- Updated: March 19, 2026
- 7 min read
Exporting Token‑Bucket Usage Data, Merging with Moltbook Metrics, and Visualizing Results – A Step‑by‑Step Guide
You can export token‑bucket usage data from the OpenClaw Rating API, merge it with Moltbook feed metrics, and visualize the combined dataset using Node.js and Chart.js in just a few steps.
Introduction
The OpenClaw Rating API provides real‑time token‑bucket usage statistics for every request made to your rating engine. Meanwhile, the Moltbook feed delivers content‑level engagement metrics such as reads, likes, and shares. Combining these two data sources gives developers a 360° view of both system performance and user interaction.
Why does this matter? In a world where AI‑agent hype is driving rapid adoption of telemetry‑driven SaaS products, a unified dashboard can surface bottlenecks, predict scaling needs, and power AI‑enhanced recommendations.
In this guide we’ll walk through the entire pipeline – from authenticating with OpenClaw, pulling Moltbook metrics, merging the datasets, to visualizing the results with Chart.js. All code is written in JavaScript (Node.js) and can be deployed on the UBOS homepage with a single click.
Prerequisites
- Node.js ≥ 18.x installed locally.
- An active UBOS platform overview account with API access.
- OpenClaw Rating API key (available in your UBOS dashboard).
- Moltbook feed token – request it from your Moltbook admin console.
- Optional: AI marketing agents for automated report generation.
Exporting Token‑Bucket Usage Data from OpenClaw
Authentication
OpenClaw uses Bearer‑token authentication. Store your key in an environment variable to keep it safe.
// .env
OPENCLAW_API_KEY=your_openclaw_key_hereAPI Endpoint Call
The endpoint /v1/usage/token-bucket returns a JSON array with timestamps, bucket IDs, and used tokens.
// fetchTokenBucket.js
const fetch = require('node-fetch');
require('dotenv').config();
async function getTokenBucketUsage() {
const response = await fetch('https://api.openclaw.io/v1/usage/token-bucket', {
headers: {
'Authorization': `Bearer ${process.env.OPENCLAW_API_KEY}`,
'Accept': 'application/json'
}
});
if (!response.ok) {
throw new Error(`OpenClaw error: ${response.statusText}`);
}
return await response.json(); // [{timestamp, bucketId, usedTokens}, ...]
}
module.exports = { getTokenBucketUsage };Run the script locally to verify the payload:
node -e "require('./fetchTokenBucket').getTokenBucketUsage().then(console.log)"Fetching Moltbook Feed Metrics
Moltbook exposes a /feed/metrics endpoint that returns per‑article engagement data.
// fetchMoltbook.js
const fetch = require('node-fetch');
require('dotenv').config();
async function getMoltbookMetrics() {
const response = await fetch('https://api.moltbook.io/v2/feed/metrics', {
headers: {
'Authorization': `Bearer ${process.env.MOLTBOOK_TOKEN}`,
'Accept': 'application/json'
}
});
if (!response.ok) {
throw new Error(`Moltbook error: ${response.statusText}`);
}
return await response.json(); // [{articleId, reads, likes, shares}, ...]
}
module.exports = { getMoltbookMetrics };Merging the Two Datasets
Data Structures
We’ll normalize both responses into a map keyed by a common identifier – the bucketId from OpenClaw matches the articleId in Moltbook.
// mergeData.js
const { getTokenBucketUsage } = require('./fetchTokenBucket');
const { getMoltbookMetrics } = require('./fetchMoltbook');
async function mergeDatasets() {
const [tokenData, moltData] = await Promise.all([
getTokenBucketUsage(),
getMoltbookMetrics()
]);
// Create a lookup for Moltbook metrics
const moltMap = new Map();
moltData.forEach(item => moltMap.set(item.articleId, item));
// Combine
const combined = tokenData.map(tb => {
const molt = moltMap.get(tb.bucketId) || { reads: 0, likes: 0, shares: 0 };
return {
timestamp: tb.timestamp,
bucketId: tb.bucketId,
usedTokens: tb.usedTokens,
reads: molt.reads,
likes: molt.likes,
shares: molt.shares
};
});
return combined;
}
// Export for visualization step
module.exports = { mergeDatasets };The resulting array can be fed directly into a charting library.
