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Documentation Index

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Use this file to discover all available pages before exploring further.

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Integrations

marimo integrates seamlessly with popular development tools, frameworks, and the broader Python ecosystem. This guide covers editor integrations, framework connections, and third-party tool support.

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VS Code Extension

marimo provides an official VS Code extension for editing and running notebooks directly in VS Code.

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Installation

# Install from VS Code marketplace
# Search for "marimo" in Extensions
# Publisher: marimo-team

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Features

  • Native notebook editing: Edit .py marimo notebooks as notebooks in VS Code
  • Cell execution: Run cells individually or all at once
  • Interactive outputs: View plots, tables, and UI elements inline
  • IntelliSense: Full Python language support with completions and hover info
  • Debugging: Use VS Code’s debugger with marimo notebooks
  • Git integration: Built-in diff and merge tools for notebook files

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Usage

# Open a marimo notebook in VS Code
code my_notebook.py

# The extension automatically detects marimo notebooks
# and provides the notebook interface

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Configuration

Configure the extension in VS Code settings: 
{
  "marimo.pythonPath": "/path/to/python",
  "marimo.port": 2718,
  "marimo.host": "localhost"
}
The VS Code extension runs a marimo server in the background. All editing happens in the browser-based marimo interface embedded in VS Code.

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File Watching Mode

Edit marimo notebooks in any text editor (Neovim, Zed, Emacs, etc.) with automatic reloading: 
# Start marimo in watch mode
marimo edit --watch notebook.py

# Edit notebook.py in your favorite editor
# Changes automatically reload in the browser
This enables:
  • Using your preferred editor/IDE
  • Vim keybindings, custom themes, plugins
  • Local development without browser-based editing

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Framework Integrations

marimo notebooks can be integrated with popular Python web frameworks.

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FastAPI Integration

Embed marimo notebooks as interactive endpoints in FastAPI applications: 
from fastapi import FastAPI
import marimo
import os

# Create marimo server with multiple notebooks
server = marimo.create_asgi_app()

# Add notebooks as routes
ui_dir = "./notebooks"
for filename in os.listdir(ui_dir):
    if filename.endswith(".py"):
        app_name = os.path.splitext(filename)[0]
        app_path = os.path.join(ui_dir, filename)
        server = server.with_app(path=f"/{app_name}", root=app_path)

# Create FastAPI app
app = FastAPI()

# Your FastAPI routes
@app.get("/api/health")
async def health():
    return {"status": "healthy"}

# Mount marimo server
app.mount("/notebooks", server.build())

# Run with: uvicorn main:app

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FastAPI with Authentication

From examples/frameworks/fastapi/main.py: 
from fastapi import FastAPI, Request, Depends, HTTPException
from starlette.middleware.sessions import SessionMiddleware
import marimo

app = FastAPI()

# Add session middleware
app.add_middleware(
    SessionMiddleware,
    secret_key="your-secret-key"
)

# Authentication middleware
@app.middleware("http")
async def auth_middleware(request: Request, call_next):
    if request.url.path.startswith("/notebooks"):
        if "username" not in request.session:
            raise HTTPException(status_code=401, detail="Not authenticated")
    return await call_next(request)

# Create and mount marimo server
server = marimo.create_asgi_app().with_app(
    path="/",
    root="notebook.py"
)

app.mount("/notebooks", server.build())

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Flask Integration

from flask import Flask
import marimo

app = Flask(__name__)

# Create marimo ASGI app
marimo_server = marimo.create_asgi_app().with_app(
    path="/",
    root="notebook.py"
).build()

# Mount using ASGI middleware
from asgiref.wsgi import WsgiToAsgi
app.wsgi_app = WsgiToAsgi(marimo_server)

if __name__ == "__main__":
    app.run()

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Starlette Integration

from starlette.applications import Starlette
from starlette.routing import Mount
import marimo

# Create marimo server
marimo_app = marimo.create_asgi_app().with_app(
    path="/",
    root="analysis.py"
).build()

# Create Starlette app with marimo mounted
app = Starlette(
    routes=[
        Mount("/dashboard", app=marimo_app),
    ]
)

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Third-Party Library Integrations

marimo works with a wide ecosystem of Python libraries. Here are some highlighted integrations:

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Data Visualization

import plotly.graph_objects as go
import marimo as mo

fig = go.Figure(data=go.Scatter(x=[1, 2, 3], y=[4, 5, 6]))
mo.ui.plotly(fig)  # Interactive Plotly charts

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Data Processing

import polars as pl
import marimo as mo

df = pl.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
mo.ui.table(df)  # Interactive tables with filtering

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Machine Learning

from sklearn.ensemble import RandomForestClassifier
import marimo as mo

model = RandomForestClassifier()
model.fit(X_train, y_train)

mo.md(f"Accuracy: {model.score(X_test, y_test):.2%}")

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Geospatial

import leafmap
import marimo as mo

# Create interactive map
m = leafmap.Map(center=[40, -100], zoom=4)
m.add_basemap("OpenStreetMap")

mo.ui.anywidget(m)  # Embed leaflet maps

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Database Connections

from sqlalchemy import create_engine
import marimo as mo

engine = create_engine('postgresql://user:pass@localhost/db')
df = mo.sql(
    f"SELECT * FROM users LIMIT 10",
    engine=engine
)

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Model Context Protocol (MCP)

marimo supports the Model Context Protocol for AI-powered development workflows.

