Andrii Bidochko

• Updated: March 14, 2026
• 3 min read

Observability and Debugging OpenClaw on UBOS: Metrics, Tracing, Logging, and Alerting

# Observability and Debugging OpenClaw on UBOS: Metrics, Tracing, Logging, and Alerting

Operators deploying **OpenClaw** on UBOS need deep visibility into the system to ensure reliability and fast issue resolution. This guide walks through a complete observability stack built on Prometheus, OpenTelemetry, Grafana, centralized logging, and alerting rules.

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## 1. Metrics with Prometheus

1. **Expose OpenClaw metrics** – Enable the `/metrics` endpoint in the OpenClaw configuration. UBOS ships a Prometheus exporter that scrapes this endpoint automatically.
2. **Configure UBOS Prometheus** – Add a scrape job in the `prometheus.yml` located at `/etc/ubos/prometheus`:
yaml
– job_name: ‘openclaw’
static_configs:
– targets: [‘localhost:9090’]

3. **Validate** – Visit `http:///prometheus/targets` and ensure the OpenClaw target is **UP**.

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## 2. Distributed Tracing with OpenTelemetry

1. **Instrument OpenClaw** – Include the OpenTelemetry SDK in the OpenClaw runtime and configure an exporter (e.g., Jaeger or OTLP).
2. **UBOS collector** – UBOS runs an OpenTelemetry Collector that receives traces on port `4317`. Add the collector endpoint to OpenClaw’s config:

{
“otel”: {
“endpoint”: “http://localhost:4317″
}
}

3. **View traces** – Use the built‑in Jaeger UI at `http:///jaeger` to explore request flows across the OpenClaw services.

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## 3. Visualisation with Grafana Dashboards

UBOS provides a pre‑configured Grafana instance. Import the **OpenClaw Observability** dashboard (ID: `12345`) or create a custom one:

– **CPU & Memory** – `node_cpu_seconds_total`, `node_memory_MemAvailable_bytes`
– **OpenClaw request latency** – `histogram_quantile(0.95, sum(rate(openclaw_http_request_duration_seconds_bucket[5m])) by (le))`
– **Error rate** – `rate(openclaw_http_requests_total{status=~”5..”}[5m])`

The dashboard automatically picks up metrics from the Prometheus data source configured by UBOS.

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## 4. Centralised Logging

1. **Log forwarder** – UBOS runs a Fluent Bit agent that tails `/var/log/openclaw/*.log` and forwards logs to an Elasticsearch cluster.
2. **Kibana** – Access logs via Kibana at `http:///kibana`. Create a saved search for `source:openclaw` and add it to a dashboard.
3. **Log enrichment** – Enable JSON logging in OpenClaw to capture structured fields like `request_id`, `user`, and `error_code`.

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## 5. Alerting Rules

Define Prometheus alerting rules in `/etc/ubos/alertmanager/rules.yml`:
yaml
groups:
– name: openclaw-alerts
rules:
– alert: OpenClawHighErrorRate
expr: rate(openclaw_http_requests_total{status=~”5..”}[5m]) > 0.05
for: 2m
labels:
severity: critical
annotations:
summary: “High error rate on OpenClaw”
description: “Error rate > 5% for the last 5 minutes.”

The UBOS Alertmanager routes alerts to Slack, email, or PagerDuty based on severity.

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## 6. One‑click Deployment Reference

For a complete, production‑ready deployment of OpenClaw with the observability stack, follow the official guide: [Host OpenClaw on UBOS](https://ubos.tech/host-openclaw/)

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### Summary

– **Prometheus** scrapes OpenClaw metrics.
– **OpenTelemetry** provides end‑to‑end tracing.
– **Grafana** visualises key performance indicators.
– **Fluent Bit + Elasticsearch** centralises logs.
– **Alertmanager** notifies operators on critical conditions.

With these components in place, operators gain full visibility into OpenClaw’s health and can quickly debug issues, ensuring a reliable service for end users.

Andrii Bidochko

CTO UBOS

Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.