Running AI tools locally can be a practical choice when you want faster iteration, lower ongoing costs, or more control over data flow. This tutorial explains a clear, repeatable way to install OpenClaw with Ollama, verify everything is working, and fix common setup issues.

What you’re installing (in plain terms)

  • Ollama: a local runtime that helps you download and run language models on your machine.
  • OpenClaw: an app/tooling layer that connects to a local model runtime (like Ollama) so you can use models through a structured interface.

Prerequisites

  • A supported OS (macOS, Windows, or Linux).
  • Admin permissions to install software.
  • Enough disk space for at least one model (often several GB).
  • A terminal (Command Prompt/PowerShell on Windows, Terminal on macOS/Linux).

Step 1: Install Ollama

  1. Download and install Ollama from its official distribution for your OS.
  2. After installation, open a terminal and confirm it’s available by running a version or help command (for example, listing available commands).

Sanity check: can Ollama run a model?

Before adding OpenClaw, confirm Ollama can download and run at least one model locally:

  1. Use Ollama to pull a model (download it).
  2. Start a quick run session and send a simple prompt (e.g., “Write one sentence about local AI”).

If this works, your runtime is functioning and you can move on.

Step 2: Install OpenClaw

OpenClaw installation methods vary (package manager, installer, or container). Choose the one recommended by the project documentation. In most setups you’ll do one of the following:

  • Install via package (common for developer-focused tools): download the release or install via a package manager.
  • Run via container: use Docker/Podman if the project provides an image.

Keep it simple: install and confirm the app starts

  1. Complete the install method for your OS.
  2. Launch OpenClaw once to ensure it starts without errors.

Step 3: Connect OpenClaw to Ollama

This is the key step: OpenClaw needs to know where Ollama is running.

  1. Ensure Ollama is running in the background (some OS installs run it as a service; others require you to start it).
  2. In OpenClaw settings (or configuration file), select Ollama as the provider/runtime.
  3. Set the base URL/host to the local Ollama endpoint (commonly a localhost address).
  4. Pick a model that you already pulled in Ollama.

Verification test

Run a short prompt from inside OpenClaw. If you see a normal response (not an error about connection or missing model), the integration is complete.

Step 4: Install or select models (practical advice)

For a smooth first experience:

  • Start with a smaller model if your machine is mid-range.
  • Use a single model initially; add more after your pipeline is stable.
  • Keep an eye on disk usage—models accumulate quickly.

Common problems and fixes

1) OpenClaw can’t connect to Ollama

  • Check Ollama is running: restart the service/app and try again.
  • Verify the host/port: confirm OpenClaw’s configured endpoint matches the local Ollama address.
  • Firewall/VPN: temporarily disable or allow localhost traffic if something is interfering.

2) “Model not found” or empty model list

  • Pull the model in Ollama first, then refresh OpenClaw.
  • Ensure OpenClaw is pointing to the same Ollama instance where you downloaded the model.

3) Slow responses

  • Switch to a smaller model.
  • Close other heavy apps to free RAM/CPU.
  • If available, enable hardware acceleration options recommended by your OS/runtime.

4) Installation conflicts or missing dependencies

  • Re-run the installer with admin rights.
  • On Linux, ensure required system packages are installed (varies by distro).
  • If using containers, confirm Docker/Podman permissions and that the daemon is running.

Recommended next steps

  • Create a repeatable workflow: document the model you use, the endpoint, and any environment variables.
  • Build a simple prompt template: keep a “system” style instruction and a few reusable task prompts.
  • Validate outputs: local AI is powerful, but you still need basic QA—especially for customer-facing text.

Once OpenClaw is talking to Ollama reliably, you have a stable base for experimenting with local AI features—without depending on a hosted API for every request.