- Updated: March 22, 2026
- 3 min read
Building a Specialized OpenClaw Agent: A Step‑by‑Step Guide Using the Plugin Rating API and Full‑Stack Template
# Building a Specialized OpenClaw Agent
## Introduction
In this guide we walk you through creating a **customer‑support assistant** powered by the OpenClaw framework. We’ll use the **Plugin Rating API** to rank responses and the **Full‑Stack Template** to wire up the front‑end, back‑end, and deployment pipeline.
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## Architecture Overview
+——————-+ +——————-+ +——————-+
| Front‑End UI | | OpenClaw Core | | Plugin Rating API |
+——————-+ +——————-+ +——————-+
^ ^
| |
v v
+——————-+ +——————-+
| Authentication | | Persistence DB |
+——————-+ +——————-+
* **Front‑End UI** – React/Next.js page served from UBOS.
* **OpenClaw Core** – Handles conversation flow, context management, and tool execution.
* **Plugin Rating API** – External service that scores each generated answer; the highest‑scoring answer is returned to the user.
* **Authentication** – JWT‑based guard for secure access.
* **Persistence DB** – Stores conversation history (PostgreSQL).
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## Step‑by‑Step Implementation
### 1. Scaffold the Project
bash
npx create-openclaw-agent my-support‑assistant
cd my-support‑assistant
### 2. Add the Rating Plugin
Edit `plugins/ratings.js`:
javascript
import axios from ‘axios’;
export async function rateResponse(response) {
const res = await axios.post(‘https://rating.api.ubos.tech/evaluate’, {
text: response,
context: ‘customer‑support’
});
return res.data.score; // 0‑1
}
### 3. Wire the Plugin into the Agent
javascript
import { rateResponse } from ‘./plugins/ratings’;
export async function generateAnswer(prompt, context) {
const candidates = await openClaw.generateMultiple(prompt, context);
const scored = [];
for (const cand of candidates) {
const score = await rateResponse(cand);
scored.push({ cand, score });
}
// Return the highest‑scoring candidate
scored.sort((a, b) => b.score – a.score);
return scored[0].cand;
}
### 4. Create the Front‑End Component
tsx
import { useState } from ‘react’;
import axios from ‘axios’;
export default function SupportChat() {
const [msg, setMsg] = useState(”);
const [history, setHistory] = useState([]);
const send = async () => {
const res = await axios.post(‘/api/chat’, { message: msg });
setHistory([…history, { user: msg, bot: res.data.reply }]);
setMsg(”);
};
return (
);
}
### 5. Deploy on UBOS
bash
ubos deploy . –env production
The command builds the Docker image, pushes it to the UBOS registry, and creates the WordPress‑compatible landing page.
—
## Why This Architecture?
* **Modularity** – The rating plugin can be swapped for any other scoring service without touching the core logic.
* **Scalability** – Each component runs in its own container; the rating service can be horizontally scaled.
* **Security** – JWT authentication isolates user sessions, and the persistence layer is isolated from the public web server.
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## Full‑Stack Template Benefits
The Full‑Stack Template bundles:
1. **Next.js front‑end** with server‑side rendering for SEO.
2. **Express API** that proxies OpenClaw calls and the rating service.
3. **Docker Compose** files for local development and production.
4. **CI/CD pipeline** pre‑configured for UBOS.
—
## Conclusion
You now have a fully functional, rating‑aware customer‑support assistant that can be extended to other domains (sales, HR, etc.). For more details on hosting OpenClaw agents, visit our internal guide: [/agent/copywriter](/agent/copywriter).
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## Internal Link
For the full OpenClaw hosting guide, see the page at .
—
*Happy coding!*
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.