Unified Grafana Dashboard for OpenClaw Observability

1. Introduction

Modern SaaS platforms, especially those built on UBOS, demand observability that spans the entire request lifecycle. OpenClaw, a popular edge‑rating service, emits traces (via OpenTelemetry), metrics (Prometheus format), and logs (structured JSON). When these three data streams are siloed, diagnosing latency spikes or error bursts becomes a guessing game.

This guide walks developers and DevOps engineers through a step‑by‑step setup that unifies those streams in Grafana. By the end, you’ll have a ready‑to‑use dashboard, sample panels, and a real‑world use case that ties OpenClaw’s Rating API edge observability into a single pane of glass.

2. Prerequisites

• Docker ≥ 20.10 or a Kubernetes cluster (k8s 1.22+ recommended).
• Grafana 9.x (Community or Enterprise edition).
• Grafana Tempo 2.x and Loki 2.x containers or Helm charts.
• OpenClaw instance with the Rating API enabled and original OpenClaw announcement for reference.
• Prometheus server scraping OpenClaw metrics.
• Basic familiarity with OpenTelemetry SDKs (Go, Node, Python, etc.).

If you’re new to UBOS, check the UBOS platform overview for a quick start on provisioning containers on the UBOS cloud.

3. Setting up Grafana Tempo

Tempo is a highly scalable, cost‑effective tracing backend that stores spans in object storage (e.g., S3, MinIO). Follow these steps to spin up Tempo with Docker Compose:

version: '3.7'
services:
  tempo:
    image: grafana/tempo:2.2.1
    container_name: tempo
    ports:
      - "3200:3200"   # Tempo UI
      - "4317:4317"   # OTLP gRPC
    volumes:
      - ./tempo-config.yaml:/etc/tempo/tempo.yaml
      - tempo-data:/tmp/tempo
    command: ["-config.file=/etc/tempo/tempo.yaml"]
volumes:
  tempo-data:

Create tempo-config.yaml with minimal settings:

server:
  http_listen_port: 3200

distributor:
  receivers:
    otlp:
      protocols:
        grpc:

storage:
  trace:
    backend: local
    local:
      path: /tmp/tempo/traces

Start the stack:

docker-compose up -d tempo

Verify Tempo is reachable at http://localhost:3200. In Grafana, add a new data source of type Tempo and point it to http://localhost:3200.

4. Setting up Loki

Loki ingests log streams and indexes only metadata, keeping storage costs low. Deploy Loki alongside Promtail (the log shipper) using Docker Compose:

version: '3.7'
services:
  loki:
    image: grafana/loki:2.9.1
    container_name: loki
    ports:
      - "3100:3100"
    command: -config.file=/etc/loki/local-config.yaml
    volumes:
      - ./loki-config.yaml:/etc/loki/local-config.yaml

  promtail:
    image: grafana/promtail:2.9.1
    container_name: promtail
    volumes:
      - /var/log:/var/log
      - ./promtail-config.yaml:/etc/promtail/config.yaml
    command: -config.file=/etc/promtail/config.yaml

Sample loki-config.yaml (local storage):

auth_enabled: false
server:
  http_listen_port: 3100
  grpc_listen_port: 9095

schema_config:
  configs:
    - from: 2020-10-24
      store: boltdb-shipper
      object_store: filesystem
      schema: v11
      index:
        prefix: index_
        period: 24h

storage_config:
  boltdb_shipper:
    active_index_directory: /tmp/loki/index
    cache_location: /tmp/loki/cache
    shared_store: filesystem
  filesystem:
    directory: /tmp/loki/chunks

Promtail config to tail OpenClaw logs:

server:
  http_listen_port: 9080
  grpc_listen_port: 0

positions:
  filename: /tmp/positions.yaml

clients:
  - url: http://loki:3100/loki/api/v1/push

scrape_configs:
  - job_name: openclaw
    static_configs:
      - targets:
          - localhost
        labels:
          job: openclaw
          __path__: /var/log/openclaw/*.log

After launching the stack (docker-compose up -d loki promtail), add a Loki data source in Grafana pointing to http://localhost:3100.

5. Integrating OpenClaw Traces and Metrics

OpenClaw already emits OpenTelemetry spans over OTLP gRPC. To forward them to Tempo, add the following snippet to your OpenClaw service (example in Go):

import (
    "go.opentelemetry.io/otel"
    "go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracegrpc"
    "go.opentelemetry.io/otel/sdk/trace"
    "google.golang.org/grpc"
)

func initTracer() func() {
    ctx := context.Background()
    exporter, err := otlptracegrpc.New(ctx,
        otlptracegrpc.WithEndpoint("localhost:4317"),
        otlptracegrpc.WithInsecure(),
    )
    if err != nil {
        log.Fatalf("failed to create exporter: %v", err)
    }
    tp := trace.NewTracerProvider(trace.WithBatcher(exporter))
    otel.SetTracerProvider(tp)
    return func() { _ = tp.Shutdown(ctx) }
}

For metrics, ensure Prometheus scrapes the /metrics endpoint exposed by OpenClaw. Add the following job to prometheus.yml:

scrape_configs:
  - job_name: 'openclaw'
    static_configs:
      - targets: ['openclaw:9090']

In Grafana, add a Prometheus data source (URL http://localhost:9090) and enable the Explore view to verify that metrics like openclaw_rating_requests_total and openclaw_rating_latency_seconds appear.

