Converting Streamlit Apps to Marimo

For general marimo notebook conventions (cell structure, PEP 723 metadata, output rendering,

marimo check

, variable naming, etc.), refer to the

marimo-notebook

skill. This skill focuses specifically on

mapping Streamlit concepts to marimo equivalents

.

Steps

Read the Streamlit app

to understand its widgets, layout, and state management.

Create a new marimo notebook

following the

marimo-notebook

skill conventions. Add all dependencies the Streamlit app uses (pandas, plotly, altair, etc.) — but replace

streamlit

with

marimo

. You should not overwrite the original file.

Map Streamlit components to marimo equivalents

using the reference tables below. Key principles:

UI elements are

assigned to variables

and their current value is accessed via

.value

.

Cells that reference a UI element automatically re-run when the user interacts with it — no callbacks needed.

Handle conceptual differences

in execution model, state, and caching (see below).

Run

uvx marimo check

on the result and fix any issues.

Widget Mapping Reference

Input Widgets

Streamlit

marimo

Notes

st.slider()

mo.ui.slider()

st.select_slider()

mo.ui.slider(steps=[...])

Pass discrete values via

steps

st.text_input()

mo.ui.text()

st.text_area()

mo.ui.text_area()

st.number_input()

mo.ui.number()

st.checkbox()

mo.ui.checkbox()

st.toggle()

mo.ui.switch()

st.radio()

mo.ui.radio()

st.selectbox()

mo.ui.dropdown()

st.multiselect()

mo.ui.multiselect()

st.date_input()

mo.ui.date()

st.time_input()

mo.ui.text()

No dedicated time widget

st.file_uploader()

mo.ui.file()

Use

.contents()

to read bytes

st.color_picker()

mo.ui.text(value="#000000")

No dedicated color picker

st.button()

mo.ui.button()

or

mo.ui.run_button()

Use

run_button

for triggering expensive computations

st.download_button()

mo.download()

Returns a download link element

st.form()

+

st.form_submit_button()

mo.ui.form(element)

Wraps any element so its value only updates on submit

Display Elements

Streamlit

marimo

Notes

st.write()

mo.md()

or last expression

st.markdown()

mo.md()

Supports f-strings:

mo.md(f"Value: {x.value}")

st.latex()

mo.md(r"$...$")

marimo uses KaTeX; see

references/latex.md

st.code()

mo.md("python\n...\n")

st.dataframe()

df

(last expression)

DataFrames render as interactive marimo widgets natively; use

mo.ui.dataframe(df)

only for no-code transformations

st.table()

df

(last expression)

Use

mo.ui.table(df)

if you need row selection

st.metric()

mo.stat()

st.json()

mo.json()

or

mo.tree()

mo.tree()

for interactive collapsible view

st.image()

mo.image()

st.audio()

mo.audio()

st.video()

mo.video()

Charts

Streamlit

marimo

Notes

st.plotly_chart(fig)

fig

(last expression)

Use

mo.ui.plotly(fig)

for selections

st.altair_chart(chart)

chart

(last expression)

Use

mo.ui.altair_chart(chart)

for selections

st.pyplot(fig)

fig

(last expression)

Use

mo.ui.matplotlib(fig)

for interactive matplotlib

Layout

Streamlit

marimo

Notes

st.sidebar

mo.sidebar([...])

Pass a list of elements

st.columns()

mo.hstack([...])

Use

widths=[...]

for column ratios

st.tabs()

mo.ui.tabs({...})

Dict of

{"Tab Name": content}

st.expander()

mo.accordion({...})

Dict of

{"Title": content}

st.container()

mo.vstack([...])

st.empty()

mo.output.replace()

st.progress()

mo.status.progress_bar()

st.spinner()

mo.status.spinner()

Context manager

Key Conceptual Differences

Execution Model

Streamlit reruns the

entire script

top-to-bottom on every interaction. Marimo uses a

reactive cell DAG

— only cells that depend on changed variables re-execute.

No need for

st.rerun()

— reactivity is automatic.

No need for

st.stop()

— structure cells so downstream cells naturally depend on upstream values.

State Management

Streamlit

marimo

st.session_state["key"]

Regular Python variables between cells

Callback functions (

on_change

)

Cells referencing

widget.value

re-run automatically

st.query_params

mo.query_params

Caching

Streamlit

marimo

@st.cache_data

@mo.cache

@st.cache_resource

@mo.persistent_cache

@mo.cache

is the primary caching decorator — it works like

functools.cache

but is aware of marimo's reactivity.

@mo.persistent_cache

goes further by persisting results to disk across sessions, useful for expensive computations like model training.

Multi-Page Apps

Marimo offers two approaches for multi-page Streamlit apps:

Single notebook with routing

Use

mo.routes

with

mo.nav_menu

or

mo.sidebar

to build multiple "pages" (tabs/routes) inside one notebook.

Multiple notebooks as a gallery

Run a folder of notebooks with marimo run folder/ to serve them as a gallery with navigation. Deploying marimo features molab to host marimo apps instead of the streamlit community cloud. You can generate an "open in molab" button via the add-molab-badge skill. Custom components streamlit has a feature for custom components. These are not compatible with marimo. You might be able to generate an equivalent anywidget via the marimo-anywidget skill but discuss this with the user before working on that.