Run a Local Coding Agent with Gemma 4 and Pi

Patrick Loeber has published a guide showing how to run a local coding agent entirely on your own hardware using LM Studio, Pi, and Google’s open-weight Gemma 4 model. The setup is aimed at terminal-based coding work, where the model can help inspect files, edit code, and carry out commands without sending the session to a cloud service.

The appeal here is straightforward. Many teams want AI assistance for software development, but do not want source code, prompts, or shell activity flowing through a hosted service. A local agent keeps the data path inside the machine or server you control, which matters for private codebases, internal tooling, and environments with tighter security rules.

Gemma is Google’s family of open-weight models, meaning the model weights are available for local use rather than being locked behind a hosted-only product. LM Studio is a desktop application that makes it easier to run compatible models locally, manage downloads, and expose an API-like interface to tools that can talk to a model on the same machine.

Pi is the agent layer in Loeber’s setup. In practical terms, that means it is the tool that takes the model’s output and turns it into coding actions, such as reading a repository, proposing changes, or running commands in a shell. That is the key distinction between a chatbot and an agent: the agent does not just answer questions, it can act on the environment.

Loeber’s guide walks through the configuration steps needed to make the stack work together. That includes selecting the model, connecting Pi to the local LM Studio instance, setting context size, and enabling the skills or extensions that give the agent access to useful development tasks.

Context size matters because coding work often depends on how much of the project the model can see at once. A larger context window lets the agent reason over more files, longer command output, and broader snippets of code, although local hardware limits still shape what is practical.

Skills and extensions are also part of the picture. In agent tooling, these are the components that teach the system how to interact with common developer workflows, such as file manipulation, terminal commands, or project-specific actions. Without them, the model can still chat about code, but it is less useful as an active coding assistant.

Running this kind of setup locally is not just about convenience. It also gives operators more direct control over what the agent can touch, what commands it can run, and what data stays inside the machine. That is one reason local LLM tooling has become popular among self-hosters and developers who want AI help without adopting a cloud-first workflow.

At the same time, a local coding agent is only as safe as the permissions around it. If the model can access a shell, the surrounding tooling has to decide which commands are allowed, which directories are in scope, and how much autonomy the agent should have before a human reviews its changes.

Loeber’s walkthrough is part tutorial and part reference setup for people who want to experiment with local agentic coding without building every component from scratch. It gives a concrete path for pairing an open-weight model with a terminal-focused agent framework, using the local LM Studio instance as the model backend and Pi as the layer that drives the coding workflow.