OpenClaw + Ollama: Run Your AI Agent for Free with Local Models

ByL.B. Kaelin

You want to build an AI agent, but every API call costs money. You’re prototyping, testing, or just tinkering, and the meter is always running. It feels like you can’t think freely because every idea has a price tag.

That changes today. You can run your OpenClaw agent completely offline, for free, using local models. This is the key to unlimited experimentation without a single API bill.

By connecting OpenClaw to Ollama, you turn your computer into a private AI workshop. Your agent can plan, execute code, and manage tasks using powerful open-source models running right on your machine. No data leaves your system. No costs accrue.

This guide shows you how to set up the openclaw ollama | openclaw local models, free ai agent stack in minutes. You’ll walk away with a fully functional, private agent that you can run anytime, for any project, at zero marginal cost.

Let’s get your machine working for you.

What You Need Before Starting

Think of this like building a workshop. You need the space, the power tools, and the raw materials before you can start crafting. Here’s what you need and why.

  • OpenClaw Installed: This is your agent’s brain and body. Without it, you have no AI to run locally. If you haven’t installed it yet, that’s your only manual step. We’ll give you the command.
  • Docker & Docker Compose: OpenClaw runs in containers. Docker is the standardized workshop floor where all the tools run predictably. Docker Compose is the blueprint that tells them how to work together.
  • Ollama: This is your local model server. It’s like having a private, free OpenAI API endpoint running on your machine. It downloads and serves open-source models like Llama 3.2 or Mistral.
  • A Computer with Enough RAM: Local models live in your computer’s memory. For a useful 7-billion parameter model, aim for at least 16GB of total system RAM. You can start with smaller models on 8GB, but performance will be limited.
  • Basic Terminal Comfort: You’ll be running a few commands. If you can copy and paste into a terminal, you’re set.

Setting Up Your Environment

This setup does one thing: it wires your OpenClaw agent’s “thinking” module to your local Ollama server instead of a paid cloud API. Once connected, every agent thought is free and private.

1. Install OpenClaw (If You Haven’t)

If you already have OpenClaw running, skip this. If not, this is the foundation. We use the official installer which sets up everything in Docker for you.

curl -sSL https://raw.githubusercontent.com/openclaw-ai/openclaw/main/install.sh | bash

The script will ask for your sudo password to set up Docker networks. It then pulls the images and starts OpenClaw. Wait for it to finish. You should see a message about OpenClaw being available at http://localhost:3000.

2. Install and Start Ollama

Ollama runs separately from OpenClaw. It’s a background service that hosts the models. Installation is one command.

Linux/macOS:

curl -fsSL https://ollama.ai/install.sh | sh

Windows (via WSL2): Run the above command inside your WSL2 distribution (like Ubuntu).

After installation, start the Ollama service. It needs to be running before OpenClaw can talk to it.

ollama serve

Leave this terminal window open. You’ll see logs when models are loaded or used. In a new terminal, test it by pulling a model. Let’s start with a capable, efficient one.

ollama pull llama3.2:3b

This downloads the 3-billion parameter Llama 3.2 model. It’s fast and a good starting point. You’ll see a progress bar. Once done, your local model server is ready.

3. Connect OpenClaw to Ollama

Now for the magic wire. We need to tell OpenClaw to use your local Ollama endpoint instead of OpenAI. You do this by setting one environment variable.

First, navigate to your OpenClaw project directory (where the docker-compose.yml file is, likely ~/openclaw). Then, open the environment file for editing.

cd ~/openclaw
nano .env

Find the line that says LLM_API_BASE= (it might be commented out with a #). Change it to point to your Ollama service.

LLM_API_BASE=http://host.docker.internal:11434/v1

Save the file (Ctrl+X, then Y, then Enter in nano). This URL is a special Docker address that lets the OpenClaw container talk to the Ollama service on your host machine.

Now, restart OpenClaw to load the new configuration.

docker compose down
docker compose up -d

Wait a moment for it to restart, then check the logs to ensure it came up cleanly.

docker compose logs -f openclaw

You might see a connection error at first if Ollama isn’t ready. That’s okay. The key is that OpenClaw is now trying to use your local endpoint.

4. Verify the Connection

Time to test. Open your OpenClaw web interface at http://localhost:3000. Create a new agent or select an existing one. In the agent’s configuration, look for the model settings.

Instead of “gpt-4” or “gpt-3.5-turbo,” you should now see “llama3.2:3b” or similar as an available option. Select it. Save the agent configuration.

Ask your agent a simple test question: “What is 2+2?”

Watch the terminal where ollama serve is running. You should see activity logs as the model processes the request. Your agent’s response will come from your machine, not the cloud.

If it works, you’ve just cut the cord. Your AI agent is now running on your terms.

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