OpenUI Forge — Python

Build generative UI apps with a React frontend + Python FastAPI backend. Streams OpenAI-compatible NDJSON.

Activation Triggers

• "openui python", "openui fastapi", "openui flask"
• "generative ui python", "python streaming ui backend"

Prerequisites

• Node.js >= 22 (24 LTS recommended) + React >= 18.3.1 (19+ recommended) (frontend)
• Python >= 3.10 (backend)
• OPENAI_API_KEY or ANTHROPIC_API_KEY set

Quick Start

1. Create the React frontend and install OpenUI deps:

npm install @openuidev/react-ui @openuidev/react-headless @openuidev/react-lang lucide-react zod

2. Generate the system prompt from your component library:

npx @openuidev/cli generate ./src/lib/library.ts --out backend/system-prompt.txt

3. Set up the Python backend (see Full Code below)
4. Run both: frontend on :3000, backend on :8000

Full Code

Backend: backend/requirements.txt

fastapi>=0.115.0
uvicorn>=0.24.0
openai>=2.0
anthropic>=0.111.0
python-dotenv>=1.0.0

The Python >= 3.10 floor comes from fastapi/uvicorn/python-dotenv; openai and anthropic themselves need only Python 3.9.

Backend (OpenAI): backend/main.py

import os
from pathlib import Path
from dotenv import load_dotenv
from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse
from openai import AsyncOpenAI

load_dotenv()
app = FastAPI()
app.add_middleware(
    CORSMiddleware,
    allow_origins=["http://localhost:3000"],
    allow_methods=["POST"],
    allow_headers=["*"],
)

# AsyncOpenAI keeps the request from blocking the event loop during streaming.
client = AsyncOpenAI()
SYSTEM_PROMPT = Path("system-prompt.txt").read_text()

@app.post("/api/chat")
async def chat(request: Request):
    body = await request.json()
    messages = [{"role": "system", "content": SYSTEM_PROMPT}] + body["messages"]

    async def generate():
        response = await client.chat.completions.create(
            model=os.getenv("OPENAI_MODEL", "gpt-5.5"),
            stream=True,
            messages=messages,
        )
        async for chunk in response:
            data = chunk.model_dump_json()
            yield f"data: {data}\n\n"
        yield "data: [DONE]\n\n"

    return StreamingResponse(generate(), media_type="text/event-stream")

Backend (Anthropic variant): backend/main_anthropic.py

import os, json, time
from pathlib import Path
from dotenv import load_dotenv
from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse
from anthropic import AsyncAnthropic

load_dotenv()
app = FastAPI()
app.add_middleware(
    CORSMiddleware,
    allow_origins=["http://localhost:3000"],
    allow_methods=["POST"],
    allow_headers=["*"],
)

# AsyncAnthropic mirrors AsyncOpenAI so the stream does not block the loop.
client = AsyncAnthropic()
SYSTEM_PROMPT = Path("system-prompt.txt").read_text()

@app.post("/api/chat")
async def chat(request: Request):
    body = await request.json()
    stream_id = f"chatcmpl-{int(time.time())}"

    async def generate():
        async with client.messages.stream(
            model=os.getenv("ANTHROPIC_MODEL", "claude-sonnet-4-6"),
            max_tokens=4096,
            system=SYSTEM_PROMPT,
            messages=body["messages"],
        ) as stream:
            async for text in stream.text_stream:
                chunk = {"id": stream_id, "object": "chat.completion.chunk",
                         "choices": [{"index": 0, "delta": {"content": text}, "finish_reason": None}]}
                yield f"data: {json.dumps(chunk)}\n\n"
        done = {"id": stream_id, "object": "chat.completion.chunk",
                "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}]}
        yield f"data: {json.dumps(done)}\n\n"
        yield "data: [DONE]\n\n"

    return StreamingResponse(generate(), media_type="text/event-stream")

Frontend: app/chat/page.tsx (or src/Chat.tsx for Vite)

"use client";
import { FullScreen } from "@openuidev/react-ui";
import { openuiChatLibrary } from "@openuidev/react-ui/genui-lib";
import {
  openAIAdapter,
  openAIMessageFormat,
} from "@openuidev/react-headless";

export default function ChatPage() {
  return (
    <FullScreen
      componentLibrary={openuiChatLibrary}
      streamProtocol={openAIAdapter()}
      messageFormat={openAIMessageFormat}
      apiUrl="http://localhost:8000/api/chat"
    />
  );
}

The Python backend emits SSE (data: {json}\n\n). Pair it with openAIAdapter() on the frontend. openAIReadableStreamAdapter() is for NDJSON (no data: prefix) and will silently produce no output here.

System Prompt Generation

Generate once, copy to backend directory:

npx @openuidev/cli generate ./src/lib/library.ts --out backend/system-prompt.txt

Regenerate after every component change.

Validation Checklist

• system-prompt.txt exists in the backend directory
• CORS allows the frontend origin
• Backend streams data: {json}\n\n lines with OpenAI chunk format
• Final chunk has finish_reason: "stop" followed by data: [DONE]
• Frontend apiUrl points to the correct backend URL
• Frontend uses streamProtocol={openAIAdapter()} and openAIMessageFormat
• componentLibrary={openuiChatLibrary} prop passed to FullScreen
• CSS import in root layout (@openuidev/react-ui/components.css)
• Run backend: uvicorn main:app --reload --port 8000

Error Patterns

Error	Cause	Fix
CORS blocked	Frontend origin not allowed	Add origin to allow_origins list
Connection refused	Backend not running	Start with uvicorn main:app --port 8000
FileNotFoundError	system-prompt.txt missing	Run the CLI generate command
Stream not rendering	Backend not sending SSE format	Ensure data: prefix and \n\n after each chunk
422 Unprocessable Entity	Request body missing messages	Check frontend sends { messages: [...] }