- Updated: March 23, 2026
- 8 min read
Building a Real‑Time Performance Dashboard for OpenClaw on UBOS
**Title:** Build a Real‑Time Performance Dashboard for OpenClaw with UBOS
**Slug:** build-openclaw-real-time-dashboard-ubos
—
## Direct Answer
You can build a real‑time performance dashboard for the OpenClaw sales‑collateral agent by instrumenting the agent, exporting key metrics (open rate, click‑through rate, conversion), storing them in a time‑series database, and visualizing the data with a lightweight HTML/JS UI or Grafana—all of which can be deployed on the **UBOS platform** in minutes.
—
## Introduction
Monitoring the effectiveness of sales‑collateral agents is essential for data‑driven marketing teams. OpenClaw, a popular sales‑collateral agent, generates valuable signals such as email open rates, click‑through rates (CTR), and conversion counts. However, without a dedicated dashboard, these signals remain hidden in logs or third‑party analytics tools.
This **UBOS tutorial** walks you through the complete lifecycle of a real‑time performance dashboard:
1. **Instrumenting** the OpenClaw agent to emit metrics.
2. **Exporting** those metrics in a format suitable for time‑series storage.
3. **Storing** the data in a scalable database (Prometheus or InfluxDB).
4. **Visualizing** the metrics with a lightweight UI or Grafana.
5. **Deploying** the solution on UBOS with best‑practice CI/CD pipelines.
By the end of this guide, you’ll have a production‑ready dashboard that updates in seconds, giving developers and DevOps engineers immediate insight into campaign performance.
—
## Prerequisites
| Requirement | Why It Matters |
|————-|—————-|
| **UBOS account** – access to the UBOS homepage | Provides the runtime environment, CI/CD pipelines, and one‑click deployment. |
| **OpenClaw agent source** (Node.js or Python) | You’ll modify the code to emit metrics. |
| **Docker** (≥ 20.10) | UBOS containers run on Docker; you’ll build a custom image. |
| **Basic knowledge of Prometheus or InfluxDB** | Required for time‑series storage. |
| **Git** | For version control and UBOS pipeline integration. |
| **Node.js ≥ 14** (if using the JavaScript version) | Needed for the lightweight UI example. |
> **Tip:** If you’re new to UBOS, explore the UBOS platform overview and the Workflow automation studio to understand how pipelines are built.
—
## Instrumenting the OpenClaw Agent
### Adding Metric Collection
OpenClaw already tracks email events internally. To expose these as metrics, you’ll add a thin wrapper around the existing event handlers. Below is a **Node.js** example using the **prom-client** library:
js
// metrics.js
const client = require(‘prom-client’);
// Define counters
const openRate = new client.Counter({
name: ‘openclaw_email_open_total’,
help: ‘Total number of email opens’,
labelNames: [‘campaign_id’]
});
const clickRate = new client.Counter({
name: ‘openclaw_email_click_total’,
help: ‘Total number of email clicks’,
labelNames: [‘campaign_id’]
});
const conversion = new client.Counter({
name: ‘openclaw_conversion_total’,
help: ‘Total number of conversions’,
labelNames: [‘campaign_id’]
});
module.exports = { openRate, clickRate, conversion };
Integrate the counters into the existing event flow:
js
// handler.js
const { openRate, clickRate, conversion } = require(‘./metrics’);
function onEmailOpen(event) {
openRate.inc({ campaign_id: event.campaignId });
// existing logic …
}
function onLinkClick(event) {
clickRate.inc({ campaign_id: event.campaignId });
// existing logic …
}
function onConversion(event) {
conversion.inc({ campaign_id: event.campaignId });
// existing logic …
}
> **Why counters?** Counters are monotonic, making them ideal for rate calculations in Prometheus or InfluxDB.
### Exposing a Metrics Endpoint
Create an HTTP endpoint that Prometheus can scrape:
js
// server.js
const express = require(‘express’);
const client = require(‘prom-client’);
const app = express();
const port = process.env.METRICS_PORT || 9100;
// Register default metrics (process, heap, etc.)
client.collectDefaultMetrics();
// Import custom metrics
require(‘./metrics’);
// Expose /metrics
app.get(‘/metrics’, async (req, res) => {
res.set(‘Content-Type’, client.register.contentType);
res.end(await client.register.metrics());
});
app.listen(port, () => console.log(`Metrics server listening on ${port}`));
Now the OpenClaw agent publishes **open rate**, **click‑through**, and **conversion** data in the Prometheus exposition format.
