panel-live-server¶

A local Panel web server that executes Python code snippets and renders the resulting visualizations as live, interactive web pages — so humans and AI assistants can display and inspect Python outputs in real time.

pls mcp — AI assistant integration¶

Give Claude, GitHub Copilot, or any MCP-compatible AI assistant the ability to render visualizations directly in your IDE. The validate and show tools execute Python and return a live URL — no manual setup required.

uv tool install panel-live-server[pydata]
pls mcp # configure this command in Claude, Copilot etc.

Ask your AI assistant:

Please show a quick and beautiful Matplotlib trading dashboard

Please show a basic, interactive Panel app with a slider.

Now replace the text with a hvplot and show it.

Please show the most beautiful matplotlib plot

The AI calls show to render it — the visualization appears immediately in your chat interface.

pls serve — Standalone visualization server¶

Start a local web server and create interactive visualizations through a browser UI or REST API. Every snippet gets its own permanent URL.

uv tool install panel-live-server[pydata]
pls serve # run this command in the terminal

Open http://localhost:5077/add and submit any Python visualization:

import pandas as pd
import hvplot.pandas

df = pd.DataFrame({'Product': ['A', 'B', 'C', 'D'], 'Sales': [120, 95, 180, 150]})
df.hvplot.bar(x='Product', y='Sales', title='Sales by Product')

Browse your visualizations at /feed, manage them at /admin, and link directly to any individual chart at /view?id=....

Features¶

Two execution methods¶

• Jupyter (default) — the last expression is automatically displayed, just like a notebook cell
• Panel — explicit .servable() calls for multi-component dashboards with reactive widgets

Works with any Python visualization library¶

hvplot · plotly · altair · matplotlib · seaborn · holoviews · bokeh · vega · deckgl · and more

Persistent storage¶

Every snippet is saved to a local SQLite database with full-text search. Visualizations survive server restarts and are accessible by URL at any time.

Robust subprocess management¶

The Panel server runs as a managed subprocess with health monitoring and automatic restart (up to a configurable limit). Port conflicts and stale processes are handled automatically.

Validate before you render¶

A dedicated validate tool runs four static checks — syntax, security, package availability, and Panel extension declarations — and returns a structured result before any rendering happens. show reuses the cached result automatically, so there is no double-validation overhead.

MCP App UI¶

When used with a compatible AI client, visualizations render inline with zoom controls (25 / 50 / 75 / 100 %), one-click URL and code copying, and a loading indicator.

REST API¶

import requests

response = requests.post(
    "http://localhost:5077/api/snippet",
    json={"code": "1 + 1", "name": "Addition", "method": "jupyter"}
)
print(response.json()["url"])

Works everywhere¶

Local, Jupyter, JupyterHub, VS Code Dev Containers, GitHub Codespaces — URLs are automatically externalized via Jupyter Server Proxy when needed.

Installation¶

pip install panel-live-server

With the full PyData stack (hvplot, plotly, altair, polars, etc.):

pip install "panel-live-server[pydata]"

Via conda / pixi:

pixi add panel-live-server

pip install panel-live-server==0.1.0a1

Quick start¶

=== "MCP (Claude Code)"

Add to your MCP configuration:

```json
{
  "mcpServers": {
    "panel-live-server": {
      "command": "pls",
      "args": ["mcp"]
    }
  }
}
```

The AI will automatically call `validate` first, then `show` — errors are caught before
rendering so you always get clear, actionable feedback instead of a blank panel.

Ask your AI: *"Show me a scatter plot of this data using the show tool."*

=== "Standalone"

```bash
pls serve
# Open http://localhost:5077/add in your browser
```

=== "REST API"

```python
import requests

r = requests.post(
    "http://localhost:5077/api/snippet",
    json={
        "code": "import panel as pn\npn.widgets.IntSlider(name='x', start=0, end=100)",
        "name": "Slider",
        "method": "jupyter",
    }
)
print(r.json()["url"])  # http://localhost:5077/view?id=...
```

Learn more¶