Openui forge langchain

OpenUI Forge — LangChain

Build generative UI apps with OpenUI + LangChain. Stream from ChatOpenAI or ChatAnthropic, convert to OpenAI NDJSON.

Activation Triggers

• "openui langchain", "openui langgraph", "openui langsmith"
• "generative ui langchain", "langchain streaming ui"

Prerequisites

• Node.js >= 22 (24 LTS recommended), React >= 18.3.1 (19+ recommended)
• OPENAI_API_KEY or ANTHROPIC_API_KEY set
• Next.js project (App Router recommended)

Quick Start

1. Install dependencies (pick one or both LLM providers):

npm install @openuidev/react-ui @openuidev/react-headless @openuidev/react-lang lucide-react zod @langchain/openai @langchain/core
# For Anthropic: npm install @langchain/anthropic

2. Add the CSS import to app/layout.tsx:

import "@openuidev/react-ui/components.css";

3. Create the API route and frontend page below
4. Run npm run dev and test

Full Code

Backend (OpenAI): app/api/chat/route.ts

import { openuiChatLibrary } from "@openuidev/react-ui/genui-lib";
import { ChatOpenAI } from "@langchain/openai";
import { HumanMessage, SystemMessage, AIMessage } from "@langchain/core/messages";

const model = new ChatOpenAI({ model: process.env.OPENAI_MODEL ?? "gpt-5.5", streaming: true });

export async function POST(req: Request) {
  const { messages } = await req.json();

  const systemPrompt = openuiChatLibrary.prompt({
    preamble: "You are a helpful assistant that generates interactive UIs.",
  });

  const lcMessages = [
    new SystemMessage(systemPrompt),
    ...messages.map((m: { role: string; content: string }) =>
      m.role === "user" ? new HumanMessage(m.content) : new AIMessage(m.content)
    ),
  ];

  const stream = await model.stream(lcMessages);
  const encoder = new TextEncoder();
  const id = `chatcmpl-${Date.now()}`;

  const readableStream = new ReadableStream({
    async start(controller) {
      for await (const chunk of stream) {
        const text = typeof chunk.content === "string" ? chunk.content : "";
        if (!text) continue;
        const payload = {
          id,
          object: "chat.completion.chunk",
          choices: [{ index: 0, delta: { content: text }, finish_reason: null }],
        };
        controller.enqueue(encoder.encode(`data: ${JSON.stringify(payload)}\n\n`));
      }
      const done = {
        id,
        object: "chat.completion.chunk",
        choices: [{ index: 0, delta: {}, finish_reason: "stop" }],
      };
      controller.enqueue(encoder.encode(`data: ${JSON.stringify(done)}\n\n`));
      controller.enqueue(encoder.encode("data: [DONE]\n\n"));
      controller.close();
    },
  });

  return new Response(readableStream, {
    headers: { "Content-Type": "text/event-stream" },
  });
}

Backend (Anthropic variant): app/api/chat/route.ts

Replace the model initialization and import:

import { ChatAnthropic } from "@langchain/anthropic";

const model = new ChatAnthropic({
  model: process.env.ANTHROPIC_MODEL ?? "claude-sonnet-4-6",
  maxTokens: 4096,
  streaming: true,
});

Everything else (message mapping, stream conversion, response) stays identical.

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="/api/chat"
    />
  );
}

The backend emits SSE (data: {json}\n\n). Pair it with openAIAdapter() on the frontend. (langGraphAdapter is also exported from @openuidev/react-headless if you stream LangGraph events natively rather than converting to OpenAI shape.)

Component Creation

import { defineComponent } from "@openuidev/react-lang";
import { z } from "zod";

export const MetricCard = defineComponent({
  name: "MetricCard",
  description: "Displays a metric with label, value, and optional trend",
  props: z.object({
    label: z.string().describe("Metric name"),
    value: z.number().describe("Current metric value"),
    trend: z.enum(["up", "down", "flat"]).optional().describe("Trend direction"),
  }),
  component: ({ props }) => (
    <div style={{ padding: 16, border: "1px solid #e5e7eb", borderRadius: 8 }}>
      <div style={{ fontSize: 14, color: "#6b7280" }}>{props.label}</div>
      <div style={{ fontSize: 24, fontWeight: 700 }}>{props.value}</div>
      {props.trend && <span>{props.trend === "up" ? "+" : props.trend === "down" ? "-" : "="}</span>}
    </div>
  ),
});

System Prompt Generation

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

Validation Checklist

• LLM provider API key is set
• @langchain/openai or @langchain/anthropic installed
• Messages correctly mapped to LangChain message types
• Stream chunks converted to OpenAI-compatible SSE with data: prefix
• Final chunk has finish_reason: "stop" and ends with data: [DONE]
• Frontend uses streamProtocol={openAIAdapter()} and openAIMessageFormat
• CSS import in root layout

Error Patterns

Error	Cause	Fix
Empty chunks in stream	LangChain AIMessageChunk content may be empty	Skip chunks where text is empty
Type error on messages	Wrong LangChain message class	Map user to HumanMessage, assistant to AIMessage
Module not found	Missing LangChain provider package	Install @langchain/openai or @langchain/anthropic
Stream hangs	Missing [DONE] sentinel	Always send final stop chunk and [DONE]
CORS error	Cross-origin frontend	Add CORS headers if frontend/backend are split