Visualizing the Results
Choosing a Charting Library
Chart.js is lightweight, works both in the browser and with chartjs-node-canvas for server‑side PNG generation.
Rendering the Combined Data (Browser)
Create a simple HTML page that pulls the merged JSON from an endpoint you host on UBOS.
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>OpenClaw + Moltbook Dashboard</title>
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<link href="https://cdn.jsdelivr.net/npm/tailwindcss@2.2.19/dist/tailwind.min.css" rel="stylesheet">
</head>
<body class="p-8">
<h1 class="text-3xl font-bold mb-6">Token‑Bucket & Moltbook Metrics</h1>
<canvas id="metricsChart" class="bg-white rounded shadow"></canvas>
<script>
async function loadData() {
const res = await fetch('/api/combined-metrics');
const data = await res.json();
const labels = data.map(d => new Date(d.timestamp).toLocaleDateString());
const tokenSeries = data.map(d => d.usedTokens);
const readsSeries = data.map(d => d.reads);
const likesSeries = data.map(d => d.likes);
const ctx = document.getElementById('metricsChart').getContext('2d');
new Chart(ctx, {
type: 'line',
data: {
labels,
datasets: [
{ label: 'Used Tokens', data: tokenSeries, borderColor: '#3b82f6', fill: false },
{ label: 'Reads', data: readsSeries, borderColor: '#10b981', fill: false },
{ label: 'Likes', data: likesSeries, borderColor: '#f59e0b', fill: false }
]
},
options: {
responsive: true,
plugins: { legend: { position: 'top' } }
}
});
}
loadData();
</script>
</body>
</html>
Server‑Side PNG Generation (Node)
If you prefer an image that can be embedded in email reports, use chartjs-node-canvas:
// generateChart.js
const { ChartJSNodeCanvas } = require('chartjs-node-canvas');
const { mergeDatasets } = require('./mergeData');
const width = 800;
const height = 400;
const chartJSNodeCanvas = new ChartJSNodeCanvas({ width, height });
async function createChart() {
const data = await mergeDatasets();
const configuration = {
type: 'line',
data: {
labels: data.map(d => new Date(d.timestamp).toLocaleDateString()),
datasets: [
{ label: 'Used Tokens', data: data.map(d => d.usedTokens), borderColor: '#3b82f6', fill: false },
{ label: 'Reads', data: data.map(d => d.reads), borderColor: '#10b981', fill: false },
{ label: 'Likes', data: data.map(d => d.likes), borderColor: '#f59e0b', fill: false }
]
},
options: { plugins: { legend: { position: 'top' } } }
};
const image = await chartJSNodeCanvas.renderToBuffer(configuration);
const fs = require('fs');
fs.writeFileSync('combined-metrics.png', image);
console.log('Chart saved as combined-metrics.png');
}
createChart().catch(console.error);
The generated combined-metrics.png can be served via a static route on UBOS or attached to automated reports.
Embedding the Internal Link Naturally
When you publish the dashboard on your UBOS‑hosted site, you can add a call‑to‑action that points readers to the dedicated OpenClaw hosting page. For example:
“Ready to scale your rating engine without managing servers? Host OpenClaw on UBOS and let our platform handle the heavy lifting.”
Why This Guide Is Powered by the AI‑Agent Wave
Modern AI agents—like the Talk with Claude AI app—can draft code, suggest optimizations, and even generate documentation. This article itself was refined using an AI‑assisted writing workflow, ensuring the steps are concise, up‑to‑date, and free of boilerplate noise.
Leverage AI agents in your own pipelines: feed the merged JSON into an OpenAI ChatGPT integration to auto‑generate insights, or connect to ChatGPT and Telegram integration for real‑time alerts.
Illustration
Conclusion & Next Steps
We’ve demonstrated how to:
- Authenticate and pull token‑bucket data from the OpenClaw Rating API.
- Retrieve Moltbook feed metrics.
- Merge both datasets into a unified structure.
- Render an interactive Chart.js dashboard or a server‑side PNG.
Next, consider extending the pipeline:
- Schedule the merge script with the Workflow automation studio for hourly updates.
- Apply Enterprise AI platform by UBOS to run predictive models on the combined data.
- Use UBOS templates for quick start to spin up a full‑stack monitoring app in minutes.
Ready to try it yourself? Host OpenClaw on UBOS today and accelerate your telemetry stack.