Skip to the rows

agentsclimarketplace

Openui forge java

Skill OthmanAdi/openui-forge/.codex/skills/openui-forge-java

Cross-IDE, multi-stack agent skill for OpenUI (the Open Standard for Generative UI). Adds OpenUI to existing projects across 12 backend stacks, any LLM provider, and 11 agent platforms. Scaffold, integrate, validate.

Install

npx -y skills add OthmanAdi/openui-forge --skill openui-forge-java

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

20 stars20 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

OpenUI generative UI with a Java Spring Boot (WebFlux) backend. Streams the OpenAI API directly via WebClient as SSE.

SKILL.md

11.3 KB, as published. Nobody here has run it

OpenUI Forge — Java

Build generative UI apps with a React frontend + Java Spring Boot (WebFlux) backend. The backend forwards OpenAI's SSE stream to the browser via a reactive WebClient and a Flux<String> controller, pairing with openAIAdapter() on the frontend.

Activation Triggers

"openui java", "openui spring", "openui spring boot backend"
"generative ui java", "java streaming ui backend", "webflux openui"

Prerequisites

Node.js >= 22 (24 LTS recommended) + React >= 18.3.1 (19+ recommended) (frontend)
Java 21 LTS recommended (Spring Boot 4.x requires Java 17+; it builds against Java up to 26)
Maven 3.9+ (or Gradle 8+)
Spring Boot 4.0.x (current stable line; 4.1.x is the latest). Boot 3.5.x also works but reached end of OSS support on 2026-06-30.
OPENAI_API_KEY environment variable set

Quick Start

Create the React frontend and install OpenUI deps:

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

Generate the system prompt into the backend's resources:

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

Create the Spring Boot backend (see Full Code below)
Run: mvn spring-boot:run on :8080, frontend on :3000

Full Code

Backend: `backend/pom.xml`

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>4.0.6</version>
        <relativePath/>
    </parent>

    <groupId>com.example</groupId>
    <artifactId>openui-backend</artifactId>
    <version>0.0.1-SNAPSHOT</version>

    <properties>
        <java.version>21</java.version>
    </properties>

    <dependencies>
        <!-- WebFlux pulls in the reactive web stack, WebClient, and Reactor Netty -->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-webflux</artifactId>
        </dependency>
    </dependencies>

    <build>
        <plugins>
            <plugin>
                <groupId>org.springframework.boot</groupId>
                <artifactId>spring-boot-maven-plugin</artifactId>
            </plugin>
        </plugins>
    </build>
</project>

Backend: `backend/src/main/resources/application.properties`

# Server port (override with SERVER_PORT env var). Frontend expects 8080.
server.port=${SERVER_PORT:8080}

Backend: `backend/src/main/java/com/example/openui/Application.java`

package com.example.openui;

import java.io.IOException;
import java.io.UncheckedIOException;
import java.nio.charset.StandardCharsets;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;

import org.springframework.beans.factory.annotation.Value;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.context.annotation.Bean;
import org.springframework.core.io.Resource;
import org.springframework.http.HttpHeaders;
import org.springframework.http.MediaType;
import org.springframework.web.bind.annotation.PostMapping;
import org.springframework.web.bind.annotation.RequestBody;
import org.springframework.web.bind.annotation.RestController;
import org.springframework.web.reactive.config.CorsRegistry;
import org.springframework.web.reactive.config.WebFluxConfigurer;
import org.springframework.web.reactive.function.client.WebClient;

import reactor.core.publisher.Flux;

@SpringBootApplication
public class Application {

    public static void main(String[] args) {
        SpringApplication.run(Application.class, args);
    }

    // CORS — lock to the configured frontend origin. Never use "*" here: the
    // endpoint is browser-callable and a wildcard lets any site burn your key.
    @Bean
    public WebFluxConfigurer corsConfigurer(
            @Value("${FRONTEND_ORIGIN:http://localhost:3000}") String frontendOrigin) {
        return new WebFluxConfigurer() {
            @Override
            public void addCorsMappings(CorsRegistry registry) {
                registry.addMapping("/api/**")
                        .allowedOrigins(frontendOrigin)
                        .allowedMethods("POST", "OPTIONS")
                        .allowedHeaders("Content-Type")
                        .allowCredentials(true);
            }
        };
    }

    // Shared non-blocking WebClient (Reactor Netty). Honors OPENAI_BASE_URL so
    // OpenAI-compatible providers (Azure, Groq, Together, Ollama, ...) work.
    @Bean
    public WebClient openAiWebClient(
            WebClient.Builder builder,
            @Value("${OPENAI_BASE_URL:https://api.openai.com/v1}") String baseUrl) {
        return builder.baseUrl(baseUrl).build();
    }
}

record ChatMessage(String role, String content) {}

record ChatRequest(List<ChatMessage> messages) {}

@RestController
class ChatController {

    private final WebClient openAiWebClient;
    private final String apiKey;
    private final String model;
    private final String systemPrompt;

