OpenClaw Prometheus Metrics Explained

OpenClaw’s Prometheus exporter is not just “health but in Prometheus format.” The docs describe a deliberate diagnostics surface that translates bounded internal events into low-cardinality metrics you can scrape through the normal gateway auth boundary.

What it is

When the diagnostics-prometheus plugin is enabled, the gateway serves Prometheus exposition text at an authenticated HTTP route. The exported series cover run outcomes, queue behavior, model usage, tool execution, session state, memory pressure, and exporter health.

How it works

The exporter listens to trusted internal diagnostics events rather than scraping logs. It then emits bounded metrics with intentionally limited label space so your monitoring system stays stable instead of turning into a high-cardinality accident.

• The route lives at /api/diagnostics/prometheus and inherits gateway auth rather than exposing a public anonymous /metrics surface.
• diagnostics.enabled must be true, otherwise the route can exist while staying effectively empty.
• The docs describe a global retained-series cap of 2048 and a counter that increments when new series are dropped.
• Prometheus and OpenTelemetry export are separate surfaces; you can run either, both, or neither.

Why operators care

Operators care because quantified visibility changes how you run the gateway. Queue wait time, token volume, cost accumulation, and tool latency let you catch regressions earlier than human complaints usually do. The low-cardinality policy also matters because observability that melts your monitoring stack is not observability.

Boundaries that matter

This exporter is metrics-only. If you need traces, logs, or collector-based routing, the docs point you toward OpenTelemetry export instead. It also deliberately refuses to leak prompt text, response text, session keys, hostnames, file paths, or secrets into metric labels, which is exactly the restraint you want.

Rollout approach

For understanding OpenClaw Prometheus metrics before you alert on them, keep the first pass small: one owner, one environment, one visible test, and one rollback path. OpenClaw features get powerful once they touch real chats or devices, so a short rehearsal is usually safer than a giant configuration sprint.

Common mistake

The common mistake is treating every visible label as free. The docs go out of their way to explain bounded labels and the retained-series cap because monitoring systems fail differently from application runtimes.

Maintenance rhythm

Write down the exact command, config path, auth assumption, and verification step you used. A short runbook note is cheaper than rediscovering the same behavior during an outage. Review your dashboards occasionally against the exporter docs so you do not base alerts on assumptions that were never guaranteed.

Safety checks

Keep the exporter protected, use rate and increase style queries that tolerate restarts, and treat the dropped-series counter as a real signal instead of background noise.

How to tell you understand it

You understand the exporter when you can explain why the route is authenticated, why empty output can still be “correct” before traffic, and why low-cardinality label discipline is part of the feature rather than a limitation.

One operator-friendly test is to explain the feature without product fluff: what owns it, what permissions gate it, and which fallback keeps it predictable when the happy path disappears.

That framing matters because OpenClaw features usually look magical only from far away. Up close, the dependable ones have a clear owner, a bounded trust surface, and a boring recovery path when the network, model, device, or auth layer stops cooperating. If you can describe those three pieces from the docs, you usually understand the feature well enough to operate it without superstition.

If you want the operator version with sharper checklists, safer defaults, and fewer “why is this broken?” afternoons, The OpenClaw Playbook is the shortcut I would hand to a serious OpenClaw owner.