OpenClaw + Ollama: How to Run a Fully Local AI Agent for Free

Quick Answer

You can run OpenClaw with Ollama to get a fully local AI agent that costs nothing to operate after the initial setup. Ollama runs open AI models on your own hardware, and OpenClaw turns those models into functional agents that can take actions, use tools, and connect to your apps. Everything stays on your machine — no data leaves, no API keys required, no monthly bills.

Why Go Local?

Running your AI agent locally has three major advantages. First, privacy. Every message you send, every document you process, every task you automate — it all stays on your hardware. Nothing is sent to a third-party server. For anyone handling sensitive information, this is a game-changer.

Second, cost. Cloud API calls add up quickly. If you are using GPT-4 or Claude heavily, you can easily spend hundreds of pounds per month. A local setup costs nothing per message after the initial hardware investment. If you already have a decent computer, your investment is zero.

Third, no rate limits. Cloud APIs throttle you when you send too many requests. A local model runs as fast as your hardware allows, with no queuing, no waiting, and no "you have exceeded your rate limit" errors.

What You Need

The hardware requirements depend on which model you want to run. Here is a practical breakdown:

Minimum viable setup (7B-8B models): 16GB RAM, any modern CPU. No GPU required, though it will be slower. This handles basic agent tasks — answering questions, simple tool calling, short conversations. Good enough to get started and experiment.

Recommended setup (14B models): 32GB RAM, a GPU with 8GB or more VRAM (like an NVIDIA RTX 3060 or better). This gives you noticeably better responses, more reliable tool calling, and longer context handling. The sweet spot for most people.

Power setup (70B+ models): 64GB RAM, a GPU with 24GB+ VRAM (like an RTX 4090). This runs models that rival cloud APIs in quality. Overkill for most users, but incredible if you have the hardware.

Most modern laptops with 16GB RAM can run 7B-8B models. If you bought a computer in the last three to four years, you can probably get started today.

Step 1: Install Ollama

Ollama is the easiest way to run AI models locally. It handles downloading model weights, managing memory, and serving the model through a local API — all with a single command.

Head to ollama.com and download the installer for your operating system. Ollama supports macOS, Linux, and Windows. Installation takes about two minutes.

Once installed, open your terminal and pull a model. For a great starting point, try Qwen 3.5 at 14B parameters. The community currently recommends the Qwen 3 series for the best balance of quality and speed with OpenClaw. DeepSeek R1 and Llama 3.3 are also solid choices.

The model will download (a few gigabytes depending on size) and then you are ready. You can test it by chatting directly in the terminal to make sure everything is working.

Step 2: Install OpenClaw

With Ollama running, now install OpenClaw. The easiest method is using Docker. Make sure Docker is installed on your machine, then pull the official OpenClaw image and start it up.

During setup, you will need to configure OpenClaw to use Ollama as its model provider. This means pointing OpenClaw to the local API endpoint that Ollama exposes (usually localhost on port 11434). Set the API type to openai-completions, which enables the OpenAI-compatible communication that OpenClaw expects.

One important setting: context length. OpenClaw works best with a context length of at least 64,000 tokens. Make sure your Ollama model is configured with enough context for your agent to work effectively. Larger context means the agent can handle longer conversations and more complex tasks without losing track.

Step 3: Pick Your Model

Not all models are created equal when it comes to agent tasks. The key capability you need is reliable tool calling — the ability for the model to correctly identify when to use a tool, which tool to use, and what parameters to pass.

Based on community testing in early 2026, the best models for OpenClaw agent work are Qwen 3.5 (excellent tool calling, fast, multilingual), DeepSeek R1 (strong reasoning, good for complex tasks), and Llama 3.3 or Llama 4 Scout (solid all-rounders with massive context). At minimum, use a 14B parameter model or larger. The 8B models work but are more likely to hallucinate tool calls or forget context during longer interactions.

All cost values in your OpenClaw configuration should be set to zero since the model runs locally — no per-token charges.

Step 4: Connect Your Channels

Your local agent can still connect to messaging platforms. OpenClaw supports WhatsApp, Telegram, Discord, Slack, and many more. The difference is that messages come in, get processed locally on your machine, and responses go back out — the AI thinking happens entirely on your hardware.

For a quick start, Telegram is the easiest channel to set up. Create a bot through Telegram's BotFather, grab the API token, and add it to your OpenClaw configuration. You will be chatting with your local agent through Telegram within five minutes.

Step 5: Install Skills

A model on its own can only chat. Skills are what turn it into an agent. Browse ClawHub for skills that match your needs. Web search, file management, calendar access, email — whatever you want your agent to do, there is probably a skill for it.

Keep in mind that some skills require external API access (like web search skills that call search engine APIs). These will still work with a local model, but the API calls themselves go to external services. Your prompts and the AI processing remain local — only the specific tool actions reach the internet.

When to Go Cloud Instead

Local is brilliant for privacy and cost, but it is not always the best choice. If you need your agent running 24/7 and you do not want to leave your computer on all the time, a cloud deployment makes more sense. If you want to use the largest, most capable models without buying expensive hardware, cloud is the way to go.

This is where Tulip comes in. Tulip gives you the same OpenClaw setup but running on managed cloud infrastructure with access to the full range of open models. You get the benefits of open-source agents without managing hardware. Many people start local to learn and experiment, then move to Tulip when they want reliability and scale.

Frequently Asked Questions

How much disk space do I need?

A 7B model takes about 4GB of disk space. A 14B model takes about 8GB. A 70B model takes about 40GB. You will also need space for OpenClaw itself and any skills you install, but these are small.

Will this slow down my computer?

While your agent is actively processing a message, yes — it will use significant CPU or GPU resources. When idle, resource usage is minimal. If you find it impacts your daily work, consider running the agent on a separate machine or a cheap mini PC.

Can I run this on a Raspberry Pi?

Yes, but with limitations. A Raspberry Pi 5 with 8GB RAM can run very small models (3B parameters or less). It will be slow but functional for simple tasks. Check out our dedicated guide on running OpenClaw on a Raspberry Pi for more details.

Is there a way to get the privacy benefits of local with the reliability of cloud?

Yes. Tulip runs on renewable-powered bare metal infrastructure where your data stays within the platform. You get cloud reliability with a stronger privacy posture than typical cloud providers. It is a middle ground that works well for people who want always-on agents without self-hosting.