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In-editor AI assistance

Using agents like Claude Code or OpenCode? Try marimo pair instead.

This guide covers AI assistance built into the marimo editor. To work with coding agents such as Claude Code or OpenCode, use marimo pair instead.

marimo's editor lets you use AI to author and modify your notebook. Use AI to:

  • generate new cells and refactor existing ones;
  • complete code snippets with copilots.

Unlike traditional assistants that only have access to the text of your program, marimo's assistant has access to the values of variables in memory, letting it code against your dataframe and database schemas.

This guide provides an overview of these features and how to configure them.

Locating your marimo.toml config file

Various instructions in this guide refer to the marimo.toml configuration file. Locate this file with marimo config show | head.

Connecting to an LLM provider

Connect to an LLM through the notebook settings menu.

You can configure the following providers:

  • OpenAI
  • GitHub Copilot
  • Anthropic
  • AWS Bedrock
  • Google AI
  • Ollama
  • and any OpenAI-compatible provider

See the llm_providers guide for detailed instructions on how to configure each provider.

Generating cells with AI

marimo has built-in support for generating and refactoring code with LLMs.

Enabling AI code generation. To enable AI code generation, first install required dependencies through the notebook settings.

Install required dependencies for AI generation through the notebook settings.

Then configure your LLM provider through the AI tab in the settings menu; see the section on connecting your LLM for detailed instructions.

Variable context

marimo provides models your notebook code as context. You can additionally supply variables and their values by referencing them by name with @. For example, to include the columns of a dataframe df in your prompt, write @df.

Pass variables to your prompt by tagging them with `@`.

Generate new cells

Generate with AI button

At the bottom of every notebook is a button titled "Generate with AI". Click this button to add entirely new cells to your notebook.

Chat panel

The chat panel on the left sidebar lets you chat with an LLM and ask questions about your notebook. The LLM can also generate code cells that you can insert into your notebook.

Use the Notebook access selector to choose what the assistant can do:

  • Read only: Read notebook code, outputs, and variable previews.
  • Suggest edits: Read the notebook and propose cell changes for review.
  • Full access: Read, edit, and run cells, with Python scratchpad access.

Suggest edits uses cell proposals that you can keep or discard. Full access can modify the notebook directly and execute Python in the live kernel.

See AI tools for details about notebook tools and MCP integrations. Web search and fetch can be enabled separately at any notebook access level.

The ai.mode configuration values are ask for Read only (the default), agent for Suggest edits, and code_mode for Full access.

Refactor existing cells

Make edits to an existing cell by inputting Ctrl/Cmd-shift-e while a cell is focused. This opens a prompt box that has your cell's code as input.

Custom rules

You can customize how the AI assistant behaves by adding rules in the marimo settings. These rules help ensure consistent code generation across all AI providers. You can find more information about marimo's supported plotting libraries and data handling in the plotting guide and working with data guide.

Configure custom AI rules in settings

For example, you can add rules about:

  • Preferred plotting libraries (matplotlib, plotly, altair)
  • Data handling practices
  • Code style conventions
  • Error handling preferences

Example custom rules:

Use plotly for interactive visualizations and matplotlib for static plots
Prefer polars over pandas for data manipulation due to better performance
Include docstrings for all functions using NumPy style
Use Type hints for all function parameters and return values
Handle errors with try/except blocks and provide informative error messages
Follow PEP 8 style guidelines
When working with data:
- Use altair, plotly for declarative visualizations
- Prefer polars over pandas
- Ensure proper error handling for data operations
For plotting:
- Use px.scatter for scatter plots
- Use px.line for time series
- Include proper axis labels and titles
- Set appropriate color schemes

Copilots

Copilots allow you to tab-complete code based on your notebook's context, similar to editors like Cursor.

GitHub Copilot

The marimo editor natively supports GitHub Copilot, an AI pair programmer, similar to VS Code:

  1. Install Node.js.
  2. Enable Copilot via the settings menu in the marimo editor.
Follow these instructions.

GitHub Copilot is not yet available in our conda distribution; please install marimo using pip/uv if you need Copilot.

Advanced configuration

You can customize GitHub Copilot's behavior by adding copilot_settings to your marimo.toml configuration file:

marimo.toml
[ai.github]
copilot_settings = { http = { proxy = "http://proxy.example.com:8888", proxyStrictSSL = true } }

Or in a more readable format:

marimo.toml
[ai.github.copilot_settings.http]
proxy = "http://proxy.example.com:8888"
proxyStrictSSL = true

[ai.github.copilot_settings.github-enterprise]
uri = "https://github.enterprise.com"  # For GitHub Enterprise users

Available configuration options (these are the same settings directory from the npm package:

  • HTTP settings: Configure proxy settings for network connections

    • proxy: HTTP proxy URL (e.g., "http://proxy.example.com:8888")
    • proxyStrictSSL: Whether to verify SSL certificates for the proxy (default: false)
    • proxyKerberosServicePrincipal: Kerberos service principal for proxy authentication

  • Telemetry settings: Control telemetry data collection

    • telemetryLevel: Level of telemetry to send - "off", "crash", "error", or "all" (default: "off")

  • GitHub Enterprise: Configure GitHub Enterprise Server

    • uri: URL of your GitHub Enterprise Server instance

Custom copilots

marimo also supports integrating with custom LLM providers for code completion suggestions. This allows you to use your own LLM service to provide in-line code suggestions based on internal providers or local models (e.g. Ollama). You may also use OpenAI, Anthropic, Google, or any other providers by providing your own API keys and configuration.

To configure a custom copilot:

  1. Ensure you have an LLM provider that offers API access for code completion (either external or running locally)
  2. Add the following configuration to your marimo.toml (or configure in the UI settings in the editor):
marimo.toml
[ai.models]
autocomplete_model = "provider/model-name"

[completion]
copilot = "custom"

The configuration options include:

  • autocomplete_model: The specific model to use for inline autocompletion.
  • copilot: The name of the copilot to use for code generation.

Disable in-editor AI features

Disable in-editor AI features, and hide their associated user interfaces, with the following configuration:

marimo.toml
[ai]
enabled = false

[completion]
copilot = false