EdGameClaw — AI Game-Based Learning Studio

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EdGameClaw — AI Game-Based Learning Studio

Turn any learning material into playable mini-games — in minutes.

What is this? A small self-hosted app on your computer: paste notes or a textbook chapter, and AI turns them into playable browser games (pixel art, scoring, different mechanics per topic). No account. No vendor lock-in. Your courses stay as files on your machine.

🚀 Installation & quick start {#quick-start}

Prerequisites

Requirement	Notes
Python 3.10+	Check with `python --version` or `python3 --version` (either name is fine — see below).
Node.js 18+	Must be on your `PATH` (`node --version`). Used for the bundled game engine helper.
API key	Any OpenAI-compatible provider; OpenRouter is a practical default.
Conda (optional)	If you prefer Conda over `venv`, see Conda below — same steps afterward.

python vs python3: You can use either; they are just different command names on disk. What matters is that the interpreter is Python 3.10+. On Windows and inside Conda, python is usually correct. On some macOS/Linux setups, only python3 is installed (or python still points to Python 2). If a command fails, try the other name. The examples below use python — swap in python3 when that is what your system provides.

Important: where `.env` and data live

API_KEY (and optional MODEL, API_BASE_URL) are read from a .env file in the current working directory when you start the server, or from the repo / package directory when developing from a git clone. You only need API_KEY=... in .env; the server and generator use the same names (legacy aliases like OPENROUTER_API_KEY still work).
Generated courses are stored under courses/ next to that project directory. If you use pip install without cloning, set EDGAMECLAW_HOME to a fixed folder, or always start uvicorn from the same directory so you do not “lose” courses. See Configuration.

Path A — Install from PyPI (fastest)

Use this when you only want to run the app, not edit the source.

Create and activate a virtual environment (recommended):
```
python -m venv .venv
```
- macOS / Linux: source .venv/bin/activate
- Windows (cmd): .venv\Scripts\activate.bat
- Windows (PowerShell): .venv\Scripts\Activate.ps1

Install the package

pip install -U pip
pip install edgameclaw

Choose a project folder where you will always run the server (this folder will hold .env, courses/, etc.). cd into it.
Create .env in that folder with at least:
```
API_KEY=sk-your-key-here
```
Optional: MODEL=... and API_BASE_URL=... (defaults work with OpenRouter).

Start the server (from the same folder as .env):

uvicorn edgameclaw.server:app --host 127.0.0.1 --port 8000

Open the Studio: http://127.0.0.1:8000/studio (or http://127.0.0.1:8000/ — it redirects to the app). Paste Markdown, parse, then generate.

If .env has API_KEY, you can leave the API key field empty in the Studio; otherwise paste your key there (stored in the browser only).

Conda instead of `venv` {#conda-instead-of-venv}

EdGameClaw is installed with pip (PyPI). Conda gives you an isolated Python; you still use pip inside that env to install the app.

Install Miniconda, Anaconda, or Mambaforge if you do not already have conda.
Create and activate an environment (Python 3.10–3.12 are fine):
```
conda create -n edgameclaw python=3.12 -y
conda activate edgameclaw
```
Optional: use the conda-forge channel for the Python build:
```
conda create -n edgameclaw -c conda-forge python=3.12 -y
conda activate edgameclaw
```
If you use micromamba or mamba, the same commands work with mamba create ... / mamba activate ... (often faster).
Install EdGameClaw — pick one:
- From PyPI (same as Path A step 2):
```
pip install -U pip
pip install edgameclaw
```
- From source (after git clone and cd into the repo):
```
pip install -U pip
pip install -e .
```
Continue with Path A from step 3 (project folder, .env, uvicorn) or Path B from step 3 (cp .env.example .env, then start the server).

start.sh and Conda: start.sh prefers .venv/bin/uvicorn when that file exists. If you only use Conda, either run uvicorn edgameclaw.server:app --host 127.0.0.1 --port 8000 yourself after conda activate, or run python -m uvicorn edgameclaw.server:app --host 127.0.0.1 --port 8000 so the correct interpreter is used without relying on start.sh.

Path B — Install from source (git clone)

Use this to contribute, patch prompts, or use start.sh on macOS/Linux.

Clone and enter the repo

git clone https://github.com/yh2072/edgameclaw.git
cd edgameclaw

Virtual environment + editable install

python -m venv .venv
source .venv/bin/activate          # Windows: .venv\Scripts\activate
pip install -U pip
pip install -e .

