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openui-forge-langchain

OpenUI generative UI with LangChain/LangGraph backend. Supports ChatOpenAI and ChatAnthropic.

Quelle prüfen Auf GitHub ansehen

Preis unbestätigt★ 22 GitHub-StarsVerzeichnis aktualisiert · 13. Sept. 2026agent-skill

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OpenUI generative UI with LangChain/LangGraph backend. Supports ChatOpenAI and ChatAnthropic.

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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

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

Add the CSS import to app/layout.tsx:

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

Create the API route and frontend page below
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

Quelle ansehen ↗

Dateimetadaten

name: openui-forge-langchain
description: OpenUI generative UI with LangChain/LangGraph backend. Supports ChatOpenAI and ChatAnthropic.
version: 1.2.0
author: OthmanAdi

Originaltext anzeigen

---
name: openui-forge-langchain
description: OpenUI generative UI with LangChain/LangGraph backend. Supports ChatOpenAI and ChatAnthropic.
version: 1.2.0
author: OthmanAdi
---

# 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):
```bash
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`:
```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`

```typescript
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:

```typescript
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`

```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

```tsx
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

```bash
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 |

Quelle prüfen

Preis und Betriebskosten

Skill beziehen: Preis unbestätigt
Ausführen: Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
Lizenz: MIT
Preis unbestätigt: Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.

Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →

Skill-Quelle erfasst

Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.

Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
KI-Prüffreigabe fehlt
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 22 GitHub stars
Stars/forks activity: 22 stars, 0 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Vollständiges Audit öffnen

Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

ErfasstStatisch geprüft

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository: OthmanAdi/openui-forge
Lizenz: MIT
Version: 1.2.0
Letzter GitHub-Push: 3. Aug. 2026
Verzeichnis aktualisiert: 13. Sept. 2026
Anleitungspfad: .agents/skills/openui-forge-langchain/SKILL.md @ 977180d4d3e9

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

49/100

Prüfung nötig

Vertrauen

56/100

Do not auto-install

Audit

67/100

Prüfung nötig

Vollständiges Audit öffnen Eval-Bericht ansehen

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
KI-Prüffreigabe fehlt
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 22 GitHub stars
Stars/forks activity: 22 stars, 0 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Verified installs: —
Ergebnisse: —

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

Agent-Zugang

Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.

Installations-API öffnen LLM-Textformat Manifest öffnen Auto-Auflösungsplan

Weitere Details

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    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 67,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Low GitHub adoption signal",
      "AI review approval is missing",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 22 GitHub stars",
      "Stars/forks activity: 22 stars, 0 forks; issue activity unavailable in current metadata"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 49,
    "label": "Needs review"
  },
  "supply": {
    "track": "Design and creative production",
    "scenario": "Design and creative",
    "maintenance": "2mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "AI review approval is missing",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use openui-forge-langchain in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 64/100 Manual review",
      "Audit: 67/100 Needs review",
      "Safety: 31/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "othmanadi-openui-forge-langchain (openui-forge-langchain)",
      "install_command": "npx skills add OthmanAdi/openui-forge --skill openui-forge-langchain",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "othmanadi-openui-forge-langchain",
      "task": "Use openui-forge-langchain in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/othmanadi-openui-forge-langchain",
    "api": "https://www.openagentskill.com/api/agent/skills/othmanadi-openui-forge-langchain",
    "audit": "https://www.openagentskill.com/skills/othmanadi-openui-forge-langchain/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=othmanadi-openui-forge-langchain&task=Use%20openui-forge-langchain%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20openui-forge-langchain%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20openui-forge-langchain%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/othmanadi-openui-forge-langchain/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/othmanadi-openui-forge-langchain"
  }
}

Für Ersteller

Quelle des Eintrags

Registry-indexiert

Beanspruchbar

Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: OthmanAdi
Quelle: OthmanAdi/openui-forge
Indexiert von: OpenAgentSkill Community-Index

Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.

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