Building a Real‑Time Grafana Dashboard for OpenClaw Plugin Ratings

Introduction

Monitoring the health and popularity of OpenClaw plugins is essential for developers and DevOps engineers who want to react quickly to user feedback. By turning raw rating data into a live Grafana dashboard, you gain instant visibility into trends, spikes, and potential issues. This guide walks you through the entire pipeline—from pulling data from the OpenClaw API, persisting it in InfluxDB, to crafting a polished Grafana dashboard with alerts.

The steps described here are fully compatible with the host OpenClaw on UBOS platform, allowing you to spin up the required services with a single click.

Prerequisites

• OpenClaw instance – a running OpenClaw server with API access enabled.
• Grafana setup – Grafana 9+ installed locally or on a container.
• Time‑series database – InfluxDB 2.x is recommended for its native Grafana integration.

If you are already exploring UBOS capabilities, you might also be interested in the Enterprise AI platform by UBOS, which provides managed InfluxDB clusters and Grafana as a service.

Fetching rating data from the OpenClaw API

API endpoint details

OpenClaw exposes a REST endpoint that returns plugin rating statistics in JSON format:

GET https://your-openclaw.com/api/v1/plugins/{plugin_id}/ratings

The response includes average_rating, rating_count, and a timestamp.

Authentication

The API uses token‑based authentication. Generate a token in the OpenClaw admin UI and pass it as a Bearer header:

Authorization: Bearer YOUR_API_TOKEN

Sample script (Python)

Below is a minimal Python script that pulls the latest rating for a list of plugins and prints the result. The script can be scheduled with cron or run as a lightweight service.

import os
import requests
import time
from datetime import datetime

API_BASE = "https://your-openclaw.com/api/v1/plugins"
TOKEN = os.getenv("OPENCLAW_TOKEN")
PLUGIN_IDS = ["plugin-abc", "plugin-xyz"]  # replace with your IDs

def fetch_rating(plugin_id):
    url = f"{API_BASE}/{plugin_id}/ratings"
    headers = {"Authorization": f"Bearer {TOKEN}"}
    resp = requests.get(url, headers=headers)
    resp.raise_for_status()
    data = resp.json()
    return {
        "plugin_id": plugin_id,
        "average": data["average_rating"],
        "count": data["rating_count"],
        "ts": datetime.utcnow().isoformat()
    }

if __name__ == "__main__":
    for pid in PLUGIN_IDS:
        rating = fetch_rating(pid)
        print(rating)
        # Here you would push the rating to InfluxDB (see next section)

For Node.js fans, the same logic can be expressed with axios and dotenv. The key takeaway is a clean separation between data retrieval and persistence.

Storing data in a time‑series database

Database schema design

InfluxDB stores data as measurements with tags (indexed) and fields (non‑indexed). For plugin ratings, a simple schema works well:

Writing data points

The Python influxdb-client library makes writes straightforward:

from influxdb_client import InfluxDBClient, Point, WritePrecision

INFLUX_URL = "http://localhost:8086"
INFLUX_TOKEN = os.getenv("INFLUX_TOKEN")
ORG = "my-org"
BUCKET = "openclaw"

client = InfluxDBClient(url=INFLUX_URL, token=INFLUX_TOKEN, org=ORG)
write_api = client.write_api(write_options=WritePrecision.NS)

def write_rating(rating):
    point = (
        Point("plugin_ratings")
        .tag("plugin_id", rating["plugin_id"])
        .field("average", float(rating["average"]))
        .field("count", int(rating["count"]))
        .time(rating["ts"])
    )
    write_api.write(bucket=BUCKET, record=point)

# Example usage inside the fetch loop
if __name__ == "__main__":
    for pid in PLUGIN_IDS:
        rating = fetch_rating(pid)
        write_rating(rating)

Schedule the script to run every minute (or as needed) to keep the time‑series data fresh. InfluxDB’s retention policies let you automatically purge data older than a configurable period, keeping storage costs low.

Setting up Grafana dashboard

Adding the data source

1. Log in to Grafana and navigate to Configuration → Data Sources.
2. Click Add data source and select InfluxDB.
3. Enter the URL (e.g., http://localhost:8086), organization, and token.
4. Set the default bucket to openclaw and click Save & Test.

Creating panels for rating trends

A typical dashboard contains two panels per plugin:

• Average rating over time – line chart showing the average field.
• Rating count – bar chart visualizing the count field.

Example query for the average rating of plugin-abc:

from(bucket:"openclaw")
  |> range(start: -30d)
  |> filter(fn: (r) => r._measurement == "plugin_ratings")
  |> filter(fn: (r) => r.plugin_id == "plugin-abc")
  |> filter(fn: (r) => r._field == "average")
  |> aggregateWindow(every: 1h, fn: mean)
  |> yield(name: "mean")

Alerting on rating thresholds

Grafana’s built‑in alerting can notify you when a plugin’s average rating drops below a critical value (e.g., 3.0). Create an alert rule on the line chart:

1. Open the panel’s Alert tab.
2. Click Create Alert Rule.
3. Set the condition: WHEN avg() OF query(A, 5m, now) IS BELOW 3.0.
4. Configure notification channels (Slack, email, or the Workflow automation studio for custom webhook handling).

Publishing the article on UBOS

SEO considerations

To maximize discoverability, we followed GEO best practices: a concise opening answer, short paragraphs, and clear headings. The primary keyword Grafana dashboard appears in the title, URL slug, and first paragraph. Secondary keywords such as OpenClaw, plugin ratings, and real‑time monitoring are naturally woven into sub‑headings and body copy.

Internal linking boosts topical authority. For example, readers interested in broader AI integrations can explore the AI marketing agents page, while those looking for a quick start can check out the UBOS templates for quick start.

Internal linking examples used in this guide

• Telegram integration on UBOS
• ChatGPT and Telegram integration
• OpenAI ChatGPT integration
• Chroma DB integration
• ElevenLabs AI voice integration
• About UBOS
• UBOS partner program
• Enterprise AI platform by UBOS

The single required link to the OpenClaw hosting page is already placed at the top of the article, ensuring readers can quickly provision the environment.

Conclusion and next steps

You now have a fully automated pipeline that captures OpenClaw plugin ratings in real time, stores them efficiently in InfluxDB, and visualizes the data with a responsive Grafana dashboard. This setup empowers you to detect rating drops instantly, trigger automated remediation via the Web app editor on UBOS, and share insights with stakeholders.

Future enhancements could include:

• Enriching rating data with sentiment analysis using the AI Chatbot template.
• Generating weekly PDF reports via the AI Email Marketing service.
• Adding geographic breakdowns with the AI YouTube Comment Analysis tool for cross‑platform feedback.

Happy monitoring, and may your plugins always enjoy five‑star ratings!