Environment file
```
cp .env.example .env
```
Edit .env and set API_KEY.
Start the server — pick one:
- macOS / Linux: bash start.sh — sets PYTHONPATH, starts the Node helper on port 3100 if needed, then runs Uvicorn. Stop with Ctrl+C.
- Any OS: same as PyPI — from the repo root:
```
uvicorn edgameclaw.server:app --host 127.0.0.1 --port 8000
```
  The Python app will try to auto-start the bundled Node engine on 3100 when that port is free. If you run Node yourself, set EDGAMECLAW_ENGINE_STATE_AUTO=0 in .env.
Open http://127.0.0.1:8000/studio.

Docker

There is no official image in this repo. If you build or use a community image, expose port 8000, pass API_KEY (and optionally EDGAMECLAW_HOME as a volume for persistence), and ensure Node is available inside the container if you disable auto-start.

Troubleshooting

Problem	What to do
`uvicorn` not found	Activate your venv or `conda activate edgameclaw`, or run `python -m uvicorn edgameclaw.server:app --host 127.0.0.1 --port 8000`.
Node / engine / port 3100	Install Node 18+ from nodejs.org. Free port 3100 or set `ENGINE_STATE_URL` / `EDGAMECLAW_ENGINE_STATE_AUTO=0` and run `node server.js` manually under `edgameclaw/node/`.
Port 8000 in use	Stop the other process or run with `--port 8001` (and open that port in the browser).
Generation says missing API key	Put `API_KEY` in `.env` in the directory from which you started Uvicorn, or paste the key in the Studio.
Courses disappeared	You probably started Uvicorn from a different cwd. Set `EDGAMECLAW_HOME` to one absolute path and always use it, or always `cd` to the same project folder before starting.

🎬 Watch a demo

demo.mp4

Paste content → AI builds a game-based course → Play in the browser.

🏆 Why EdGameClaw

Most “AI learning” tools give you slides or narrated videos. Here you get real mini-games: retries, scores, and a mechanic chosen to fit the topic — plus AI-generated pixel art and 24 themes. Everything runs on your machine with any OpenAI-compatible API you like.

	Typical AI course tools	EdGameClaw
Output	Slides, video, text	Playable browser games
Learning	Mostly passive	Active — score, fail, retry
Look	Generic templates	Pixel art per lesson
Mechanics	One quiz style	Matched to each concept
Host	Often cloud-only	Self-hosted, offline-friendly, BYOK

🎮 Case Study — One Sentence. One Game.

Each game below was generated from a single sentence or short paragraph. No manual design. No coding.

A World in a Square Inch

global vs. local debate in neuroscience

basic economics principles

Convolutional Neural Networks

Philosophy, neuroscience, economics, deep learning — EdGameClaw adapts its game mechanics to each subject automatically.

🧩 More Generated Mini-Games

📋 Generated Course Syllabus

EdGameClaw doesn't just create games — it first generates a structured course syllabus, then maps each chapter to the right game mechanic.

⚙️ Configuration {#configuration}

Minimum to get going: set API_KEY (or paste it only in the Studio UI — it stays in your browser).

Copy .env.example to .env when you run from a git clone, or create .env yourself in the folder where you start uvicorn:

Setting	Required?	What it does
`API_KEY`	Yes*	Your OpenAI-compatible API key
`MODEL`	No	Model name (default: `google/gemini-3-flash-preview`)
`API_BASE_URL`	No	API base URL (default: OpenRouter)
`PORT`	No	Web app port (default `8000`)
`BIND_HOST`	No	Listen address (default `127.0.0.1`)
`EDGAMECLAW_HOME`	No	Where to save `courses/`, `assets/`, `jobs/`, and `courses.json` (defaults: repo root when developing from git; current folder when installed from pip)
`EDGAMECLAW_ENGINE_STATE_AUTO`	No	`1` (default) = auto-start bundled Node on port 3100 if free. `0` = you run Node yourself.
`ENGINE_STATE_URL`	No	URL of the engine helper (default `http://127.0.0.1:3100`) — must match the Node port.

*Or enter the key only in the Studio (browser-only, not sent to our servers).

Suggested provider: OpenRouter — one key, many models. Default model google/gemini-3-flash-preview is fast and cheap for testing.

How generation works

Paste your content (Markdown).
EdGameClaw reads structure (title, chapters).
AI writes a syllabus and picks a game mechanic per chapter.
AI builds each mini-game (pixel art + logic).
You play in the browser — no extra export step.

