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Streamlit custom components

lib/streamlit/.agents/skills/developing-with-streamlit/references/third-party-components.md

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Streamlit custom components

Extend Streamlit with third-party custom components from the community.

What are custom components?

Custom components are standalone Python libraries that add features not in Streamlit's core API. They're built by the community and can be installed like any Python package.

Installation

Install using the PyPI package name (not the repo name—they can differ):

bash
uv add <pypi-package-name>

Then import according to the component's documentation. The import name often differs from the package name too.

Use with caution

Components are not maintained by Streamlit. Before adopting:

  • Check maintenance - Is it actively maintained? Recent commits?
  • Check compatibility - Does it work with your Streamlit version?
  • Check popularity - GitHub stars, downloads, community usage
  • Consider alternatives - Can you achieve this with core Streamlit?

Custom components can break when Streamlit updates, so prefer core features when possible.

streamlit-keyup

Text input that fires on every keystroke instead of waiting for enter/blur. Useful for live search.

bash
uv add streamlit-keyup
python
from st_keyup import st_keyup

query = st_keyup("Search", debounce=300)  # 300ms debounce
filtered = df[df["name"].str.contains(query, case=False)]
st.dataframe(filtered)

streamlit-bokeh

Official replacement for st.bokeh_chart (removed from Streamlit API). Maintained by Streamlit.

bash
uv add streamlit-bokeh
python
from bokeh.plotting import figure
from streamlit_bokeh import streamlit_bokeh

p = figure(title="Simple Line", x_axis_label="x", y_axis_label="y")
p.line([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], line_width=2)
streamlit_bokeh(p)

streamlit-aggrid

Interactive dataframes with sorting, filtering, cell editing, grouping, and pivoting. Use when you need customization beyond what st.dataframe and st.data_editor offer.

bash
uv add streamlit-aggrid
python
from st_aggrid import AgGrid

AgGrid(df, editable=True, filter=True)

When to use aggrid over st.dataframe:

  • Interactive row grouping and pivoting
  • Advanced filtering and sorting UI
  • Complex cell editing workflows
  • Custom cell renderers

streamlit-folium

Interactive maps powered by Folium.

bash
uv add streamlit-folium
python
import folium
from streamlit_folium import st_folium

m = folium.Map(location=[37.7749, -122.4194], zoom_start=12)
st_folium(m, width=700)

streamlit-pivot-table

Interactive pivot tables with drag-and-drop dimensions, aggregation, and drill-down. Maintained by Streamlit.

bash
uv add streamlit-pivot
python
import pandas as pd
from streamlit_pivot import st_pivot_table

df = pd.read_csv("sales.csv")
result = st_pivot_table(
    df,
    key="my_pivot",
    rows=["Region"],
    columns=["Year"],
    values=["Revenue"],
    aggregation={"Revenue": "sum"},
    show_totals=True,
)

streamlit-extras

A collection of community utilities. Cherry-pick what you need.

bash
uv add streamlit-extras
python
from streamlit_extras.pagination import pagination

page = pagination(num_pages=10, default=1, key="my_pages")
st.write(f"Current page: {page}")
python
from annotated_text import annotated_text

annotated_text(
    "This ",
    ("is", "verb", "#8ef"),
    " some ",
    ("annotated", "adj", "#faa"),
    ("text", "noun", "#afa"),
)

Discover more

Browse the custom component gallery: https://streamlit.io/components

Filter by category, popularity, and recency to find custom components for your use case.

References