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openui-forge-langchain
OpenUI generative UI with LangChain/LangGraph backend. Supports ChatOpenAI and ChatAnthropic.
Resumen
OpenUI generative UI with LangChain/LangGraph backend. Supports ChatOpenAI and ChatAnthropic.
Leer documentación completa
Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.
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_KEYorANTHROPIC_API_KEYset- 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 devand 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 withopenAIAdapter()on the frontend. (langGraphAdapteris also exported from@openuidev/react-headlessif 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/openaior@langchain/anthropicinstalled - 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 withdata: [DONE] - Frontend uses
streamProtocol={openAIAdapter()}andopenAIMessageFormat - 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 |
Metadatos del archivo
name: openui-forge-langchain description: OpenUI generative UI with LangChain/LangGraph backend. Supports ChatOpenAI and ChatAnthropic. version: 1.2.0 author: OthmanAdi
Ver texto original
---
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 |
Revisar el código fuente
Precio y costes de ejecución
- Obtener el skill
- Precio sin confirmar
- Ejecutarlo
- Requisitos sin confirmar. Consulta los costes del agente, API y servicios en la fuente.
- Licencia
- MIT
- Precio sin confirmar
- No hemos confirmado el precio. Los enlaces existentes al código y a la instalación siguen disponibles.
Obtener gratis no significa ejecutar gratis. El precio no es una evaluación de seguridad. Enviar información de precio →
Fuente del skill registrada
La ruta de instrucciones está registrada. No implica pruebas de ejecución, seguridad ni compatibilidad.
Revisar antes de instalar: Evitar instalación automática
Licencia: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Low GitHub adoption signal
- Falta aprobación de revisión por IA
- 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
Las herramientas son indicios de metadatos, no compatibilidad probada. Los prompts son sugerencias.
Empieza con una tarea pequeña
- 1Lee la fuente y confirma entradas, resultados, dependencias y permisos.
- 2Pide un plan al agente. Aprueba la configuración y los costes antes de probar en un entorno aislado.
- 3Comprueba resultados y archivos modificados. Informa solo de lo ejecutado y conserva la revisión de la fuente.
Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.
Fuente y notas de uso
Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.
- Repositorio fuente
- OthmanAdi/openui-forge
- Licencia
- MIT
- Versión
- 1.2.0
- Último push de GitHub
- 3 ago 2026
- Registro actualizado
- 13 sept 2026
- Ruta de instrucciones
- .agents/skills/openui-forge-langchain/SKILL.md @ 977180d4d3e9
Versión declarada en el registro; consulta las versiones de la fuente.
Calidad
49/100
Requiere revisión
Confianza
56/100
Do not auto-install
Auditoría
67/100
Requiere revisión
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Low GitHub adoption signal
- Falta aprobación de revisión por IA
- 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
- —
- Resultados
- —
Copiar no es instalar. Los recuentos requieren un informe de instalación correcta, no garantizan calidad general.
Acceso para agentes
La API Registry expone señales de decisión, confianza, auditoría, casos de uso e instalación sin raspar la interfaz.
Más detalles
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]
},
"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"
}
}Para el creador
Fuente de la ficha
Indexado por Registry
Esta ficha se indexó desde fuentes públicas y no está marcada como oficial hasta que se apruebe una reclamación de mantenedor.
- Creador
- OthmanAdi
- Fuente
- OthmanAdi/openui-forge
- Indexado por
- Índice comunitario de OpenAgentSkill
La atribución enlaza al repositorio público o al perfil del creador. Los creadores pueden reclamar la ficha para actualizar las señales de propiedad.
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[](https://www.openagentskill.com/skills/othmanadi-openui-forge-langchain/audit)
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