📁 Project layout (for contributors)

edgameclaw/                    # repository root
├── pyproject.toml             # Python package metadata (pip install)
├── edgameclaw/                # importable package
│   ├── server.py              # FastAPI app
│   ├── generator/             # AI course generation pipeline
│   │   ├── pipeline.py
│   │   ├── api.py
│   │   └── ...
│   ├── engine/                # Game engine (JS + HTML)
│   ├── static/                # Landing + Studio UI
│   ├── readme/                # README assets (GIFs, screenshots)
│   └── node/                  # Engine-state server (bundled; auto-started by default)
├── courses/                   # Generated courses (local storage)
├── server.py                  # optional shim: re-exports `app` for `uvicorn server:app`
├── .env.example
├── requirements.txt           # usually: editable install (`-e .`)
└── start.sh

🌍 Supported Languages

Generate courses in: English, Chinese (中文), Japanese (日本語), Spanish (Español), French (Français), Korean (한국어), Arabic (العربية), German (Deutsch)

🎨 Visual Themes

24 built-in themes:

Cute/Modern: pink-cute, ocean-dream, forest-sage, candy-pop, galaxy-purple
Chinese: china-porcelain, china-cinnabar, dunhuang, forbidden-red, china-landscape
Historical: renaissance, baroque, nordic, victorian, mediterranean
Fantasy/Genre: fairy-tale, detective, sci-fi, academy, myth

📜 License

This project is open source under the AGPL-3.0 license. See LICENSE-AGPL-3.0 for the full text. For commercial licensing, contact: yh2072@nyu.edu.

🌟 Production Use

ahafrog is the hosted, full-featured SaaS platform built on top of EdGameClaw — with user accounts, social features, leaderboards, and a managed infrastructure. Try it if you want the full experience without self-hosting.

🤝 Share Your Courses with the World

Built something great with EdGameClaw? Publish it on ahafrog.com and share it with learners everywhere. Your course will appear in the public course library — free to play for anyone.

📖 Citation {#citation}

If you use EdGameClaw in research or a project, please cite:

@software{hang2026edgameclaw,
  author    = {Hang, Yuqi},
  title     = {EdGameClaw: AI Game-Based Learning Studio},
  year      = {2026},
  url       = {https://github.com/yh2072/edgameclaw},
  note      = {Open-source AI pipeline for converting learning content into interactive mini-games}
}

👤 Author

Yuqi Hang — PhD Student @ New York University

Built EdGameClaw as an open-source foundation for AI-powered game-based learning. Research interests include AI for educational games, human-computer interaction, neuroaesthetics and educational neuroscience.

GitHub: @yh2072
Website: yuqihang.net
Project: ahafrog.com
Community: Discord — EdGameClaw

⭐ If this project is useful to you, please star it on GitHub!

简体中文说明

将任何学习材料转化为可玩的小游戏 — 只需几分钟。

这是什么？ 装在你电脑上的自托管小工具：粘贴笔记或教材片段，AI 会生成可在浏览器里玩的小游戏（像素风、计分、每个知识点不同玩法）。不用注册账号，不绑定某一家云，课程以文件形式保存在本机。

🚀 安装与快速开始 {#quick-start-zh}

环境要求

依赖	说明
Python 3.10+	用 `python --version` 或 `python3 --version` 确认版本（见下说明）
Node.js 18+	需在 `PATH` 中（`node --version`）。用于内置小游戏引擎辅助服务
API 密钥	任意 OpenAI 兼容服务商；新手推荐 OpenRouter
Conda（可选）	若用 Conda 代替 `venv`，见下文 Conda 环境；后续步骤相同。

python 和 python3： 两个都可以用，只是系统里可执行文件名不同，关键是版本为 Python 3.10+。Windows、Conda 环境里一般用 python。部分 macOS/Linux 只装了 python3，或 python 仍指向 Python 2，此时请用 python3。下文命令统一写 python，若报错请改成 python3。

务必弄清：`.env` 和数据放哪

启动服务时，程序会在当前工作目录、以及（从 git 克隆时）仓库根目录等位置查找 .env。最少配置一行 API_KEY=你的密钥 即可；网页端与后台生成流水线使用同一套变量名（仍兼容旧的 OPENROUTER_API_KEY 等别名）。
生成的课程在 courses/ 目录下，位置相对于「你固定使用的项目目录」。若只用 pip install 且经常换文件夹启动，课程会好像「不见了」——请固定在一个目录里启动，或设置环境变量 EDGAMECLAW_HOME 指向固定绝对路径。详见下文配置说明。

方式 A — 从 PyPI 安装（最快）

适合只使用、不改源码。

创建并激活虚拟环境（推荐）：
```
python -m venv .venv
```
- macOS / Linux： source .venv/bin/activate
- Windows（cmd）： .venv\Scripts\activate.bat
- Windows（PowerShell）： .venv\Scripts\Activate.ps1

安装包

pip install -U pip
pip install edgameclaw

选定一个「项目目录」，以后每次都在此目录下启动（这里会放 .env、courses/ 等）。cd 进去。
在该目录新建 .env，至少包含：
```
API_KEY=你的密钥
```
可选：MODEL、API_BASE_URL（默认已对接 OpenRouter 风格接口）。

在同一目录启动服务：

uvicorn edgameclaw.server:app --host 127.0.0.1 --port 8000

浏览器打开 http://127.0.0.1:8000/studio（根路径 http://127.0.0.1:8000/ 会跳转）。粘贴 Markdown → 解析 → 生成游戏。

若 .env 里已配置 API_KEY，Studio 里密钥可留空；否则在网页里粘贴（仅保存在本机浏览器）。

Conda 环境 {#conda-环境}

EdGameClaw 通过 pip 安装（发布在 PyPI）。Conda 负责隔离 Python 版本；在已激活的 Conda 环境里仍用 pip 安装本软件。

若尚未安装 Conda，可选用 Miniconda、Anaconda 或 Mambaforge。
创建并激活环境（Python 3.10–3.12 均可）：
```
conda create -n edgameclaw python=3.12 -y
conda activate edgameclaw
```
可选：使用 conda-forge 源安装 Python：
```
conda create -n edgameclaw -c conda-forge python=3.12 -y
conda activate edgameclaw
```
若使用 micromamba 或 mamba，将 conda create / conda activate 换成 mamba create / mamba activate 即可（通常解析更快）。
安装 EdGameClaw（二选一）：
- PyPI（与「方式 A」第 2 步相同）：
```
pip install -U pip
pip install edgameclaw
```
- 源码（git clone 并 cd 到仓库根目录后）：
```
pip install -U pip
pip install -e .
```
接下来接 方式 A 的第 3 步起（项目目录、.env、uvicorn），或 方式 B 的第 3 步起（cp .env.example .env 等）。

start.sh 与 Conda： 脚本会优先使用仓库里的 .venv/bin/uvicorn。若你只用 Conda、未建 .venv，请在 conda activate 后直接执行 uvicorn edgameclaw.server:app --host 127.0.0.1 --port 8000，或 python -m uvicorn edgameclaw.server:app --host 127.0.0.1 --port 8000，确保用到当前环境中的解释器。

方式 B — 从源码安装（git 克隆）

适合改代码、改提示词，或在 macOS/Linux 下用 start.sh。

克隆并进入仓库

git clone https://github.com/yh2072/edgameclaw.git
cd edgameclaw

虚拟环境 + 可编辑安装

python -m venv .venv
source .venv/bin/activate          # Windows：.venv\Scripts\activate
pip install -U pip
pip install -e .

复制环境变量模板并填写密钥
```
cp .env.example .env
```
编辑 .env，设置 API_KEY。
启动服务（二选一）：
- macOS / Linux： bash start.sh —— 配置 PYTHONPATH，必要时在 3100 端口启动 Node，再启动 Uvicorn；用 Ctrl+C 结束。
- 任意系统： 在仓库根目录执行：
```
uvicorn edgameclaw.server:app --host 127.0.0.1 --port 8000
```
  Python 服务会在 3100 空闲时尝试自动启动自带 Node。若你自行运行 Node，在 .env 中设置 EDGAMECLAW_ENGINE_STATE_AUTO=0。
打开 http://127.0.0.1:8000/studio。

Docker

本仓库不提供官方镜像。若使用自建或社区镜像，需映射 8000 端口，传入 API_KEY，并用卷挂载 EDGAMECLAW_HOME（或等价路径）以持久化课程；若关闭自动启动 Node，容器内需能运行 Node。

