Unified Grafana Dashboard for OpenClaw Rating API Edge: Combining Prometheus Token‑Bucket Metrics and Jaeger Traces

Introduction

Monitoring high‑throughput APIs such as OpenClaw Rating API Edge demands more than isolated metrics or traces. Rate‑limiting data (token‑bucket counters) tells you how many requests are allowed, while Jaeger spans reveal where latency spikes occur. By unifying these signals in a single Grafana dashboard you gain a holistic view that shortens MTTR, improves capacity planning, and validates SLA compliance.

This guide synthesizes three foundational tutorials—Prometheus token‑bucket metrics, OpenTelemetry/Jaeger tracing, and Grafana dashboard creation—into a practical, step‑by‑step workflow that runs on the host OpenClaw on UBOS platform.

Recap of the Three Original Guides

2.1 Token‑bucket metrics in Prometheus

The token‑bucket algorithm is a popular rate‑limiting technique. Each bucket holds a configurable number of tokens that refill at a steady rate. When a request arrives, a token is consumed; if the bucket is empty, the request is rejected. Exposing the bucket state as Prometheus metrics (e.g., openclaw_rate_limit_tokens_total and openclaw_rate_limit_refill_seconds) lets you chart request capacity over time.

2.2 OpenTelemetry/Jaeger tracing for OpenClaw

OpenTelemetry instruments each microservice in the OpenClaw stack, sending spans to a Jaeger collector. Traces capture the full request journey—from ingress gateway through rating engines to database calls—allowing you to pinpoint latency contributors and error hotspots. The Self‑Hosting OpenClaw on UBOS guide details the required OTEL_EXPORTER_JAEGER_ENDPOINT configuration.

2.3 Building Grafana dashboards for OpenClaw

Grafana visualizes both Prometheus time‑series and Jaeger trace data. The Deploy Grafana and create a dashboard tutorial walks you through selecting an application version, setting secrets, and defining a dashboard JSON model. The result is a reusable panel library that can be extended with custom queries.

3. Unified Architecture Overview

The unified monitoring stack consists of four layers:

• Application Layer: OpenClaw services emit token‑bucket metrics via prometheus_client and OpenTelemetry spans via the Jaeger exporter.
• Observability Layer: Prometheus scrapes the /metrics endpoint; Jaeger receives spans over HTTP.
• Data‑Fusion Layer: Grafana’s Prometheus data source and Jaeger data source are combined in a single dashboard using mixed queries.
• Presentation Layer: End‑users view the unified dashboard on the UBOS Edge Node, with alerts for token depletion and trace latency thresholds.

The diagram below illustrates the data flow.

4. Step‑by‑Step Example

4.1 Prerequisites

• UBOS platform installed on an edge node (see the UBOS platform overview).
• OpenClaw source cloned and running via the Self‑Hosting OpenClaw on UBOS guide.
• Prometheus server and Jaeger collector containers deployed as UBOS services.
• Grafana 9+ installed with access to the UBOS web UI.
• Basic knowledge of Docker Compose and YAML configuration.

4.2 Exporting Token‑Bucket Metrics

Add the following snippet to the OpenClaw settings.py (or equivalent) to expose bucket state:

from prometheus_client import Gauge, start_http_server

# Define gauges
TOKEN_GAUGE = Gauge('openclaw_rate_limit_tokens_total',
                    'Current number of tokens in the bucket',
                    ['service'])
REFILL_GAUGE = Gauge('openclaw_rate_limit_refill_seconds',
                     'Seconds until next token refill',
                     ['service'])

def update_metrics(service_name, tokens, seconds_to_refill):
    TOKEN_GAUGE.labels(service=service_name).set(tokens)
    REFILL_GAUGE.labels(service=service_name).set(seconds_to_refill)

# Start metrics endpoint on port 9100
start_http_server(9100)

Call update_metrics() inside the rate‑limiter logic after each request. Prometheus will scrape http://edge-node-ip:9100/metrics.

