Unified Observability for OpenClaw on UBOS: Combining OpenTelemetry Tracing, Prometheus Metrics, and Grafana Dashboards

1. Introduction

OpenClaw is a powerful, open‑source web‑crawler framework that many SaaS teams embed into their data‑pipeline. When deployed on UBOS, developers gain access to a low‑code platform that streamlines deployment, scaling, and integration. However, without a robust observability stack, pinpointing latency spikes or resource bottlenecks becomes a guessing game.

This guide walks you through a step‑by‑step implementation of a unified observability solution that merges three industry‑standard tools:

• OpenTelemetry for distributed tracing.
• Prometheus for time‑series metrics.
• Grafana for visual dashboards and alerting.

By the end, you’ll have a production‑ready setup that can be replicated across any OpenClaw instance hosted on UBOS.

2. Overview of Unified Observability

Unified observability means collecting traces, metrics, and logs in a single, correlated view. The three pillars work together as follows:

When these layers are tightly integrated, a single Grafana panel can show a spike in request latency, the corresponding trace details, and the underlying CPU metric—all in real time.

3. Setting up OpenTelemetry Tracing

3.1 Install OpenTelemetry SDK

UBOS supports container‑based deployments, so you can add the OpenTelemetry SDK directly to your OpenClaw Dockerfile.

FROM python:3.11-slim

# Install OpenTelemetry packages
RUN pip install opentelemetry-sdk opentelemetry-instrumentation \
    opentelemetry-exporter-otlp

# Copy OpenClaw source
COPY . /app
WORKDIR /app

# Instrument the entrypoint
ENTRYPOINT ["opentelemetry-instrument", "python", "run_claw.py"]

3.2 Configure Tracing Exporter

UBOS provides a managed Enterprise AI platform that includes an OTLP collector. Point the SDK to this endpoint:

import os
from opentelemetry import trace
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter

resource = Resource(attributes={
    "service.name": "openclaw",
    "service.version": "1.0.0",
    "deployment.environment": os.getenv("UBOS_ENV", "production")
})

provider = TracerProvider(resource=resource)
trace.set_tracer_provider(provider)

otlp_exporter = OTLPSpanExporter(endpoint="http://otel-collector.ubos.svc:4317", insecure=True)
span_processor = BatchSpanProcessor(otlp_exporter)
provider.add_span_processor(span_processor)

tracer = trace.get_tracer(__name__)

3.3 Verify Traces

After redeploying, generate a few crawl jobs and open the UBOS partner program dashboard. You should see trace IDs like 4bf92f3577b34da6a3ce929d0e0e4736 appear in the OTLP UI. Use the OpenTelemetry specification for deeper validation.

4. Configuring Prometheus Metrics Exporter

4.1 Install Prometheus Client

Python’s prometheus_client library is lightweight and works out‑of‑the‑box with UBOS.

RUN pip install prometheus_client

4.2 Expose Metrics Endpoint

Add a small HTTP server that serves /metrics. UBOS automatically maps container ports to a public endpoint.

from prometheus_client import start_http_server, Counter, Summary
import time

REQUEST_COUNT = Counter('openclaw_requests_total', 'Total crawl requests')
REQUEST_LATENCY = Summary('openclaw_request_latency_seconds', 'Latency per request')

def crawl(url):
    REQUEST_COUNT.inc()
    with REQUEST_LATENCY.time():
        # Simulated crawl logic
        time.sleep(0.2)

if __name__ == "__main__":
    start_http_server(8000)  # Exposes /metrics on port 8000
    while True:
        crawl("https://example.com")

4.3 Scrape Configuration

In the UBOS platform overview, add a Prometheus scrape job that points to the container’s metrics port.

scrape_configs:
  - job_name: 'openclaw'
    static_configs:
      - targets: ['openclaw-service.ubos.svc:8000']

After reloading Prometheus, you’ll see metrics like openclaw_requests_total and openclaw_request_latency_seconds appear in the UI.

5. Building Grafana Dashboards

5.1 Add Prometheus Data Source

Navigate to Configuration → Data Sources → Add data source** in Grafana, select Prometheus**, and set the URL to the Prometheus service exposed by UBOS (e.g., http://prometheus.ubos.svc:9090).

5.2 Create Dashboards for Traces and Metrics

Use the built‑in Web app editor on UBOS to generate a JSON model for a new dashboard. Below is a minimal example that combines a latency graph with a trace link.

{
  "title": "OpenClaw Unified Observability",
  "panels": [
    {
      "type": "graph",
      "title": "Request Latency (seconds)",
      "targets": [
        {
          "expr": "rate(openclaw_request_latency_seconds_sum[1m]) / rate(openclaw_request_latency_seconds_count[1m])",
          "legendFormat": "Avg Latency"
        }
      ]
    },
    {
      "type": "table",
      "title": "Recent Traces",
      "targets": [
        {
          "expr": "otel_trace_id{service=\"openclaw\"}",
          "legendFormat": "{{trace_id}}"
        }
      ],
      "transformations": [
        {
          "id": "addFieldFromCalc",
          "options": {
            "name": "Trace Link",
            "calc": "concat('https://grafana.ubos.svc/d/trace/', ${__value.raw})"
          }
        }
      ]
    }
  ]
}

5.3 Alerting Setup

Define an alert rule that fires when the 5‑minute average latency exceeds 2 seconds:

avg_over_time(openclaw_request_latency_seconds_sum[5m]) / avg_over_time(openclaw_request_latency_seconds_count[5m]) > 2

Configure the notification channel to send alerts to Slack, email, or the UBOS AI marketing agents for automated incident tickets.

6. Step‑by‑step Example

1. Fork the OpenClaw repo on your UBOS workspace.
2. Add the OpenTelemetry and Prometheus dependencies to requirements.txt.
3. Update the Dockerfile as shown in Section 3.1.
4. Insert the tracing initialization code (Section 3.2) at the top of run_claw.py.
5. Expose the metrics server (Section 4.2) and rebuild the container.
6. Deploy the container via the UBOS OpenClaw hosting page. UBOS will automatically provision a public URL and a health‑check endpoint.
7. In the UBOS console, enable the Prometheus scrape job (Section 4.3).
8. Open Grafana, add the Prometheus data source, and import the dashboard JSON from Section 5.2.
9. Trigger a few crawl jobs from the UBOS UI and watch the traces appear in real time.
10. Adjust alert thresholds as needed and integrate with your incident‑response workflow.

Following these steps gives you a production‑grade observability stack without writing a single line of YAML outside of UBOS’s low‑code interface.

7. Embedding Internal Link

For readers who want to explore more about hosting OpenClaw on UBOS, the dedicated page OpenClaw hosting on UBOS provides a quick‑start wizard, pricing details, and a one‑click deployment button.

8. Conclusion and Next Steps

Unified observability transforms OpenClaw from a black‑box crawler into a transparent, self‑healing service. By leveraging OpenTelemetry, Prometheus, and Grafana within the UBOS ecosystem, you gain:

• Instant visibility into request latency and error rates.
• Correlated trace data that shortens mean‑time‑to‑resolution.
• Scalable metrics collection that grows with your workload.
• Custom dashboards and alerts that align with business SLAs.

Ready to scale further? Consider exploring the Enterprise AI platform by UBOS for advanced model serving, or the UBOS partner program to get dedicated support and co‑marketing opportunities.

Start building your observability pipeline today and let UBOS handle the heavy lifting so you can focus on extracting insights from the web.