常见问题

现象	处理
`uvicorn` 找不到	先激活 venv（`source .venv/bin/activate`）或 Conda（`conda activate edgameclaw`），或执行 `python -m uvicorn edgameclaw.server:app --host 127.0.0.1 --port 8000`
Node / 3100 端口	从 nodejs.org 安装 Node 18+；释放 3100，或修改 `ENGINE_STATE_URL` / 设置 `EDGAMECLAW_ENGINE_STATE_AUTO=0` 后手动在 `edgameclaw/node/` 下执行 `node server.js`
8000 被占用	结束占用进程，或改用 `--port 8001` 并在浏览器访问对应端口
提示没有 API 密钥	在启动 Uvicorn 时的当前目录放置含 `API_KEY` 的 `.env`，或在 Studio 中粘贴密钥
课程列表空了	多半换了启动时的当前目录；请固定目录或设置 `EDGAMECLAW_HOME`

🎬 演示视频

demo.mp4

粘贴内容 → AI 生成课程 → 浏览器里开玩。

一句话看懂 EdGameClaw

多数「AI 学习」只会出幻灯片或配音视频。这里是真正能玩的迷你游戏：重试、得分、按知识点选机制，还有 AI 像素画 和 24 套主题。数据在你自己电脑上，任意 OpenAI 兼容 API 都能接。

	常见 AI 课工具	EdGameClaw
输出	幻灯片、视频、文字	浏览器里可玩的游戏
学习	偏被动	主动玩 — 得分、失败、重试
画面	模板感	每课独立像素风
机制	一种测验	按知识点换玩法
部署	常依赖云	自托管、可离线、自带密钥（BYOK）

🎮 案例展示 — 一句话，一个游戏

以下每个游戏都由一句话或一小段文字生成，无需手动设计，无需编程。

方寸乾坤

神经科学：全局 vs 局部

经济学原理

卷积神经网络

哲学、神经科学、经济学、深度学习 — EdGameClaw 会自动为每个学科匹配最合适的游戏机制。

🧩 更多生成小游戏展示

📋 自动生成的课程大纲

EdGameClaw 不只是生成游戏 — 它会先生成结构化的课程大纲，再将每个章节映射到对应的游戏机制。

⚙️ 配置说明 {#配置说明}

最少要配： API_KEY（或只在 Studio 网页里粘贴，密钥只留在浏览器）。

从 git 克隆时，把 .env.example 复制成 .env；只用 pip 时，在运行 uvicorn 的目录下自己建 .env：

变量	必填？	说明
`API_KEY`	是*	OpenAI 兼容的 API 密钥
`MODEL`	否	模型名（默认 `google/gemini-3-flash-preview`）
`API_BASE_URL`	否	API 地址（默认 OpenRouter）
`PORT`	否	网页端口（默认 `8000`）
`BIND_HOST`	否	监听地址（默认 `127.0.0.1`）
`EDGAMECLAW_HOME`	否	存放课程与数据的目录（git 开发时默认仓库根；pip 安装时默认当前目录）
`EDGAMECLAW_ENGINE_STATE_AUTO`	否	`1`（默认）= 3100 空闲时自动起内置 Node；`0` = 自己起 Node
`ENGINE_STATE_URL`	否	引擎辅助服务地址（默认 `http://127.0.0.1:3100`）

*也可仅在 Studio 里输入密钥。

推荐接入： OpenRouter — 一把密钥，多模型。默认模型适合试玩。

生成流程（五步）

粘贴 Markdown 内容
解析课程结构
AI 写大纲并匹配每章游戏机制
AI 生成各章小游戏
浏览器里直接玩

📁 项目结构（给开发者）

edgameclaw/                    # 仓库根目录
├── pyproject.toml             # Python 包元数据（pip install）
├── edgameclaw/                # 可导入的包
│   ├── server.py              # FastAPI 应用
│   ├── generator/             # AI 课程生成流水线
│   ├── engine/                # 游戏引擎（JS + HTML）
│   ├── static/                # 落地页与 Studio
│   ├── readme/                # README 配图资源
│   └── node/                  # 引擎状态服务（默认随 uvicorn 自动启动）
├── courses/                   # 生成的课程（本地存储）
├── server.py                  # 可选：供 `uvicorn server:app` 转发到包内应用
├── .env.example
├── requirements.txt           # 通常为可编辑安装（`-e .`）
└── start.sh

🌍 支持语言

支持生成以下语言的课程：英语、中文、日语、西班牙语、法语、韩语、阿拉伯语、德语

🎨 视觉主题

24 种内置主题：

可爱/现代： pink-cute、ocean-dream、forest-sage、candy-pop、galaxy-purple
中国风： china-porcelain、china-cinnabar、dunhuang、forbidden-red、china-landscape
历史风： renaissance、baroque、nordic、victorian、mediterranean
幻想/类型： fairy-tale、detective、sci-fi、academy、myth

