autodemo

autodemo

MCP server that turns any running web app into demo videos, interactive walkthroughs, and marketing captures via one command. Enables AI agents to show their work with regenerated demos on every PR.

Category
Visit Server

README

<div align="center">

AutoDemo

Demos as code. Turn any running web app into demo videos, interactive walkthroughs, and marketing captures — in one command. Regenerated by CI, so they can never go stale.

CI npm License: MIT MCP

Product page · Quick start · GitHub Action · For AI agents · Recipes · Contributing

</div>


autodemo demo "Sign up and open the dashboard" --url http://localhost:3000

That single command drives a real browser through your real app and produces:

Artifact What it's for
video.mp4 launch posts, landing pages, PR descriptions — cursor, click rings, human-paced typing
index.html static, embeddable interactive walkthrough (step screenshots + notes, keyboard navigable)
assets/*.png named marketing captures of real UI regions, for homepages and decks
run.json reproducible metadata — timings, steps, artifact contract for tooling

The teaser below was generated by AutoDemo, about its own product page, in CI:

AutoDemo teaser

Why

Demos and screenshots rot the moment the UI changes. Re-recording them is manual, slow, and always last on the list. Interactive-demo SaaS tools fix freshness with $300–500/month plans, per-seat pricing, and a browser extension that captures snapshots into their cloud.

AutoDemo treats demos like the rest of your software:

  • Versioned — scenarios are YAML in your repo, reviewed like code
  • Reproducible — deterministic Playwright steps, or AI steps when you want speed
  • Continuous — a 5-line GitHub Action regenerates everything on merge; a failing demo is a failing user flow
  • Yours — runs on your machine and your CI; no cloud, no telemetry, MIT licensed

Quick start

One-line install (macOS / Linux / WSL — installs Bun, the CLI, and a browser):

curl -fsSL https://raw.githubusercontent.com/praveen-palanisamy/autodemo/main/install.sh | bash

Or zero-install / per-project:

bunx @praveen-palanisamy/autodemo --help          # Bun
npm add -D @praveen-palanisamy/autodemo           # or as a dev dependency
bunx playwright install chromium

1. The magic moment (AI-authored)

With any LLM key in your env (ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY, GROQ_API_KEY — or a local OLLAMA_HOST, auto-detected):

autodemo demo "Sign up, create a project, open the dashboard" --url http://localhost:3000

Watch the browser do it, then open the printed video.mp4 and walkthrough. Add --save to keep the scenario for replay.

2. Deterministic demos (no LLM needed)

autodemo init   # writes a commented .autodemo.yml
scenarios:
  signup:
    description: "Signup with readable typing and click highlights"
    steps:
      - type: goto
        url: /signup
      - type: fill
        selector: "[data-testid=email]"
        value: "maya@example.com"
        typing: true
      - type: click
        selector: "[data-testid=submit]"
      - type: waitFor
        text: "Dashboard"
      - type: screenshot
        name: dashboard-hero
        selector: "[data-testid=dashboard]"
autodemo run signup --url http://localhost:3000          # one scenario
autodemo run --all --url http://localhost:3000 --headless # all of them (CI)

3. Keep them fresh forever (CI)

- uses: praveen-palanisamy/autodemo@v0
  with:
    url: http://localhost:3000

Full inputs and recipes: docs/GITHUB_ACTION.md.

For AI agents

AutoDemo is agent-native: coding agents use it to show their work — a demo video on every PR.

bunx @praveen-palanisamy/autodemo mcp --no-tui    # MCP server over stdio

One-line registration for Cursor / Claude Code / Codex, JSON CLI contracts, and a drop-in rules snippet: docs/AGENTS.md.

Features

  • Any LLM, or none — OpenAI, Anthropic, Google, Groq, Ollama/local, any OpenAI-compatible endpoint; deterministic scenarios need zero keys
  • Authenticated flows — reusable browser storage state; login once, demo logged-in forever
  • Marketing-grade output — dev overlays hidden, loading noise trimmed (videoStartStep), named region captures, custom cursor & click highlights
  • Story tools — on-screen narrate beats, per-step notes in walkthroughs
  • Interactive TUI — wizards for recording and running (autodemo record --interactive)
  • CI-grade--json output, stable exit codes, trace.zip on failure, artifacts contract in run.json

How it compares

AutoDemo Demo SaaS (Supademo, Arcade, Storylane…) Screen recorders
Price Free, OSS $300–500/mo for HTML capture, per-seat $10–30/mo
Freshness Regenerated in CI Manual re-capture Manual re-record
Output Video + walkthrough + stills, one run Usually one format Video only
In git / code review
Agent-operable (MCP)

The SaaS tools are great for no-code editing, analytics, and hosted demo hubs. AutoDemo is for teams who want demos to be build artifacts.

Docs

CLI reference commands, flags, exit codes
Configuration .autodemo.yml, step types, LLM providers, auth state
GitHub Action demos in CI
AI agents MCP setup, JSON CLI, rules snippet
Recipes copy-paste scenarios
Architecture engines, runner, artifact pipeline
Testing · Local CI development workflows

Requirements

  • Bun ≥ 1.3 (the installer sets it up)
  • Playwright Chromium (one-time bunx playwright install chromium)
  • ffmpeg for MP4 export (optional — everything else works without it)

Contributing

The 15-minute setup, project tour, and good first issue list live in CONTRIBUTING.md. The lowest-friction contribution is a scenario recipe.

bun install && bun run playwright:install
bun test && bun run lint && bun run typecheck

License

MIT © Praveen Palanisamy and AutoDemo contributors.


<div align="center"> <sub>This README's teaser, the <a href="https://praveen-palanisamy.github.io/autodemo/">product page</a>, and its demo videos are all generated by AutoDemo itself — on every deploy.</sub> </div>

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured