Build a Local AI Agent with GPT-OSS & Ollama

8/12/2025

Here's How You Can Build a Local AI Agent Using GPT-OSS & Ollama (and Why You'd Want To)

Hey everyone, let's talk about something pretty exciting that's been happening in the AI space. For a while now, if you wanted to play with really powerful language models, you were pretty much tied to cloud services & API keys. It was cool, but it came with costs & privacy concerns. Well, things are changing, & for the better. OpenAI recently dropped their first open-source model since GPT-2, called GPT-OSS, & it's a HUGE deal.

What if I told you that you can now run a GPT-4 level model right on your own computer? I'm talking about a fully local AI agent that's powerful, private, & basically free to run once you have the hardware. Sounds awesome, right?

Turns out, with a bit of setup using tools like Ollama, you can do exactly that. In this guide, I’m going to walk you through everything you need to know to get your very own local AI agent up & running. We'll cover what these tools are, why you should care, & a step-by-step process to build your own.

So, What’s the Big Deal with Local AI?

Before we dive into the "how," let's talk about the "why." Why would you want to run an AI model locally instead of just using a cloud service?

Honestly, there are some pretty compelling reasons:

Privacy is Paramount: When you use a cloud-based AI, your data is being sent to someone else's servers. For personal stuff or sensitive business data, that's not always ideal. Running a model locally means everything stays on your machine. Period.
Zero Latency & Offline Access: Your local agent doesn't need an internet connection to work. This means it's super fast (no network lag) & you can use it anywhere, even if your internet is down.
Cost-Effective: While cloud services have ongoing costs that can add up, running a local model is free. You invest in the hardware upfront, but after that, there are no subscription fees or per-token charges.
Ultimate Customization & Control: When the model is yours, you have total control. You can tweak it, fine-tune it on your own data, & integrate it into your workflows in ways that just aren't possible with a closed-off API.

This is a game-changer for developers, researchers, & even hobbyists who want to build custom solutions without being tethered to a big tech company.

The Tools of the Trade: GPT-OSS & Ollama

To make this magic happen, we're going to be using two key pieces of technology:

1. GPT-OSS: This is OpenAI's new open-weight language model. "Open-weight" means that while the model architecture is open, the trained weights (the "brain" of the model) are also available for you to download & use. It's released under the permissive Apache 2.0 license, which makes it great for both personal & commercial projects.

The cool thing is that it comes in different sizes, so you can pick the one that best suits your hardware.

gpt-oss-20b: This is the smaller model, perfect for high-end consumer GPUs or Apple Silicon Macs with at least 16GB of VRAM or unified memory. It's great for lower latency & more specialized tasks.
gpt-oss-120b: This is the big kahuna, a full-sized model designed for serious, general-purpose reasoning. You'll need a beefier setup for this one, ideally with 60GB or more of VRAM, like a multi-GPU workstation.

These models are also optimized with a special quantization format (MXFP4) that compresses them, making them more efficient without a huge performance hit.

2. Ollama: If GPT-OSS is the engine, Ollama is the entire car built around it. Ollama is a fantastic tool that makes it incredibly simple to download, manage, & run large language models on your own machine. It handles all the complicated stuff in the background & gives you a simple command-line interface or an API to interact with the models. Think of it as a friendly manager for your local AI models.

Let's Get Building: A Step-by-Step Guide

Alright, enough talk. Let's get our hands dirty & build our local AI agent. I'll walk you through the core steps.

Step 1: Install Ollama

First things first, you need to get Ollama on your system. It's a pretty straightforward process.

Head over to the Ollama website.
Download the installer for your operating system (macOS, Linux, or Windows).
Run the installer. It will set up the Ollama application & the command-line tool.

Once it's installed, you can open your terminal (or Command Prompt on Windows) & you should be ready for the next step.

Step 2: Download the GPT-OSS Model

Now that you have Ollama, you need to download the GPT-OSS model. This is where you'll choose between the 20B & 120B versions. For most people starting out, the 20B model is the way to go unless you have a serious AI workstation.

In your terminal, run one of the following commands:

For the 20B model:

ollama pull gpt-oss:20b

For the 120B model:

ollama pull gpt-oss:120b

Ollama will then download the model for you. It's a big file, so it might take a little while depending on your internet connection. Once it's done, you'll have a powerful language model sitting right on your hard drive.

Step 3: Chat with Your Local AI

This is the fun part. You can immediately start chatting with your new local AI agent directly from the terminal.

Just run this command:

ollama run gpt-oss:20b

Ollama will load the model & you'll get a prompt where you can start typing your messages. Go ahead, ask it anything! You're now having a conversation with an AI that's running 100% locally on your machine. Pretty cool, huh?

Taking It to the Next Level: Building a Real Agent

Chatting in the terminal is cool, but the real power comes from integrating your local model into applications & workflows. This is how you build a true "agent" that can perform tasks, not just answer questions.

There are a few ways to do this, but a popular approach is to use Python with libraries like LangChain or even OpenAI's own SDK.

Using the OpenAI SDK with Ollama

One of the best features of Ollama is that it exposes an API that's compatible with OpenAI's Chat Completions API. This means you can use the familiar

openai

Python library to interact with your local model, which is SUPER convenient.

Here’s a basic Python example: