Run Claude Code and OpenClaw in Ollama gives you a simple way to set up a full AI development stack without depending on cloud APIs.
This lets you work faster, avoid token limits, and use powerful models for free.
It becomes the core of a clean SOP you can reuse every time you need a fresh environment.
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Most people think setting up local AI tools requires complicated installation steps.
The truth is much simpler.
Once you follow this SOP, you can run Claude Code and OpenClaw in Ollama in a few minutes, and the system stays stable for every future project.
This tutorial breaks everything down in a clear, repeatable process.
Let’s start with the foundation and build from there.
Install Ollama for a Clean Start Before You Run Claude Code and OpenClaw in Ollama
Download Ollama from the official site.
Install it like any normal application.
Open it once to confirm it runs properly in the background.
This creates the local environment that powers everything else.
Ollama becomes the engine that replaces cloud APIs and gives you local inference at zero cost.
Once Ollama is running, you have the base layer to proceed with the SOP.
Pull Your First Model to Prepare to Run Claude Code and OpenClaw in Ollama
Choose a model that fits your machine.
Many people start with GLM 4.7 Flash because it offers strong performance and runs smoothly on modern hardware.
Use a command like:
ollama pull glm-4.7-flash
The download creates a local file containing everything the model needs to run offline.
This step only happens once.
Future runs use the local copy instantly.
After the model finishes downloading, your environment can now support Claude Code and OpenClaw without relying on paid APIs.
Run the Model Locally as the Foundation for Run Claude Code and OpenClaw in Ollama
Start the model with:
ollama run glm-4.7-flash
This checks that everything loads correctly.
It also confirms your machine can generate responses at a stable speed.
Once this works, Ollama is ready to integrate with Claude Code and OpenClaw.
Local generation forms the backbone of the entire SOP.
Without this step, no tool can connect to the model.
Launch Claude Code With Your Local Model to Begin Run Claude Code and OpenClaw in Ollama
Claude Code works with local inference through a simple command.
Open a terminal and start Claude Code with your chosen model.
The binding process tells Claude Code to use your local model instead of a cloud API.
Now Claude Code becomes a full development assistant running entirely on your machine.
You avoid token limits.
You avoid rate limits.
You avoid cloud throttling.
Once Claude Code responds correctly, the integration is complete.
Set Up OpenClaw to Use the Same Local Model for Run Claude Code and OpenClaw in Ollama
OpenClaw can also connect directly to your local model through Ollama.
Start OpenClaw as usual.
When prompted to select a model source, choose the Ollama gateway.
This replaces cloud calls with local inference.
The gateway confirms the connection.
After that, every OpenClaw agent call flows through your machine.
The system becomes faster because there is no latency or network delay.
This step completes the integration between Claude Code, OpenClaw, and Ollama.
Test the Environment to Confirm Run Claude Code and OpenClaw in Ollama Is Working
Open a fresh Claude Code window.
Ask it to build a quick landing page.
Watch how fast it responds.
Open a new OpenClaw terminal.
Ask it to perform a simple coding task.
Notice how both tools use the same local model without any cloud dependency.
Testing removes uncertainty and proves the environment is stable.
This also shows you how much faster local inference feels compared to cloud calls.
Build a Simple SOP for Your Future Projects With Run Claude Code and OpenClaw in Ollama
Most builders repeat the same steps daily.
Turning these steps into an SOP makes the setup instant.
Use a structure like this:
-
Start Ollama
-
Load the model
-
Open Claude Code
-
Launch OpenClaw
-
Confirm the model is active
-
Begin development or automation tasks
This removes friction and lets you focus on the work that matters.
A repeatable SOP means you never waste time reconfiguring tools.
It also helps you train team members easily if you scale your operations.
Optimize Performance After You Run Claude Code and OpenClaw in Ollama
Local systems improve as your hardware improves.
If you want more speed, add more RAM.
If you want faster token output, use a stronger GPU.
If you work with long documents, increase system memory.
Performance tuning becomes predictable because everything runs locally.
You stay in control of the system instead of depending on outside servers.
This makes the workflow far more reliable.
Store Commands and Notes to Speed Up Run Claude Code and OpenClaw in Ollama
Save your commands in a simple text file.
Keep notes on which models run best on your machine.
Write down any tweaks that help performance.
This keeps your environment consistent across every session.
Small details make your SOP easier to follow later.
Everything becomes faster once you remove guesswork.
Once you’re ready to level up, check out Julian Goldie’s FREE AI Success Lab Community here:
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Inside, you’ll get step-by-step workflows, templates, and tutorials showing exactly how creators use AI to automate content, marketing, and workflows.
It’s free to join — and it’s where people learn how to use AI to save time and make real progress.
If you want to explore the full OpenClaw guide, including detailed setup instructions, feature breakdowns, and practical usage tips, check it out here: https://www.getopenclaw.ai/
FAQ
1. Where can I get templates to automate this?
Inside the AI Success Lab, with full SOP libraries and Ollama workflows.
2. Do I need strong hardware to run these models?
No. Lightweight models run well on most modern machines.
3. Can Claude Code and OpenClaw run at the same time?
Yes. Both tools can use the same local model through Ollama.
4. What happens if my internet disconnects?
Nothing breaks. Local inference keeps working.
5. Why use this setup instead of cloud APIs?
You get faster response times, zero downtime, and no token costs.
