- Updated: March 20, 2026
- 3 min read
Integrating Moltbook with OpenClaw Rating API Edge: A Complete End‑to‑End Tutorial
# Integrating Moltbook with OpenClaw Rating API Edge: A Complete End‑to‑End Tutorial
*Published by the UBOS Team*
—
## Introduction
The AI‑agent ecosystem is exploding, and developers need robust, real‑time personalization pipelines to stay competitive. In this tutorial we walk through an end‑to‑end integration of **Moltbook** with the **OpenClaw Rating API Edge**, apply per‑agent token‑bucket limits for real‑time personalization, and visualize those limits on a Grafana dashboard. The workflow showcases the power of the unified OpenClaw ecosystem and ties directly into the current AI‑agent hype.
—
## Prerequisites
– A running UBOS instance with WordPress installed.
– Access to the OpenClaw Rating API Edge (API key and endpoint).
– Moltbook installed on your development machine.
– Grafana server reachable from your network.
– Basic knowledge of Docker and Node‑RED.
—
## Step 1: Set Up Moltbook
1. Clone the Moltbook repository:
bash
git clone https://github.com/openclaw/moltbook.git
cd moltbook
2. Install dependencies:
bash
npm install
3. Configure the `config.json` with your OpenClaw credentials:
{
“openclaw”: {
“apiKey”: “YOUR_OPENCLAW_API_KEY”,
“endpoint”: “https://api.openclaw.tech/rating”
}
}
—
## Step 2: Connect to the OpenClaw Rating API Edge
Create a Node‑RED flow that forwards user events from Moltbook to the Rating API Edge.
[ {
“id”: “moltbook_input”,
“type”: “http in”,
“method”: “post”,
“url”: “/event”
}, {
“id”: “call_rating_api”,
“type”: “http request”,
“method”: “POST”,
“url”: “{{openclaw.endpoint}}”,
“headers”: { “Authorization”: “Bearer {{openclaw.apiKey}}” }
}, {
“id”: “response”,
“type”: “http response”
} ]
Deploy the flow and test it with a sample payload.
—
## Step 3: Apply Per‑Agent Token‑Bucket Limits
Token‑bucket limits ensure each AI agent receives a fair share of rating requests.
javascript
// tokenBucket.js
class TokenBucket {
constructor(capacity, refillRate) {
this.capacity = capacity;
this.tokens = capacity;
this.refillRate = refillRate; // tokens per second
setInterval(() => this.refill(), 1000);
}
refill() {
this.tokens = Math.min(this.capacity, this.tokens + this.refillRate);
}
tryConsume() {
if (this.tokens > 0) {
this.tokens–;
return true;
}
return false;
}
}
module.exports = TokenBucket;
Instantiate a bucket per agent in your Node‑RED function node and gate the request to the Rating API Edge.
—
## Step 4: Export Metrics to Prometheus
Grafana reads metrics from Prometheus. Expose bucket state via an HTTP endpoint.
javascript
// metrics.js
const express = require(‘express’);
const app = express();
app.get(‘/metrics’, (req, res) => {
const metrics = Object.entries(agentBuckets).map(([agent, bucket]) =>
`agent_tokens{agent=”${agent}”} ${bucket.tokens}`
).join(‘\n’);
res.set(‘Content-Type’, ‘text/plain’);
res.send(metrics);
});
app.listen(9091);
Add the endpoint to your Prometheus scrape config and create a Grafana dashboard showing `agent_tokens`.
—
## Step 5: Visualize Limits in Grafana
1. Create a new **Dashboard** → **Add Panel**.
2. Use the query:
promql
agent_tokens
3. Choose a **Gauge** visualization to see remaining tokens per agent.
4. Save the dashboard and share the link with your team.
—
## Step 6: Tie It All Together – The AI‑Agent Hype
By combining Moltbook’s event stream, OpenClaw’s real‑time rating, per‑agent throttling, and Grafana visualizations, developers can build AI‑agent services that:
– Scale predictably under load.
– Provide transparent usage metrics.
– Align with the latest AI‑agent trends where personalization and rate‑control are critical.
—
## Conclusion
You now have a complete, production‑ready pipeline that showcases the power of the **OpenClaw ecosystem**. Deploy it, monitor it, and iterate on your AI‑agent features.
For more details on hosting OpenClaw on UBOS, visit our internal guide: https://ubos.tech/host-openclaw/
—
*Happy coding!*