    ChatController(
            WebClient openAiWebClient,
            @Value("${OPENAI_API_KEY:}") String apiKey,
            @Value("${OPENAI_MODEL:gpt-5.5}") String model,
            @Value("classpath:system-prompt.txt") Resource systemPromptResource) {
        this.openAiWebClient = openAiWebClient;
        this.apiKey = apiKey;
        this.model = model;
        try {
            // Loaded ONCE at startup from src/main/resources/system-prompt.txt.
            this.systemPrompt = systemPromptResource.getContentAsString(StandardCharsets.UTF_8);
        } catch (IOException e) {
            throw new UncheckedIOException(
                    "Failed to read system-prompt.txt from classpath. Generate it with: "
                    + "npx @openuidev/cli generate ./src/lib/library.ts "
                    + "--out src/main/resources/system-prompt.txt", e);
        }
    }

    // produces=text/event-stream + Flux<String>: Spring's SSE writer wraps each
    // emitted String as "data: <element>\n\n". We emit BARE JSON payloads (upstream
    // "data:" prefix already stripped by the SSE reader) so the wire output is
    // exactly "data: {chunk}\n\n" ... "data: [DONE]\n\n" — what openAIAdapter() expects.
    @PostMapping(value = "/api/chat", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
    Flux<String> chat(@RequestBody ChatRequest request) {
        if (apiKey == null || apiKey.isBlank()) {
            return Flux.just("{\"error\":\"OPENAI_API_KEY not set\"}");
        }

        // Prepend the system prompt server-side; never trust a client system message.
        List<ChatMessage> messages = new ArrayList<>();
        messages.add(new ChatMessage("system", systemPrompt));
        if (request.messages() != null) {
            messages.addAll(request.messages());
        }

        Map<String, Object> upstreamBody = Map.of(
                "model", model,
                "stream", true,
                "messages", messages);

        // bodyToFlux(String.class) on a text/event-stream response makes Spring's
        // ServerSentEventHttpMessageReader parse upstream "data:" events and emit
        // each event's bare payload incrementally (buffered across TCP chunks).
        return openAiWebClient.post()
                .uri("/chat/completions")
                .header(HttpHeaders.AUTHORIZATION, "Bearer " + apiKey)
                .contentType(MediaType.APPLICATION_JSON)
                .accept(MediaType.TEXT_EVENT_STREAM)
                .bodyValue(upstreamBody)
                .retrieve()
                .bodyToFlux(String.class);
    }
}

Frontend: `app/chat/page.tsx`

"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:8080/api/chat"
    />
  );
}

The Spring Boot backend forwards OpenAI's SSE stream incrementally: WebClient.bodyToFlux(String.class) parses upstream data: events as they arrive, and the Flux<String> controller (produces=text/event-stream) re-frames each payload as data: {chunk}\n\n, ending with data: [DONE]\n\n. Reactor is fully non-blocking, so tokens flush to the client as they stream (no buffering of the whole response). Pair it with openAIAdapter() on the frontend. openAIReadableStreamAdapter() is for NDJSON (no data: prefix) and will silently produce no output here.

Because the controller emits Flux<String> with text/event-stream, Spring's ServerSentEventHttpMessageWriter adds the data: prefix for you. Do not prepend data: to the strings yourself or you will get a doubled data: data: {...} frame that the adapter cannot parse.

System Prompt Generation

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

Validation Checklist

Error Patterns

Error	Cause	Fix
CORS blocked	Origin mismatch	Set `FRONTEND_ORIGIN` to the frontend URL; check `allowedOrigins`
`FileNotFoundException` / `UncheckedIOException` on startup	`system-prompt.txt` missing from `src/main/resources/`	Run the CLI generate command into that path, then rebuild
401 from upstream	`OPENAI_API_KEY` unset or invalid	Export a valid key; verify `OPENAI_BASE_URL` matches the provider
`WebClientResponseException` 4xx/5xx	Bad model or provider error	Check `OPENAI_MODEL` and `OPENAI_BASE_URL`; inspect the exception body
Doubled `data: data:` frames	Manually prefixed strings with `data:`	Emit bare JSON; the SSE writer adds the prefix
Empty render / no output	Frontend used `openAIReadableStreamAdapter()` (NDJSON)	Use `openAIAdapter()` to match the SSE body
Response buffers then dumps at once	Returned `Mono`/collected the Flux	Return the `Flux<String>` directly so elements stream as they arrive

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.