Ollama Just Made OpenClaw Free. Here Is How I Set It Up.

For a while, running OpenClaw seriously meant one thing: a Claude API subscription that chewed through your budget faster than you expected.

My own bill was sitting at around $40 a month for what I would describe as light to moderate agent use. Not catastrophic, but it was always there.

Then Ollama shipped an update that changed the math entirely.

Starting with Ollama 0.17, you can launch OpenClaw with a single command and it defaults to Kimi K2.5, a cloud model from Moonshot AI, at zero cost. That means web search included, 64k context window, and a reasoning model capable enough for real tasks, all without touching your API wallet.

I set it up the day it landed and ran it through a full day of work. Here is what you need to know before you do the same.

If you are new to OpenClaw, our beginner-friendly OpenClaw guide covers the basics first.

What the Ollama 0.17 Update Changed

Ollama has supported local AI models for a while. This update is different. Starting with Ollama 0.17, the software ships with a compatibility layer for the Anthropic Messages API.

That matters because OpenClaw, like Claude Code, was designed to speak the Anthropic message format. It handles tool calls, including reading files, running web searches, and executing shell commands, more reliably through that format than through the older OpenAI-style endpoint.

The result is that Ollama now acts as a translator. You point OpenClaw at your local Ollama instance, it sends a request in Anthropic’s format, Ollama translates it for the underlying model, and the response comes back in the format OpenClaw expects.

The bridge is invisible in practice. From OpenClaw’s side, it behaves exactly like it would talking to Claude directly.

Worth knowing: OpenClaw used to go by the name Clawdbot. Our original Clawdbot review covers what it was like before the rebrand. The ollama launch clawdbot command still works as an alias if you see it in older tutorials.

Why Kimi K2.5 Instead of a Local Model

Most local models worth running for agent tasks require significant GPU memory, something in the range of 25 GB of VRAM for the stronger options. That rules out most consumer setups entirely.

Kimi K2.5 sidesteps this by running on NVIDIA cloud infrastructure. You get a model with solid reasoning and instruction-following, a full 64k context window (which OpenClaw needs for longer tasks), and a built-in web search plugin that Ollama installs automatically when you use the cloud variant.

The free tier has been generous in practice. Multiple users running it through production workflows report never hitting a rate limit on day-to-day tasks.

How to Set Up OpenClaw with Ollama and Kimi K2.5

The whole process takes about ten minutes. You need Ollama 0.17 or later, Node.js installed, and a Mac, Linux system, or Windows machine running WSL.

According to Ollama’s release notes on GitHub, version 0.17 is where the Anthropic Messages API bridge landed. Confirm you are on at least that version before starting.

1. Check your Ollama version by running ollama --version in your terminal. Update at ollama.com if you are below 0.17.
2. Run the launch command below. Ollama will detect if OpenClaw is missing and install it automatically via npm before starting.
3. Let the onboarding wizard run. It asks which messaging service you want to connect (WhatsApp, Telegram, Slack, Discord, or others) and walks you through authentication for each one.
4. When prompted for a model, select Kimi K2.5 cloud, or pass the model flag directly in the launch command.
5. Confirm the web search plugin loaded. It appears automatically for the cloud variant. You will see a confirmation in the terminal output.

The exact launch command:

ollama launch openclaw --model kimi-k2.5:cloud

If you prefer to choose the model interactively:

ollama launch openclaw

This drops you into the configuration menu where you can pick the model, configure channels, and set up approvals.

Before vs. After:

Before (Claude API): Create API key. Track usage per token. Watch costs climb on longer tasks. Manually refresh if you hit rate limits mid-session.

After (Ollama + Kimi K2.5): One command. Model runs on NVIDIA cloud. No API key needed. Web search included. Costs nothing.

Is Kimi K2.5 Good Enough for Real Work

The short answer: yes, for most everyday agent tasks.

What I found is that Kimi K2.5 handles the kinds of things OpenClaw is built for: drafting emails, summarizing long threads, running web searches, building simple apps, and executing multi-step file tasks, all without any notable quality drop from the paid Claude setup.

The reasoning follows instructions accurately across multi-step tasks and does not drift mid-sequence the way weaker models tend to.

The latency is higher than a fully local model running on strong hardware, but not significantly more than what you would see from a standard API call to any hosted model.

For interactive agent work, it is not a bottleneck.

The built-in web search is genuinely useful. It pulls in current information, which matters when you run tasks that touch anything time-sensitive.

I ran a research summary task that needed recent pricing data and got results that would have been stale from a static model.

For a sense of what kinds of workflows you can build around this, the OpenClaw automation ideas page covers a solid range of task types worth trying.

Where Kimi K2.5 Falls Short

Two areas are worth knowing before you go all-in. First, very long and complex reasoning chains, the kind where you need to hold dozens of conditions in context simultaneously, can show more degradation than Claude Opus would.

If you are building multi-agent pipelines with heavy decision trees, the quality ceiling matters.

Second, because the model runs on cloud infrastructure, you are trusting NVIDIA’s data centers with the content of your queries. For most task types this is fine.

For sensitive documents, it is not. The local runtime path handles this, covered in the next section.

The Data Privacy Question Nobody Is Asking

Every guide I have seen tells you to run the launch command and move on. None of them explains what that means for your data.

When you use the cloud variant, your prompts and the content of your tool calls go to Moonshot AI’s API via NVIDIA infrastructure.

The free access is funded by Moonshot AI as a growth play, similar to how early AI providers offered free API tiers to build adoption.

Free access in exchange for usage data is a reasonable trade for many tasks. It is not a reasonable trade for everything.

Here is how I think about what to route where:

The local runtime path in OpenClaw routes requests through a model running on your own hardware. To use it, pull a compatible local model in Ollama (glm-4.7-flash is a reasonable option if you have the VRAM), then launch OpenClaw pointing to that model instead of the cloud one.

Privacy-sensitive tasks stay completely on-premises.

How to Cut Your AI Agent Costs Down to Almost Nothing

The cost strategy that makes sense here is not all-or-nothing. Run the free Kimi K2.5 cloud model for the bulk of your agent tasks. Reserve a paid model only for the hardest tasks where quality matters most.

One user who surfaced this update put it clearly: they use Kimi K2.5 via Ollama for everyday tasks and almost never pay for Claude anymore.

For light to moderate OpenClaw use, the free tier covers it. From my own testing, the hybrid approach below is where I landed after a full week of use.

If you want something fully managed instead of self-hosting, Dynamiq offers a cloud-hosted agent platform that handles the infrastructure for you.

Worth considering if running your own Ollama instance sounds like too much to maintain long-term.

What to Know Before You Start Running OpenClaw

The tool is powerful, and that is not a warning to scare you off it. OpenClaw has access to your shell, filesystem, and network. That access is what makes it useful.

It is also what makes it worth taking seriously before you start. You can get a deeper look at how the OpenClaw agent architecture works if you want to understand what is running under the hood before you hand it access to your machine.

The setup I would recommend for most people:

1. Run OpenClaw in an isolated directory or environment, not from your home root.
2. Keep approvals turned on for tool calls, especially for anything touching the filesystem or network.
3. Start with read-only tasks before granting write access.
4. For the cloud Kimi K2.5 path, do not route anything you would not put in a standard email.
5. For existing OpenClaw users switching from another model, the change is frictionless. Run the new launch command, select Kimi K2.5 when prompted, and your messaging channel connections stay in place.

The approval prompts feel tedious at first and start to feel invisible within a few days. Resist the urge to disable them permanently.

They catch the mistakes that matter most.