4.3 Capturing Jaeger Traces

Enable OpenTelemetry in each OpenClaw microservice by adding the following environment variables (see the OpenAI ChatGPT integration for a similar pattern):

OTEL_EXPORTER_JAEGER_ENDPOINT=http://jaeger-collector:14268/api/traces
OTEL_TRACES_EXPORTER=jaeger
OTEL_RESOURCE_ATTRIBUTES=service.name=openclaw-{{service_name}}
OTEL_METRICS_EXPORTER=none

The opentelemetry-instrumentation library automatically creates spans for HTTP handlers, database queries, and custom business logic. Verify trace flow in Jaeger UI at http://jaeger-ui:16686.

4.4 Creating a Combined Grafana Dashboard

Follow these sub‑steps to build the unified view:

1. Add Data Sources: In Grafana, navigate to Configuration → Data Sources. Add Prometheus (URL: http://prometheus:9090) and Jaeger (URL: http://jaeger-query:16686).
2. Create a New Dashboard: Click + → Dashboard → Add new panel. Choose Mixed as the data source to allow both Prometheus and Jaeger queries in the same panel.
3. Panel 1 – Token Bucket Health:

   query: sum(openclaw_rate_limit_tokens_total) by (service)
   

   Set the visualization to Gauge and add thresholds (e.g., green > 80%, yellow 30‑80%, red < 30%).

4. Panel 2 – Refill Rate:

   query: avg(openclaw_rate_limit_refill_seconds) by (service)
   

   Use a Time series chart to see refill dynamics.

5. Panel 3 – Trace‑Based Latency:

   Select Jaeger as the data source and use the traceql query:

   traceql
   {
     name = "http.server.request"
     duration > 200ms
   }
   

   Visualize as a Heatmap to spot slow endpoints.

6. Panel 4 – Correlation Matrix: Use Grafana’s Stat panel with a mixed query that joins token count and trace latency via Prometheus + Jaeger (requires the Prometheus Remote Write bridge). This panel shows “Requests per second vs. 95th‑percentile latency”.
7. Save & Share: Click Save dashboard, give it a name like OpenClaw Unified Monitoring, and enable Auto‑Refresh every 15 seconds.

For a ready‑made JSON model, explore the UBOS templates for quick start. The AI SEO Analyzer can also validate that your dashboard naming follows best‑practice conventions.

4.5 Verifying the Integration

After deploying the dashboard:

• Generate traffic against OpenClaw (e.g., curl -X POST http://api:8080/rate).
• Observe token‑bucket gauges decreasing and refilling according to your configured limits.
• Open Jaeger UI, locate a trace for the same request, and confirm the latency matches the Grafana heatmap.
• Trigger a burst that exhausts the bucket; Grafana should flash the red threshold while Jaeger shows increased queue time.

If any panel shows no data, double‑check the Prometheus scrape config and Jaeger exporter endpoint. The Grafana documentation provides troubleshooting tips for mixed data sources.

5. Best Practices and Troubleshooting

5.1 Metric Naming Conventions

Use a consistent prefix (e.g., openclaw_) and include service labels. This simplifies aggregation across microservices and prevents naming collisions when you add new components.

5.2 Trace Sampling Strategy

Sampling at 1‑5 % reduces Jaeger storage overhead while still providing statistically meaningful latency data. Adjust OTEL_TRACES_SAMPLER accordingly.

5.3 Alerting

• Configure Prometheus alerts for openclaw_rate_limit_tokens_total < 20 to pre‑empt throttling.
• Set Jaeger‑derived alerts for 95th‑percentile latency > 500 ms.
• Route alerts through the Workflow automation studio to Slack or PagerDuty.

5.4 Common Pitfalls

6. Conclusion and Next Steps

By integrating Prometheus token‑bucket metrics with Jaeger traces, you transform raw numbers into actionable insights. The unified Grafana dashboard not only visualizes rate‑limiting health but also ties it directly to request latency, enabling rapid root‑cause analysis.

Ready to extend this setup? Consider:

• Adding AI Video Chat Bot alerts that speak when thresholds breach.
• Leveraging the AI Email Marketing module to notify stakeholders automatically.
• Embedding the dashboard in a custom Web app editor on UBOS for a branded monitoring portal.

For pricing details, explore the UBOS pricing plans. If you’re a startup, the UBOS for startups program offers credits for early‑stage monitoring workloads.

Need a step‑by‑step walkthrough of deploying OpenClaw on the UBOS edge? Follow the official host OpenClaw on UBOS guide to get your API up and running in minutes.