the Gemma 4 LLM, and configuring OpenCode as your agent interface, ensuring privacy and cost control.
2. It details how to run Gemma 4 locally with Ollama, including model selection, size options, and creating a custom 128K context window profile for enhanced performance.
3. The setup involves connecting OpenCode to the local Gemma 4 model via a configuration file, empowering you to perform coding tasks, file manipulations, and workspace management entirely offline.
4. This local environment allows for flexible use—running tasks through the terminal or server mode—while keeping all your data on your own machine, offering better privacy and experimentation possibilities.

Creating a Local AI Coding Environment

Building your own local AI coding agent is now easier than ever. Many developers prefer local setups for better privacy, cost control, and understanding of the tools. The process involves three main parts: setting up Ollama, installing Gemma 4, and connecting OpenCode. Each step helps you create a fully functioning, private AI environment right on your computer. This setup allows you to run powerful language models without relying on cloud services. As a result, it offers more control and security for your projects.

Setting Up and Connecting the Components

First, you install Ollama, which serves the AI model locally. Ollama functions as a runtime that downloads, runs, and exposes models through a local API. For Windows users, the installation is straightforward with an official installer or PowerShell commands. Linux users can install Ollama with a simple script. Once installed, Ollama runs in the background, ready to serve models. Next, you download Gemma 4, an open language model from Google. Gemma 4 is designed for reasoning, coding, and multimodal understanding. It comes in different sizes, allowing you to select one based on your hardware. Downloading Gemma 4 via Ollama involves pulling the model and checking its size. Afterward, you verify its functionality with a quick test, like asking for the capital of France.

Now, you install OpenCode, an open-source interface for running AI tasks. OpenCode connects to local or cloud models and can inspect, run, and manipulate files in your workspace. You install it using Node.js, then verify its setup. Finally, you link OpenCode to Gemma 4 by creating a configuration file that points to the local Ollama server and specifies the model. Once connected, you launch OpenCode, which recognizes your local Gemma model for coding and workspace tasks.

Check Your Setup and Explore Its Uses

With everything in place, you can test your AI agent. OpenCode’s terminal interface lets you ask it to draft code, explain functions, or manipulate files. For example, you might request a README file or ask for script testing. This setup isn’t just for code generation; it can handle various workspace tasks, making your development process more efficient. Moreover, you can expand your environment by adding skills, connecting additional tools, or running tasks directly from the command line. The best part is, all data stays on your machine, giving you full control and privacy. This local approach offers a flexible, secure environment for developers eager to understand their AI tools and maintain control over their projects.

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