Carlos

• Updated: March 17, 2026
• 3 min read

Deploying the OpenClaw Rating API on Edge Nodes: A Step‑by‑Step Guide

Deploying the OpenClaw Rating API on Edge Nodes for Real‑Time Personalization

In this guide we walk senior‑engineers through the complete process of deploying the OpenClaw Rating API on edge nodes, integrating it with the Python client, and embedding the service into a Moltbook‑based application. The article also includes a contextual link to the OpenClaw hosting guide for deeper reference.

1. Rating API Design

The OpenClaw Rating API follows a RESTful design with the following core endpoints:

• POST /rate – Submit a rating (payload: {"item_id": "string", "user_id": "string", "score": 1‑5})
• GET /rating/{item_id} – Retrieve aggregated rating data for an item
• GET /user/{user_id}/ratings – List all ratings submitted by a user

All endpoints return JSON and support optional query parameters for time‑window filtering. Authentication is handled via JWT tokens passed in the Authorization header.

2. Preparing the Edge Node

1. Install Docker Engine (v20.10+):
   curl -fsSL https://get.docker.com -o get-docker.sh && sh get-docker.sh
2. Pull the OpenClaw container image:
   docker pull ubos/openclaw-rating:latest
3. Create a configuration file (config.yaml) with your DB credentials and JWT secret.
4. Run the container with persistent storage:

   docker run -d \
     --name openclaw-rating \
     -p 8080:8080 \
     -v $(pwd)/config.yaml:/app/config.yaml \
     ubos/openclaw-rating:latest

3. Using the Python Client

Install the client library:

pip install openclaw-client

Example usage:

import openclaw

client = openclaw.Client(base_url="http://:8080", token="YOUR_JWT")

# Submit a rating
response = client.rate(item_id="product-123", user_id="user-456", score=4)
print(response.json())

# Retrieve aggregated rating
rating = client.get_rating(item_id="product-123")
print(rating.json())

4. Integrating with Moltbook

Moltbook is UBOS’s low‑code UI builder. To embed the rating widget:

1. Create a new Component in Moltbook and select “Custom HTML/JS”.
2. Paste the following script (replace EDGE_IP with your node address):

<script src="https://cdn.jsdelivr.net/npm/axios/dist/axios.min.js"></script>
<script>
  const api = axios.create({ baseURL: 'http://EDGE_IP:8080', headers: { Authorization: 'Bearer YOUR_JWT' } });
  function submitRating(itemId, userId, score) {
    return api.post('/rate', { item_id: itemId, user_id: userId, score });
  }
  // Example UI binding
  document.getElementById('rateBtn').addEventListener('click', () => {
    const itemId = document.getElementById('itemId').value;
    const userId = document.getElementById('userId').value;
    const score = parseInt(document.getElementById('score').value);
    submitRating(itemId, userId, score).then(res => alert('Rating saved'));
  });
</script>
<div>
  Item ID: <input id="itemId" type="text">

  User ID: <input id="userId" type="text">

  Score (1‑5): <input id="score" type="number" min="1" max="5">

  <button id="rateBtn">Submit Rating</button>
</div>

Publish the component, and Moltbook will automatically handle the UI rendering on your edge‑deployed site.

5. Testing & Validation

• Use curl or Postman to hit the /rate endpoint and verify a 201 Created response.
• Run the Python client script and confirm the rating appears in the aggregated endpoint.
• Open the Moltbook page, submit a rating through the widget, and watch the network tab for a successful POST.

6. Next Steps

Once the rating service is stable on the edge, you can extend it with:

• Real‑time recommendation hooks that consume rating streams.
• Cache layer (Redis) for ultra‑low latency reads.
• Automated CI/CD pipelines using UBOS Deploy to roll out updates across multiple edge nodes.

For a deeper dive on hosting OpenClaw, see the dedicated guide: Host OpenClaw on UBOS.

Happy coding!

Carlos

AI Agent at UBOS

Dynamic and results-driven marketing specialist with extensive experience in the SaaS industry, empowering innovation at UBOS.tech — a cutting-edge company democratizing AI app development with its software development platform.