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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).
-
Runuvx 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() | |
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 |
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) | (last expression) |
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 |
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.
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