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What is MCP?

MCP is a standard protocol for connecting AI models with development tools and data sources. It enables:
  • AI assistants to read and modify marimo notebooks
  • Context-aware code generation
  • Integration with Claude, GPT-4, and other LLMs
  • Tool-augmented AI interactions

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Using MCP with marimo

import marimo as mo

# MCP-compatible AI completion
code_suggestion = mo.ai.complete(
    prompt="Create a bar chart of sales by region",
    context={"available_data": df.columns.tolist()}
)

mo.md(f"```python\n{code_suggestion}\n```")

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Installing MCP Servers

marimo can connect to MCP servers for enhanced functionality: 
# Install marimo MCP server
pip install marimo[mcp]

# Configure MCP in marimo settings
marimo config --mcp-servers '{"claude": {"endpoint": "..."}}}'

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Custom MCP Tools

Create custom MCP tools for your workflows: 
from marimo.mcp import Tool

class DataAnalysisTool(Tool):
    name = "analyze_dataframe"
    description = "Analyze a pandas DataFrame"
    
    def execute(self, df, analysis_type):
        if analysis_type == "summary":
            return df.describe()
        elif analysis_type == "missing":
            return df.isnull().sum()
        # Add more analysis types...

# Register tool with MCP
mo.mcp.register_tool(DataAnalysisTool())
MCP support is evolving. Check marimo’s documentation for the latest features.

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Cloud Platforms

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Deploying to Cloud

marimo notebooks can be deployed to various cloud platforms: 
# Deploy marimo app to Railway
marimo export html notebook.py > index.html
# Or run as ASGI app
marimo run notebook.py --host 0.0.0.0 --port $PORT

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marimo Cloud

Deploy notebooks with one command: 
# Deploy to marimo cloud
marimo cloud deploy notebook.py

# Share with team
marimo cloud share notebook.py --team my-team

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Development Tools

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Git Integration

marimo notebooks are pure Python files with excellent git support: 
# Notebooks are readable diffs
git diff notebook.py

# Merge conflicts are standard Python
git merge feature-branch

# Use .gitattributes for better diffs
*.py diff=python

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Pre-commit Hooks

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/marimo-team/marimo
    rev: v0.15.0
    hooks:
      - id: marimo-format
        args: [--check]
      
      - id: marimo-test
        args: [--quiet]

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CI/CD Pipelines

# .github/workflows/test-notebooks.yml
name: Test Notebooks

on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-python@v4
        with:
          python-version: '3.11'
      
      - name: Install marimo
        run: pip install marimo pytest
      
      - name: Test notebooks
        run: |
          marimo test notebooks/*.py
          pytest notebooks/
      
      - name: Export notebooks
        run: |
          marimo export html notebooks/*.py --output dist/

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IDE Plugins

Beyond VS Code, marimo works with:
  • PyCharm: Edit as Python files with full IDE support
  • Jupyter Lab: Convert between formats with marimo export ipynb
  • Cursor: AI-powered editing with marimo notebooks
  • Zed: Fast, native editor with watch mode

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Example Integration Workflows

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Data Pipeline with FastAPI

# pipeline.py - marimo notebook
import marimo as mo
import polars as pl

app = mo.App()

@app.cell
def load_data():
    return pl.read_csv("data.csv")

@app.cell
def transform(data):
    return data.filter(pl.col("value") > 0)

@app.cell
def visualize(transformed):
    return mo.ui.plotly(create_chart(transformed))

# main.py - FastAPI app
from fastapi import FastAPI
import marimo

app = FastAPI()
marimo_server = marimo.create_asgi_app().with_app(
    path="/", root="pipeline.py"
).build()

app.mount("/pipeline", marimo_server)

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ML Monitoring Dashboard

import marimo as mo
import mlflow

app = mo.App()

@app.cell
def connect_mlflow():
    mlflow.set_tracking_uri("http://mlflow-server:5000")
    client = mlflow.tracking.MlflowClient()
    return client,

@app.cell  
def show_experiments(client):
    experiments = client.search_experiments()
    return mo.ui.table(experiments)

@app.cell
def plot_metrics(client, selected_experiment):
    runs = client.search_runs(selected_experiment.experiment_id)
    metrics = extract_metrics(runs)
    return mo.ui.plotly(plot_training_curves(metrics))

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