Carlos

• Updated: March 18, 2026
• 6 min read

Building a Grafana Dashboard for OpenClaw Rating and Moltbook Engagement

You can build a single Grafana dashboard that correlates OpenClaw Rating API metrics with Moltbook engagement data (likes, comments, feed views) by configuring two HTTP data sources, transforming the JSON payloads, and visualizing the time‑series side‑by‑side in Grafana panels.

1. Introduction

Monitoring content performance across platforms is a common challenge for developers, data engineers, and DevOps teams. OpenClaw provides a robust Rating API that scores content quality, while Moltbook exposes engagement endpoints for likes, comments, and feed views. By unifying these signals in a Grafana dashboard you gain real‑time insight into how rating scores drive user interaction.

This guide walks you through the entire process—from provisioning a Grafana instance to publishing a polished dashboard—using OpenClaw hosting on UBOS as the underlying infrastructure.

2. Prerequisites

• A running UBOS platform overview instance with Docker support.
• Grafana 8+ installed (self‑hosted or via UBOS Enterprise AI platform by UBOS).
• API credentials for the OpenAI ChatGPT integration (used here for token generation).
• Access tokens for OpenClaw Rating API (see Telegram integration on UBOS for example token handling).
• API keys for Moltbook engagement endpoints (provided by your Moltbook admin console).
• Basic familiarity with JSONPath, PromQL (optional), and Grafana panel editing.

3. Setting up OpenClaw Rating API data source

3.1 Authentication

OpenClaw uses Bearer token authentication. Store the token securely in Grafana’s Secrets store or as an environment variable.

# Example: export OPENCLAW_TOKEN=your_token_here
export OPENCLAW_TOKEN=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...

In Grafana, create a new HTTP data source and set the Authorization header to Bearer ${OPENCLAW_TOKEN}. This ensures the token is injected on every request.

3.2 Query examples

The Rating API endpoint returns a JSON array of objects, each containing content_id, rating_score, and timestamp. Use the following query to fetch the last 24 hours of data:

GET https://api.openclaw.io/v1/ratings?since=24h

Grafana’s built‑in JSON parser can extract the fields using JSONPath expressions:

• $.rating_score → rating value
• $.timestamp → Unix epoch (ms)
• $.content_id → identifier for correlation

4. Fetching Moltbook engagement data

4.1 API endpoints

Moltbook exposes three REST endpoints:

1. GET /v2/engagement/likes?content_id={id}
2. GET /v2/engagement/comments?content_id={id}
3. GET /v2/engagement/views?content_id={id}

All endpoints require an Authorization: Bearer <MOLTBOOK_TOKEN> header.

4.2 Data transformation

Because each endpoint returns a single numeric value, we need to reshape the data into a time‑series format compatible with Grafana. A lightweight Node.js script (or a Workflow automation studio flow) can poll the three endpoints every minute, merge the results, and push them to a Prometheus Pushgateway.

const axios = require('axios');
const pushgateway = 'http://localhost:9091/metrics/job/moltbook';

async function fetchMetrics(contentId) {
  const headers = { Authorization: `Bearer ${process.env.MOLTBOOK_TOKEN}` };
  const [likes, comments, views] = await Promise.all([
    axios.get(`https://api.moltbook.io/v2/engagement/likes?content_id=${contentId}`, { headers }),
    axios.get(`https://api.moltbook.io/v2/engagement/comments?content_id=${contentId}`, { headers }),
    axios.get(`https://api.moltbook.io/v2/engagement/views?content_id=${contentId}`, { headers })
  ]);
  return {
    likes: likes.data.count,
    comments: comments.data.count,
    views: views.data.count,
    timestamp: Date.now()
  };
}

