create-ai-chatbot 0.1.0

Scaffold a full-stack AI chatbot project with FastAPI + React

create-ai-chatbot

Scaffold a full-stack AI chatbot project in seconds. Stop rebuilding the same chatbot architecture — start customizing.

# Via PyPI
pip install create-ai-chatbot
create-ai-chatbot my-chatbot

# Via npm
npx create-ai-chatbot my-chatbot

What you get

A production-ready chatbot with:

• Backend: FastAPI with streaming SSE endpoint (/chat)
• Frontend: Vite + React with a floating chat widget
• LLM: OpenRouter (supports 100+ models, free tier available)
• Deploy: Ready for Render (backend) + Vercel (frontend)

Interactive setup

$ create-ai-chatbot
==================================================
  create-ai-chatbot
  Full-stack AI chatbot scaffolding tool
==================================================

project_name [my-ai-chatbot]:
project_slug [my-ai-chatbot]:
description [AI Chatbot powered by OpenRouter]:
backend [fastapi]:
frontend [vite-react]:
llm_provider [openrouter]:
theme [dark-minimal]:

Quick start after scaffolding

cd my-chatbot

# Terminal 1 — Backend
cd backend
cp .env.example .env
# Edit .env: add your OPENROUTER_API_KEY (free at https://openrouter.ai/keys)
pip install -r requirements.txt
python -m uvicorn main:app --reload

# Terminal 2 — Frontend
cd frontend
npm install
npm run dev

Open http://localhost:3000 — click the chat button in the bottom-right.

Deploy

Backend → Render

1. Push to GitHub
2. Create new Web Service on Render
3. Build: cd backend && pip install -r requirements.txt
4. Start: cd backend && python -m uvicorn main:app --host 0.0.0.0 --port $PORT
5. Add env var: OPENROUTER_API_KEY

Frontend → Vercel

1. Import repo on Vercel
2. Set root directory to frontend
3. Add env var: VITE_API_URL = your Render backend URL

Customization

Change the system prompt

Edit backend/data/system_prompt.py:

SYSTEM_PROMPT = """You are a real estate assistant for Acme Properties.
Help users find apartments, schedule viewings, and answer questions."""

Change the model

Edit backend/.env:

OPENROUTER_MODEL=anthropic/claude-3.5-sonnet

Or any model from https://openrouter.ai/models.

Add tools

Edit backend/main.py to add function-calling tools. The streaming SSE format supports tool calls via OpenRouter's OpenAI-compatible API.

Change the theme

Edit frontend/tailwind.config.js to customize colors, fonts, and styling.

Project structure

my-chatbot/
├── backend/
│   ├── main.py              # FastAPI app with /chat and /health
│   ├── config.py            # Settings via pydantic-settings
│   ├── schemas.py           # Pydantic request/response models
│   ├── services/
│   │   └── llm.py           # LLM service with streaming
│   ├── data/
│   │   └── system_prompt.py # Your system prompt
│   ├── requirements.txt
│   ├── .env.example
│   └── render.yaml          # Render deployment config
├── frontend/
│   ├── src/
│   │   ├── App.tsx
│   │   ├── main.tsx
│   │   ├── index.css
│   │   └── components/
│   │       └── Chat.tsx     # Floating chat widget
│   ├── index.html
│   ├── package.json
│   ├── vite.config.ts
│   ├── tailwind.config.js
│   ├── tsconfig.json
│   └── tsconfig.node.json
├── .gitignore
└── README.md

Supported configurations

Option	Choices
Backend	fastapi
Frontend	vite-react
LLM Provider	openrouter (default), openai
Theme	dark-minimal (default)

Why this exists

I built the same chatbot architecture three times — for a real estate company, a hackathon, and a client project. Every time: FastAPI backend, React frontend, OpenRouter for LLM, streaming SSE, floating chat widget. The only thing that changed was the system prompt and the tools.

So I packaged it. Now you can go from zero to a deployed AI chatbot in under 5 minutes.

Contributing

PRs welcome! Areas we'd love help with:

• Next.js App Router template option
• Anthropic Claude direct integration
• Pre-built tool templates (RAG, web search, database)
• Additional themes

License

MIT