OpenClaw + Ollama: How to Run Local Models with Your Agent (2026)

Run OpenClaw with local models via Ollama. No API costs, full privacy. Supports CodeLlama, Qwen, Mistral, and more. Complete setup guide.

Quick Answer: Install Ollama (curl -fsSL https://ollama.ai/install.sh | sh), pull a coding model (ollama pull qwen2.5-coder:14b), configure OpenClaw to use the local endpoint (openclaw config set provider ollama), and start coding. Your code never leaves your machine. No API key needed.

Why should I run local models with OpenClaw?

Zero cost. No per-token charges. Code as much as you want without watching your API bill.

Full privacy. Your code, prompts, and outputs never leave your machine. No data sent to Anthropic, OpenAI, or any third party. Required for some corporate security policies.

Offline capability. After pulling the model, you can code without internet access.

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How do I install Ollama?

macOS: brew install ollama or download from ollama.ai.

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

Windows: download the installer from ollama.ai. Runs natively on Windows.

Start the Ollama server: ollama serve

Which model should I pull?

For general coding:

ollama pull qwen2.5-coder:14b

For smaller machines:

ollama pull qwen2.5-coder:7b

For maximum quality (needs 32GB+ RAM):

ollama pull deepseek-coder-v2:33b

How do I configure OpenClaw to use Ollama?

Set the provider and model:

openclaw config set provider ollama
openclaw config set model qwen2.5-coder:14b
openclaw config set endpoint http://localhost:11434

Start OpenClaw normally:

openclaw

OpenClaw connects to the local Ollama server instead of a cloud API.

What are the best local models for coding?

Qwen 2.5 Coder 14B is the best balance of quality and speed for most hardware. It handles code review, refactoring, and generation well on machines with 16GB+ RAM.

CodeLlama 13B is a solid alternative with good instruction-following for coding tasks.

DeepSeek Coder V2 33B produces the highest quality output but needs 32GB+ RAM and is slower without a GPU.

Do SKILL.md skills work with local models?

SKILL.md skills work identically with local models. The model reads the skill instructions the same way a cloud model does. Quality depends on the model's capability, so larger models follow skill instructions more reliably.

code-reviewer (764 installs) works well with 14B+ models. Free.

git-commit-writer (232 installs) works well even with 7B models because commit messages are a simpler task. Free.

Browse all skills on Agensi.

How do I improve local model performance?

GPU acceleration: if you have an NVIDIA GPU, Ollama uses it automatically. Apple Silicon Macs use the GPU cores in the unified memory.

Context window: larger context windows use more memory. If you run out of RAM, reduce the context window in Ollama's config.

Concurrent requests: if running subagents, each concurrent request uses memory. Plan for 2x the model size in RAM for parallel workflows.