The Ultimate Guide to Using Ollama with OpenClaw

Cloud AI is great, but Local AI is freedom. Freedom from subscriptions, freedom from privacy concerns, and freedom from downtime.

OpenClaw was built to be model-agnostic, but its best friend is undoubtedly Ollama. Together, they turn your computer into an autonomous AI powerhouse.

What is Ollama?

Ollama is a tool that allows you to run large language models (LLMs) locally. It handles the complex “inference” part—loading the model into memory and generating text. It exposes a simple API that OpenClaw talks to.

Setting Up the Integration

1. The “Server”

Ollama needs to be running in the background. On macOS and Windows, the desktop app handles this automatically. On Linux:

ollama serve

2. The “Client” (OpenClaw)

OpenClaw sends prompts to Ollama. You just need to tell it where Ollama is listening (usually http://localhost:11434).

Choosing the Right Model

Not all local models are created equal. Here are our top picks for OpenClaw agents in 2026:

The All-Rounders

• Llama 3.2 (8B): incredible speed and reasoning for its size. Perfect for most MacBooks.
• Mistral Large 2: If you have 24GB+ RAM, this rivals GPT-4.

The Specialized

• CodeLlama / DeepSeek-Coder: Use these if you primarily use OpenClaw for coding tasks.
• Phi-4: Tiny but mighty. Great for older laptops or background tasks.

Advanced Configuration

Context Window

By default, Ollama might limit context to 4k or 8k tokens. OpenClaw can handle much more. You can increase this in your specific model file (Modelfile) in Ollama:

PARAMETER num_ctx 32768

Then rebuild the model: ollama create my-large-model -f Modelfile.

Temperature

For an agent that takes actions (like OpenClaw), a lower temperature is usually better to ensure reliability. OpenClaw defaults to 0.0 for tool use, but you can tweak this in config.json.

Troubleshooting

• “Connection Refused”: Make sure Ollama is actually running! Check your menu bar (Mac) or task tray (Windows).
• “Model not found”: Ensure you’ve run ollama pull [modelname] before trying to use it in OpenClaw.
• Slowness: Check if your model fits in VRAM (ollama ps). If it’s spilling to system RAM, it will be slow. Try a smaller “quantization” (e.g., q4_k_m).

The Local Future

Running OpenClaw with Ollama feels like magic. There’s zero latency network lag. You can drag a file into your chat, and OpenClaw reads it instantly.

Get started today by installing OpenClaw.