// Push to Prometheus
async function pushMetrics(metrics) {
  const payload = `
moltbook_likes{content_id="${contentId}"} ${metrics.likes} ${metrics.timestamp}
moltbook_comments{content_id="${contentId}"} ${metrics.comments} ${metrics.timestamp}
moltbook_views{content_id="${contentId}"} ${metrics.views} ${metrics.timestamp}
`;
  await axios.post(pushgateway, payload);
}

// Example usage
const contentId = 'abc123';
setInterval(async () => {
  const data = await fetchMetrics(contentId);
  await pushMetrics(data);
}, 60_000); // every minute

5. Configuring Grafana data sources

Grafana now needs two data sources:

1. HTTP API datasource for OpenClaw (already created in step 3).
2. Prometheus datasource pointing at the Pushgateway (or a full Prometheus server if you prefer).

To add Prometheus:

• Navigate to Configuration → Data Sources → Add data source.
• Select Prometheus and set the URL to http://localhost:9090 (or your remote endpoint).
• Save & test – you should see the moltbook_* metrics appear in the query explorer.

6. Building the dashboard

6.1 Creating the base dashboard

From the Grafana home page, click + → Dashboard → New dashboard. Choose a Time series** panel for each metric.

6.2 Panel for OpenClaw rating

Configure the query:

SELECT
  $__timeGroup(timestamp, '1m') as time,
  avg(rating_score) as value
FROM
  openclaw_ratings
WHERE
  $__timeFilter(timestamp)

Set the visualization to Line, enable Legend with {{content_id}}, and apply a Y‑axis range of 0‑100.

6.3 Panels for Moltbook engagement

Each engagement metric uses a Prometheus query. Example for likes:

sum by (content_id) (rate(moltbook_likes[1m]))

Repeat the same for moltbook_comments and moltbook_views. Use Bar gauge for comments and Stat for views to diversify the visual language.

6.4 Using variables for dynamic content selection

Define a dashboard variable $content_id that pulls distinct IDs from the OpenClaw data source:

query_result(openclaw_ratings, "SELECT DISTINCT content_id FROM openclaw_ratings")

Reference the variable in each panel’s query (e.g., WHERE content_id = '$content_id'). This lets users switch between articles without rebuilding the dashboard.

6.5 Correlating metrics with time series

To visualize correlation, add a Scatter plot** (via the UBOS templates for quick start “Scatter Plot” plugin). Use the rating as the X‑axis and likes as the Y‑axis, both grouped by the same timestamp. Enable a trend line to instantly see whether higher ratings drive more likes.

7. Adding the internal link and branding

Every dashboard page should reinforce the UBOS brand. In the dashboard description, embed a call‑to‑action linking to the UBOS partner program. This not only boosts SEO but also guides readers toward deeper engagement with the platform.

8. Final review and publishing steps

1. Validate each panel’s query with the Query inspector to ensure no hidden errors.
2. Enable Auto‑refresh at a 1‑minute interval for near‑real‑time monitoring.
3. Export the dashboard JSON (via Dashboard settings → JSON model) and store it in your Web app editor on UBOS repository for version control.
4. Publish the dashboard to the UBOS marketplace using the UBOS portfolio examples as a reference layout.
5. Document the setup steps in your internal wiki and link back to this guide for future onboarding.

9. Conclusion and next steps

By following this developer‑focused guide you now have a live Grafana dashboard that:

• Shows OpenClaw rating trends alongside Moltbook likes, comments, and views.
• Allows instant content‑ID switching via dashboard variables.
• Provides a visual correlation (scatter plot) to assess rating impact on engagement.

Next, consider extending the pipeline:

• Integrate Chroma DB integration for semantic search over content bodies.
• Leverage ElevenLabs AI voice integration to generate audio alerts when rating drops below a threshold.
• Deploy the dashboard as a AI marketing agent that automatically recommends promotion strategies based on engagement spikes.

For a deeper dive into UBOS’s low‑code automation capabilities, explore the UBOS for startups page.

References

For additional context, see the original news article that announced the OpenClaw‑Moltbook partnership.

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.