Streamlit To Marimo

作者 marimo-team6454470960d3无许可证174 个星标收录于 2026年10月8日更新于 2026年10月8日仓库7周前更新

Convert a Streamlit app to a marimo notebook

仅含说明Software Development

AI 生成的概览

通过将组件、布局、状态和缓存映射到 marimo 对应项，把 Streamlit 应用转换为 marimo 笔记本。

功能: 该技能指导如何将现有的 Streamlit 应用转换为 marimo 笔记本。它提供参考表，把 Streamlit 的输入组件、显示元素、图表和布局容器映射到对应的 marimo 实现，并说明执行模型、状态管理、缓存、多页应用和部署方面的概念差异。它还要求对结果运行 marimo 检查并修复问题。
适用场景: 当你已有 Streamlit 应用并希望迁移到 marimo 时使用。它适合需要把 Streamlit 组件、响应式机制、会话状态或缓存转换为 marimo 约定的用户。
运行要求: 需要 marimo 包以及用于运行 marimo check 的 uvx 工具；还引用了 marimo-notebook、add-molab-badge 和 marimo-anywidget 技能。它不附带脚本，仅为说明文档。

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

Caching

@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.

来源与署名

来源：marimo-team/skills位于skills/streamlit-to-marimo提交6454470

许可证：无许可证

内容归原作者所有。SourceWeft 从公开仓库中收录这些内容。

举报或申请下架

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`

Streamlit To Marimo

Converting Streamlit Apps to Marimo

Steps

Widget Mapping Reference

Input Widgets

Display Elements

Charts

Layout

Key Conceptual Differences

Execution Model

State Management

Caching

Multi-Page Apps

Deploying

Custom components

来源与署名

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Add Molab Badge

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