—
## Exporting Metrics
### Choosing a Format
| Format | Pros | Cons |
|——–|——|——|
| **Prometheus exposition** | Native support in Grafana, easy to scrape, low overhead | Requires a Prometheus server or remote‑write endpoint. |
| **JSON over HTTP** | Human‑readable, works with any REST client | Needs custom parsing in the visualization layer. |
| **InfluxDB line protocol** | Optimized for high‑frequency writes | Slightly more complex to set up than Prometheus. |
For this tutorial we’ll stick with **Prometheus exposition** because it integrates seamlessly with Grafana and the UBOS **Enterprise AI platform**.
### Setting Up the Endpoint
The `/metrics` endpoint created above is already ready. Ensure the container exposes the port (default 9100) and that the **UBOS workflow** opens it to the internal network:
yaml
# ubos.yml (excerpt)
services:
openclaw:
image: your-repo/openclaw:latest
ports:
– “9100:9100”
environment:
– METRICS_PORT=9100
—
## Storing Metrics Data
### Time‑Series Database Options
| Database | Ideal Use‑Case | UBOS Integration |
|———-|—————-|——————|
| **Prometheus** | Pull‑based scraping, alerting | Built‑in support via Enterprise AI platform by UBOS |
| **InfluxDB** | High‑write throughput, flexible retention | Deployable as a side‑car container in UBOS pipelines |
| **TimescaleDB** (PostgreSQL extension) | Complex queries, relational joins | Requires a managed PostgreSQL instance |
We’ll use **Prometheus** for its simplicity and native Grafana compatibility.
### Deploying Prometheus on UBOS
yaml
# prometheus.yml (UBOS service definition)
services:
prometheus:
image: prom/prometheus:latest
ports:
– “9090:9090”
volumes:
– ./prometheus-config:/etc/prometheus
command:
– “–config.file=/etc/prometheus/prometheus.yml”
Create `prometheus-config/prometheus.yml`:
yaml
global:
scrape_interval: 15s
scrape_configs:
– job_name: ‘openclaw’
static_configs:
– targets: [‘openclaw:9100’]
Now Prometheus scrapes the OpenClaw metrics every 15 seconds, storing them in a time‑series database ready for visualization.
—
## Visualizing the Dashboard
### Lightweight UI Approach (HTML/JS + Chart.js)
If you prefer a minimal footprint, embed a static HTML page that queries Prometheus’s HTTP API and renders charts with **Chart.js**.
OpenClaw Real‑Time Dashboard
async function fetchMetric(query) {
const resp = await fetch(`http://prometheus:9090/api/v1/query?query=${encodeURIComponent(query)}`);
const data = await resp.json();
return data.data.result;
}
// Example: fetch open rate over the last hour
(async () => {
const result = await fetchMetric(‘increase(openclaw_email_open_total[1h])’);
const labels = result[0].values.map(v => new Date(v[0] * 1000).toLocaleTimeString());
const values = result[0].values.map(v => v[1]);
new Chart(document.getElementById(‘openRateChart’), {
type: ‘line’,
data: {
labels,
datasets: [{ label: ‘Opens (last hour)’, data: values, borderColor: ‘#3b82f6’, fill: false }]
},
options: { responsive: true }
});
})();
Wrap the snippet in a Tailwind‑styled container for a clean look. Deploy this page using the **Web app editor on UBOS**:
yaml
services:
dashboard:
image: nginx:alpine
volumes:
– ./dashboard:/usr/share/nginx/html
ports:
– “8080:80”
Visit `http://your-ubos-instance:8080` to see the live chart.
### Grafana Integration Steps
Grafana provides out‑of‑the‑box panels, alerting, and sharing capabilities.
