Unified Grafana Dashboard for OpenClaw Rating API Edge Metrics and Traces

1. Introduction

Observability is no longer a luxury; it’s a prerequisite for reliable SaaS platforms. When you expose a rate‑limiting API like OpenClaw Rating API Edge, you must monitor both the token‑bucket metrics (how many requests are allowed, denied, or throttled) and the distributed traces that reveal latency hotspots across micro‑services. Grafana excels at merging time‑series data (Prometheus) with trace data (Jaeger), giving you a single pane of glass for root‑cause analysis.

This senior‑engineer guide walks you through the entire pipeline—from provisioning the UBOS environment to publishing a production‑ready dashboard. All steps are reproducible on a local dev cluster or a cloud‑hosted UBOS instance. For a quick OpenClaw deployment reference, see the OpenClaw hosting guide.

2. Prerequisites

Before you start, verify that the following components are available and correctly versioned:

• UBOS cluster – a 3‑node UBOS deployment (v2.5+ recommended). See the UBOS platform overview for sizing guidelines.
• OpenClaw deployed – the Rating API Edge service must be running with its Prometheus exporter enabled. Follow the OpenClaw hosting guide for a one‑click install.
• Grafana installed – version 10.x or later, with admin access.
• Prometheus data source – scraping the /metrics endpoint of OpenClaw.
• Jaeger (OpenTelemetry) data source – receiving trace spans from the OpenClaw service via the OTLP exporter.

If you need a quick start for Grafana, the UBOS templates for quick start include a pre‑configured Grafana Helm chart.

3. Setting up Prometheus data source in Grafana

Grafana treats Prometheus as a first‑class time‑series source. Follow these steps:

1. Navigate to Data Sources → Add data source → select Prometheus.
2. Configure the URL to point at your Prometheus server, e.g. http://prometheus.ubos.svc.cluster.local:9090.
3. Set the access mode to Server (default) and enable Scrape interval of 15s for near‑real‑time metrics.
4. Test the connection. A green “Data source is working” message confirms the link.
5. Save & Apply. The data source now appears in the left‑hand menu.

4. Setting up Jaeger (OpenTelemetry) data source in Grafana

Jaeger integration is straightforward thanks to Grafana’s native Jaeger plugin.

1. Open Data Sources → Add data source → select Jaeger.
2. Endpoint URL: http://jaeger-collector.ubos.svc.cluster.local:14268/api/traces.
3. Trace storage: choose Jaeger (default) and set Max trace age to 48h for a balance between storage cost and debugging depth.
4. Authentication: if your cluster uses RBAC, enable Bearer token and paste the service‑account token from kubectl get secret $(kubectl get sa grafana -o jsonpath="{.secrets[0].name}") -o jsonpath="{.data.token}" | base64 --decode.
5. Test & Save. A successful test returns a list of recent traces.

“When you combine Prometheus and Jaeger in Grafana, you get a metric‑to‑trace correlation that cuts MTTR by up to 40 %.” – UBOS Observability Whitepaper

5. Creating a unified dashboard

Now that both data sources are live, we’ll assemble a dashboard that shows token‑bucket health side‑by‑side with request traces.

5.1 Panel for token‑bucket metrics

Add a new Time series panel and configure the query:

sum(rate(openclaw_token_bucket_consumed_total[1m])) by (instance)

Complement it with the bucket capacity to compute the utilization percentage:

100 * sum(rate(openclaw_token_bucket_consumed_total[1m])) by (instance) /
      sum(openclaw_token_bucket_capacity) by (instance)

Set the visualization to Gauge for an at‑a‑glance view of remaining quota. Use the Thresholds feature to color‑code green (≥ 80 % remaining), yellow (40‑80 %), and red (< 40 %).

5.2 Panel for distributed traces

Create a Trace to logs panel (Grafana 10+). In the query editor, select the Jaeger data source and use the following filter:

service.name = "openclaw-rating-api"
and operation.name = "RateLimitCheck"

Enable Trace list view and set the Time range to Last 5 minutes. This panel will surface the most recent traces, each clickable to drill down into span details.

5.3 Correlating metrics and traces

Correlation is achieved by linking the token‑bucket gauge to the trace panel via a Dashboard variable that captures the instance label.

1. Create a variable $instance with query label_values(openclaw_token_bucket_consumed_total, instance).
2. In the gauge panel, add a Repeat by variable set to $instance.
3. In the trace panel, add a filter instance = "$instance".

Now, selecting an instance from the dropdown instantly filters both panels, letting you see the exact quota usage and the associated request traces for that node.

6. Adding alerts and annotations

Proactive alerting prevents token‑bucket exhaustion before it impacts customers.

6.1 Alert rule for token‑bucket depletion

alert: OpenClawTokenBucketLow
expr: (sum(openclaw_token_bucket_remaining) by (instance) / sum(openclaw_token_bucket_capacity) by (instance)) < 0.2
for: 2m
labels:
  severity: critical
annotations:
  summary: "Token bucket low on {{ $labels.instance }}"
  description: "Remaining quota is below 20 % for the last 2 minutes."

Configure the alert to fire to your preferred UBOS partner program webhook or Slack channel.

6.2 Annotations for trace spikes

Add a dashboard annotation that marks any trace latency > 500 ms:

{
  "name": "High latency trace",
  "datasource": "Jaeger",
  "iconColor": "red",
  "enable": true,
  "query": "duration > 500ms and service.name = \"openclaw-rating-api\""
}

Annotations appear as vertical lines on the time‑series panel, instantly correlating spikes in quota consumption with slow traces.

7. Publishing the dashboard

Once satisfied, click Save dashboard, give it a meaningful title (e.g., “OpenClaw Unified Observability”), and set the Folder to Observability for easy discovery.

Export the JSON definition for version control:

{
  "dashboard": { ... },
  "folderId": 12,
  "overwrite": true
}

Store the JSON in your UBOS GitOps repository and let the Workflow automation studio apply it to all environments automatically.

8. Conclusion and next steps

You now have a production‑grade Grafana dashboard that unifies token‑bucket metrics from Prometheus with OpenTelemetry traces from Jaeger. This unified view accelerates incident response, reduces mean‑time‑to‑resolution, and provides a solid foundation for capacity planning.

Next steps you might consider:

• Integrate AI marketing agents to automatically generate post‑mortem reports from alert data.
• Leverage the Enterprise AI platform by UBOS to predict future quota exhaustion using historical trends.
• Deploy the UBOS templates for quick start such as the AI SEO Analyzer to monitor the health of your public API documentation.
• Explore the UBOS solutions for SMBs if you need a lighter footprint for edge deployments.
• Experiment with the GPT‑Powered Telegram Bot to receive real‑time alerts in Slack or Telegram channels.

For deeper dives into OpenClaw’s internal metrics, see the official OpenClaw documentation or the original news article that announced the new Edge token‑bucket exporter.