orion-ui 0.13.0

Python authoring API for Orion-native notebook UI outputs

orion-ui

Python library for building interactive notebook UI in Orion. Author controls in a code cell with import orion_ui as ui; Orion renders them as native notebook outputs.

This package is separate from orion-notebook (the orion CLI). Install orion-ui into the same Python environment as your notebook kernel.

Install

pip install orion-ui

Orion managed runtime

If you start Orion with orion and use its managed Jupyter environment (~/.orion/runtime/venv on macOS/Linux, %USERPROFILE%\.orion\runtime\venv on Windows), orion-ui is installed automatically. You do not need a separate install.

External kernel (conda, venv, your own Jupyter)

Install into that kernel's Python:

python -m pip install orion-ui

Then restart the notebook kernel and re-run your cells.

Quick example

Put a component tree as the last expression in a code cell:

import orion_ui as ui

ui.card(
    ui.stack(
        ui.select("model", ["gpt-4.1", "claude-sonnet"], label="Model", default_value="gpt-4.1"),
        ui.slider("temperature", label="Temperature", min=0, max=2, default_value=0.7, step=0.1),
    ),
    title="Controls",
    class_name="controls-card",
)

Read values in later cells:

model = ui.get("model")
temperature = ui.get("temperature")

DataFrame tables

Use ui.table() for interactive pandas DataFrame browsing without sending the entire DataFrame to the frontend:

import orion_ui as ui

ui.table(
    df,
    source="df",
    page_size=50,
    column_descriptions={
        "score": "Priority score from 1 to 10.",
        "status": "Current account status.",
    },
)

Table filtering, sorting, grouping, stats, and export requests run in the Python kernel. Saved table views are stored on the notebook output metadata as structured operations plus a readable pandas expression. Column descriptions, when provided, appear as info-icon tooltips in table headers.

class_name adds semantic CSS hooks for Orion UI in Notebook View and App View. Do not write CSS into notebook metadata; if a notebook needs custom styling, include it in the relevant cell source/output and scope selectors to rendered markdown/output areas. Orion also exposes JupyterLab-compatible rendered-content selectors such as .jp-MarkdownOutput, .jp-RenderedHTMLCommon, and .jp-OutputArea-output for cell-authored styles. Do not rely on arbitrary Tailwind classes generated at runtime.

Requirements

• Python 3.8+
• Orion (or another frontend that renders application/vnd.orion.ui+json) for interactive display

Other Jupyter frontends may show a static fallback instead of live controls.

Version coupling

Pin orion-ui to the same version as your Orion app when using managed runtimes (for example orion-ui==0.6.0). The Python output format and Orion's renderer are released together.

Links

• Orion website
• User docs
• Fix: orion_ui import error
• orion-notebook on PyPI (CLI launcher)

License

Apache-2.0