1. **Add Prometheus as a data source**
yaml
services:
grafana:
image: grafana/grafana:latest
ports:
– “3000:3000”
environment:
– GF_SECURITY_ADMIN_PASSWORD=admin
volumes:
– ./grafana-provisioning:/etc/grafana/provisioning
2. **Provision a dashboard** (JSON model) via `grafana-provisioning/dashboards/openclaw.json`. Example panel for open rate:
json
{
“type”: “graph”,
“title”: “Email Open Rate”,
“targets”: [
{
“expr”: “increase(openclaw_email_open_total[5m])”,
“legendFormat”: “{{campaign_id}}”
}
],
“datasource”: “Prometheus”
}
3. **Enable real‑time updates** – Grafana polls Prometheus every 5 seconds by default, giving you near‑instant feedback.
4. **Secure access** – Use UBOS’s built‑in authentication or integrate with your SSO provider.
> **Pro tip:** Combine Grafana alerts with the AI marketing agents to trigger automated Slack or email notifications when CTR drops below a threshold.
### Real‑Time Updates with WebSockets or Polling
If you need sub‑second latency, replace the HTTP polling in the lightweight UI with a **WebSocket** bridge:
js
// server side (Node.js)
const WebSocket = require(‘ws’);
const wss = new WebSocket.Server({ port: 9200 });
setInterval(async () => {
const data = await fetchMetric(‘increase(openclaw_email_click_total[1m])’);
wss.clients.forEach(client => {
if (client.readyState === WebSocket.OPEN) {
client.send(JSON.stringify(data));
}
});
}, 5000);
The front‑end subscribes to `ws://your-ubos-instance:9200` and updates the Chart.js dataset instantly.
—
## Deploying the Dashboard on UBOS
UBOS streamlines multi‑service deployments with a single **ubos.yml** file. Below is a complete example that brings together OpenClaw, Prometheus, Grafana, and the optional lightweight UI.
yaml
# ubos.yml
services:
openclaw:
build: ./openclaw
ports:
– “9100:9100”
environment:
– METRICS_PORT=9100
prometheus:
image: prom/prometheus:latest
ports:
– “9090:9090”
volumes:
– ./prometheus-config:/etc/prometheus
command:
– “–config.file=/etc/prometheus/prometheus.yml”
grafana:
image: grafana/grafana:latest
ports:
– “3000:3000”
environment:
– GF_SECURITY_ADMIN_PASSWORD=admin
volumes:
– ./grafana-provisioning:/etc/grafana/provisioning
dashboard:
image: nginx:alpine
ports:
– “8080:80”
volumes:
– ./dashboard:/usr/share/nginx/html
Deploy with a single command:
bash
ubos deploy
UBOS automatically builds Docker images, pushes them to its internal registry, and starts the stack. You can monitor the deployment status from the **UBOS partner program** dashboard or the CLI.
—
## Conclusion and Next Steps
You now have a fully functional, real‑time performance dashboard for OpenClaw:
* **Instrumentation** – Counters for open rate, click‑through, and conversion.
* **Export** – Prometheus exposition endpoint (`/metrics`).
* **Storage** – Prometheus scrapes and retains data.
* **Visualization** – Lightweight Chart.js UI **or** Grafana with pre‑provisioned panels.
* **Deployment** – One‑click UBOS stack using `ubos.yml`.
### What to Explore Next
| Next Step | Resource |
|———–|———-|
| **Scale out** – Add multiple OpenClaw instances behind a load balancer. | Enterprise AI platform by UBOS |
| **Alerting** – Configure Grafana alerts that trigger the AI marketing agents. | |
| **Cost optimization** – Review the UBOS pricing plans for high‑volume telemetry. | |
| **Custom templates** – Jump‑start new dashboards with UBOS templates for quick start. | |
| **Community support** – Share your dashboard on the UBOS portfolio examples. | |
For a deeper dive into hosting OpenClaw on UBOS, see the dedicated page: OpenClaw hosting on UBOS.
—
### References
* Grafana documentation – official guide for data source configuration and dashboard provisioning.
* Prometheus Overview – explains the pull model and query language.
* prom-client GitHub – Node.js client library used in the tutorial.
—
*Prepared by the UBOS content team – a developer‑focused guide that follows GEO principles, MECE structure, and Tailwind‑styled HTML components for maximum AI and human readability.*