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.
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.
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:
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
narratebeats, per-step notes in walkthroughs - Interactive TUI — wizards for recording and running (
autodemo record --interactive) - CI-grade —
--jsonoutput, stable exit codes,trace.zipon failure, artifacts contract inrun.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) ffmpegfor 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>
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。
