Dash Improve My LLMs

Full documentation: https://llms.2plot.dev — the dedicated dash-improve-my-llms documentation site, with the complete API reference and deeper examples. This page is the quick-start overview.

Installation

Visit GitHub Repo · PyPI

pip install "dash-improve-my-llms[flask]"     # Dash's default backend
pip install "dash-improve-my-llms[fastapi]"   # Dash 4.2+
pip install "dash-improve-my-llms[quart]"     # Dash 4.2+ async

Requires dash>=4.1. The backend is detected from app.server; your code is the same either way.

Introduction

A Dash app is a JavaScript application. Ask for any page without running JavaScript — which is what most crawlers, link previewers and LLM fetchers do — and every URL returns the same empty Loading... shell. To a search engine, a 30-page documentation site looks like 30 identical thin pages. To an agent, it looks like nothing at all.

dash-improve-my-llms fixes that at the framework level. You write each page's content once, as Markdown, and the package serves it everywhere a non-JavaScript consumer will look: rendered into the page's own initial HTML before React mounts, as /{page}/llms.txt, in the site-wide /llms.txt index, in sitemap.xml and robots.txt, and as an MCP resource on Dash 4.3+. One source of truth, six surfaces, and the interactive app is untouched.

This very site runs it — the /llms.txt index, the Markdown twin behind every page, and the cross-host network directory that links the *.2plot.dev satellites together are all this package at work.

Full Documentation

The complete documentation lives on its own site: llms.2plot.dev — every route, the access-control tiers, network configuration, the MCP bridge, and the machine-readable twin at llms.2plot.dev/llms.txt. This page is a summary; go there for the full reference.

Quick Start

import dash
from dash import Dash, html
from dash_improve_my_llms import add_llms_routes, RobotsConfig

app = Dash(__name__, use_pages=True)

# Absolute URLs in sitemap.xml, llms.txt and canonical tags come from this.
app._base_url = "https://myapp.com"
app._robots_config = RobotsConfig(block_ai_training=True)

app.layout = html.Div([dash.page_container])

add_llms_routes(app)   # ← the whole integration

Then give each page prose — a module-level LLMS_DOC string, or register_page_metadata(path, name=..., description=..., llms_doc=...) when pages are generated in a loop.

What You Get

Features

• Universal prerender — crawler-ready static HTML for every page, with the interactive app untouched.
• llms.txt everywhere — a Markdown twin of every page, plus a rendered viewer for humans who open it in a browser.
• Multi-backend — Flask, FastAPI and Quart, auto-detected from app.server.
• Per-request access control — visibility verdicts (allow/gated/deny) applied consistently across llms.txt, crawler HTML, prerender, sitemap and MCP.
• Cross-host network directory — publish sibling sites so an agent landing on one host can find the rest; this is how the 2plot network hangs together.
• Bot management — block AI-training crawlers while allowing AI-search citations, configurable per bot class.