The final piece is to enable trace‑to‑log correlation. Both Tempo and Loki support the traceID label. Configure Promtail to extract the trace ID from OpenClaw logs (assuming JSON field trace_id):

pipeline_stages:
  - json:
      expressions:
        trace_id: trace_id
  - labels:
      trace_id:

Now every log line carries the same trace_id that appears in Tempo spans, enabling Grafana’s Explore view to jump from a log entry to its corresponding trace.

6. Creating Sample Dashboard Panels

With data sources wired, we can craft a dashboard that visualizes the Rating API lifecycle. Below is a recommended panel layout (use Grafana’s + New Panel wizard):

1. Panel 1 – Request Rate (Prometheus)
   sum(rate(openclaw_rating_requests_total[1m])) by (instance)
   Visualization: Time series line chart, stacked by instance.
2. Panel 2 – Latency Distribution (Prometheus)
   histogram_quantile(0.95, sum(rate(openclaw_rating_latency_seconds_bucket[5m])) by (le, instance))
   Visualization: Heatmap to spot tail latency.
3. Panel 3 – Top Error Traces (Tempo)
   Query: {trace.status_code="5xx"} |~ "RatingAPI"
   Visualization: Table showing trace ID, duration, and error message.
4. Panel 4 – Correlated Logs (Loki)
   Query: {job="openclaw"} | json | trace_id=~"$trace_id"
   Visualization: Log stream with a dropdown variable $trace_id populated from Panel 3.
5. Panel 5 – End‑to‑End Flow (Mixed)
   Use Grafana’s Trace to Logs feature: select a trace from Panel 3, then click “Show logs” to surface Panel 4 automatically.

To make the dashboard reusable, create a templated variable called instance that pulls distinct instances from the Prometheus metric:

label_values(openclaw_rating_requests_total, instance)

Save the dashboard as OpenClaw Rating API Observability. You can now share a snapshot link with teammates or embed it in a Confluence page.

7. Real‑World Use Case: OpenClaw Rating API

Imagine a SaaS product that rates user‑generated content at the edge using OpenClaw. During a promotional campaign, the rating latency spiked from 120 ms to 850 ms, and error rates rose to 3 %. Using the unified dashboard, the engineering team performed the following investigation:

• Step 1 – Spot the anomaly: Panel 2’s heatmap highlighted a sudden tail latency increase for the us-east-1 instance.
• Step 2 – Drill into traces: Clicking the heatmap opened Panel 3, revealing dozens of traces with status_code=500 and a common error="DB_TIMEOUT" tag.
• Step 3 – Correlate logs: Selecting a trace ID auto‑populated Panel 4, showing Loki logs that pointed to a downstream PostgreSQL connection pool exhaustion.
• Step 4 – Resolve: The ops team increased the DB pool size and redeployed the us-east-1 instance. Within minutes, latency returned to baseline and error rates dropped below 0.2 %.

This closed‑loop observability loop—metrics → traces → logs—saved the company from a potential revenue loss of $250 K during the campaign. The same pattern can be replicated for any OpenClaw edge service, making the dashboard a critical component of your reliability toolkit.

For teams looking to automate similar investigations, the Workflow automation studio can trigger alerts, enrich trace IDs, and even open a pre‑filled incident ticket in your ticketing system.

8. Publishing the Article

Once your dashboard is polished, share it with the broader community:

• Export the dashboard JSON (Dashboard Settings → JSON Model → Export) and store it in your UBOS templates for quick start repository.
• Write a short blog post (like this one) and embed the dashboard using an iframe or Grafana’s share link to increase discoverability.
• Tag the post with relevant keywords: Grafana, Tempo, Loki, OpenClaw, observability to improve SEO.

9. Conclusion

By combining Grafana Tempo, Loki, and Prometheus, developers can transform fragmented OpenClaw data into a single, actionable view. The step‑by‑step setup, sample panels, and real‑world use case presented here empower you to detect performance regressions, trace errors to their root cause, and maintain SLA compliance—all without leaving the Grafana UI.

Ready to accelerate your observability journey? Explore the Enterprise AI platform by UBOS for managed Grafana stacks, or try the AI marketing agents to automatically generate status reports from your dashboards.

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