📜 许可证

本项目基于 AGPL-3.0 协议开源。商业授权合作请联系：yh2072@nyu.edu。

🌟 生产环境使用

ahafrog.com 是基于 EdGameClaw 构建的托管 SaaS 平台，提供用户账号、社交功能、排行榜和托管基础设施。如需完整体验而无需自托管，欢迎试用。

🤝 欢迎贡献课程、与大家一起分享

用 EdGameClaw 生成了有趣的课程？欢迎来 ahafrog.com 发布并与全球学习者共享。 你的课程将出现在公共课程库中，供任何人免费游玩。无论是中学物理、大学数学、编程入门还是历史人文——每一门好课都值得被更多人看到。

📖 引用

如果你在研究或项目中使用了 EdGameClaw，请引用：

@software{hang2026edgameclaw,
  author    = {Hang, Yuqi},
  title     = {EdGameClaw: AI Game-Based Learning Studio},
  year      = {2026},
  url       = {https://github.com/yh2072/edgameclaw},
  note      = {Open-source AI pipeline for converting learning content into interactive mini-games}
}

👤 作者

Hang Yuqi（杭雨琪） — 纽约大学博士生

EdGameClaw 是 AI 驱动游戏化学习的开源基础框架。
研究方向包括教育游戏 AI、人机交互、神经美学与教育神经科学。

GitHub: @yh2072
个人网站：yuqihang.net
官网：ahafrog.com
社区：Discord — EdGameClaw

微信交流群： 使用微信扫描下方二维码加入「edgameclaw 交流」群。微信群二维码会定期失效；若无法扫码加入，请开 GitHub Issue 或通过上方联系方式告知，我们会更新图片。

⭐ 若对你有帮助欢迎点个 Star！

Star History

Interactive chart (opens in new tab): Star History · 可交互图表（新标签打开）：Star History

Name		Name	Last commit message	Last commit date
Latest commit History 37 Commits
.github/workflows		.github/workflows
assets/audio		assets/audio
edgameclaw		edgameclaw
.env.example		.env.example
.gitignore		.gitignore
COMMERCIAL.md		COMMERCIAL.md
LICENSE		LICENSE
LICENSE-AGPL-3.0		LICENSE-AGPL-3.0
MANIFEST.in		MANIFEST.in
Procfile		Procfile
README.md		README.md
README_CN.md		README_CN.md
README_PYPI.md		README_PYPI.md
REPO_AND_PYPI.md		REPO_AND_PYPI.md
courses.json		courses.json
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
server.py		server.py
start.sh		start.sh

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EdGameClaw — AI Game-Based Learning Studio

🚀 Installation & quick start {#quick-start}

Prerequisites

Important: where .env and data live

Path A — Install from PyPI (fastest)

Conda instead of venv {#conda-instead-of-venv}

Path B — Install from source (git clone)

Docker

Troubleshooting

🎬 Watch a demo

🏆 Why EdGameClaw

🎮 Case Study — One Sentence. One Game.

🧩 More Generated Mini-Games

📋 Generated Course Syllabus

⚙️ Configuration {#configuration}

How generation works

📁 Project layout (for contributors)

🌍 Supported Languages

🎨 Visual Themes

📜 License

🌟 Production Use

🤝 Share Your Courses with the World

📖 Citation {#citation}

👤 Author

简体中文说明

🚀 安装与快速开始 {#quick-start-zh}

环境要求

务必弄清：.env 和数据放哪

方式 A — 从 PyPI 安装（最快）

Conda 环境 {#conda-环境}

方式 B — 从源码安装（git 克隆）

Docker

常见问题

🎬 演示视频

一句话看懂 EdGameClaw

🎮 案例展示 — 一句话，一个游戏

🧩 更多生成小游戏展示

📋 自动生成的课程大纲

⚙️ 配置说明 {#配置说明}

生成流程（五步）

📁 项目结构（给开发者）

🌍 支持语言

🎨 视觉主题

📜 许可证

🌟 生产环境使用

🤝 欢迎贡献课程、与大家一起分享

📖 引用

👤 作者

Star History

About

Topics

Resources

License

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Stars

Watchers

Forks

Releases

Packages 0

Uh oh!

Contributors 1

Languages

Important: where `.env` and data live

Conda instead of `venv` {#conda-instead-of-venv}

务必弄清：`.env` 和数据放哪

Packages