OpenClaw Ollama Guide

A practical guide to pairing OpenClaw with Ollama for local model control, privacy, and self-hosted experimentation.

Mar 12, 2026

OpenClaw Ollama Guide

This setup matters when you want more local control over model choice and runtime behavior, or when privacy and cost constraints make cloud APIs less attractive.

Before you start

You should have OpenClaw running locally before adding Ollama to the stack.

Step 1: install Ollama

Download Ollama from ollama.ai
Install it for your platform (macOS, Linux, or Windows)
Verify it is running: ollama --version

Ollama runs as a local server, usually on http://localhost:11434.

Step 2: pull a model

Pull a model you want to use:

ollama pull llama3

Good starting models:

llama3 — general purpose, good balance of quality and speed
mistral — fast and efficient for simpler tasks
codellama — better for code-related tasks
qwen2 — strong multilingual support

Start with one model. Do not pull five models before testing one.

Step 3: verify Ollama is responding

Test that the model works:

ollama run llama3 "Hello, how are you?"

If this returns a sensible response, Ollama is ready.

Step 4: configure OpenClaw to use Ollama

Add Ollama as a model provider in your OpenClaw config:

{
  "providers": {
    "ollama": {
      "baseUrl": "http://localhost:11434",
      "model": "llama3"
    }
  }
}

Then set Ollama as your default provider or assign it to specific tasks.

Step 5: test with a simple task

Restart Gateway and send a simple message through your connected channel. The assistant should respond using the local Ollama model.

If it does not:

confirm Ollama is running (ollama ps)
check the baseUrl in your config
verify the model name matches what you pulled
look at Gateway logs for connection errors

When Ollama is a good fit

research drafts and summaries
classification or triage tasks
local-first experimentation
privacy-sensitive workflows
cost reduction for high-volume simple tasks

When Ollama is not ideal

tasks that need the strongest reasoning quality every time
highly variable tasks with strict accuracy expectations
when you have not validated your prompts on a simpler hosted path first

Performance considerations

Local models run on your hardware. Performance depends on:

GPU: a dedicated GPU dramatically improves speed
RAM: larger models need more memory (7B models need ~8GB, 13B models need ~16GB+)
model size: smaller models are faster but less capable

If responses are slow, try a smaller model or check your hardware utilization.