---
title: marimo
---

## Install

Install [marimo](https://marimo.io). You can use `pip` or `uv` for this. You 
can also use `uv` to create a sandboxed environment for marimo by running:

```
uvx marimo edit --sandbox notebook.py
```

## Usage with Ollama

1. In marimo, go to the user settings and go to the AI tab. From here
you can find and configure Ollama as an AI provider. For local use you
would typically point the base url to `http://localhost:11434/v1`.

<div style={{ display: 'flex', justifyContent: 'center' }}>
  <img 
    src="/images/marimo-settings.png" 
    alt="Ollama settings in marimo"
    width="50%"
  />
</div>

2. Once the AI provider is set up, you can turn on/off specific AI models you'd like to access. 

<div style={{ display: 'flex', justifyContent: 'center' }}>
  <img 
    src="/images/marimo-models.png" 
    alt="Selecting an Ollama model"
    width="50%"
  />
</div>

3. You can also add a model to the list of available models by scrolling to the bottom and using the UI there. 

<div style={{ display: 'flex', justifyContent: 'center' }}>
  <img 
    src="/images/marimo-add-model.png" 
    alt="Adding a new Ollama model"
    width="50%"
  />
</div>

4. Once configured, you can now use Ollama for AI chats in marimo.

<div style={{ display: 'flex', justifyContent: 'center' }}>
  <img 
    src="/images/marimo-chat.png" 
    alt="Configure code completion"
    width="50%"
  />
</div>

4. Alternatively, you can now use Ollama for **inline code completion** in marimo. This can be configured in the "AI Features" tab. 

<div style={{ display: 'flex', justifyContent: 'center' }}>
  <img 
    src="/images/marimo-code-completion.png" 
    alt="Configure code completion"
    width="50%"
  />
</div>


## Connecting to ollama.com

1. Sign in to ollama cloud via `ollama signin` 
2. In the ollama model settings add a model that ollama hosts, like `gpt-oss:120b`.
3. You can now refer to this model in marimo!