- Updated: March 18, 2026
- 6 min read
Exposing Prometheus Metrics from OpenClaw Edge Token Bucket Limiter and Building Grafana Dashboards
To expose Prometheus‑compatible metrics from the OpenClaw Rating API Edge Token Bucket limiter, build a Grafana dashboard, and configure alerting rules, you need to enable the metrics endpoint in OpenClaw, add a Prometheus scrape job, create visual panels in Grafana, and define alerting thresholds for capacity and error conditions.
Introduction
OpenClaw’s Edge Token Bucket limiter is a powerful rate‑limiting mechanism that protects your AI services from overload. While the limiter works out‑of‑the‑box, operators often ask how to monitor its health in real time. This guide walks you through the complete workflow: exposing Prometheus‑compatible metrics, visualising them with Grafana, and setting up alerts for capacity breaches or unexpected errors. The steps are tailored for deployments on the OpenClaw host on UBOS platform, so you can leverage UBOS’s one‑click workflow and built‑in observability stack.
Prerequisites
- A running OpenClaw instance on UBOS (see the Self‑Hosting OpenClaw on UBOS guide for setup details).
- Access to the UBOS UBOS platform overview dashboard.
- Prometheus server (version 2.30+ recommended) reachable from the OpenClaw container.
- Grafana 9+ installed on the same network.
- Basic knowledge of Docker, YAML, and JSON.
1. Exposing Prometheus‑compatible metrics from the OpenClaw Rating API Edge Token Bucket limiter
The limiter already emits internal counters, but they are not exposed via HTTP by default. Follow these steps to enable the /metrics endpoint.
a. Update the OpenClaw configuration
Add a metrics block to config.yaml inside your OpenClaw workspace:
limiter:
type: token_bucket
capacity: 5000
refill_rate: 100
metrics:
enabled: true
path: /metrics
port: 9091
b. Re‑deploy the service
Run the UBOS one‑click workflow to apply the changes:
ubos restart openclaw
OpenClaw now listens on http://<host-ip>:9091/metrics. Verify with curl:
curl http://localhost:9091/metrics | head -n 10
c. Sample metric output
Typical output includes counters for total requests, allowed tokens, and rejected requests:
# HELP openclaw_limiter_requests_total Total number of requests processed by the limiter
# TYPE openclaw_limiter_requests_total counter
openclaw_limiter_requests_total 12457
# HELP openclaw_limiter_allowed_total Tokens successfully granted
# TYPE openclaw_limiter_allowed_total counter
openclaw_limiter_allowed_total 11980
# HELP openclaw_limiter_rejected_total Requests rejected due to rate limit
# TYPE openclaw_limiter_rejected_total counter
openclaw_limiter_rejected_total 477
2. Setting up a Prometheus scrape job
Prometheus needs to know where to pull the metrics. Add a new job to prometheus.yml:
scrape_configs:
- job_name: 'openclaw_limiter'
static_configs:
- targets: [':9091']
metrics_path: '/metrics'
scheme: http
Reload Prometheus (or restart the container) to apply the configuration:
docker exec prometheus prometheus --web.enable-lifecycle
curl -X POST http://localhost:9090/-/reload
Navigate to the Prometheus UI (Prometheus Overview) and query openclaw_limiter_requests_total to confirm data ingestion.
3. Building a Grafana dashboard to visualise the metrics
Grafana provides a rich UI for time‑series data. The following steps create a reusable dashboard for the limiter.
a. Add Prometheus as a data source
- Open Grafana → Configuration → Data Sources → Add data source.
- Select Prometheus and set the URL to
http://:9090. - Click Save & test.
b. Create a new dashboard
Click + → Dashboard → Add new panel. Use the following queries for each panel.
Panel 1 – Total Requests
sum(rate(openclaw_limiter_requests_total[1m]))
Panel 2 – Allowed vs. Rejected
sum(rate(openclaw_limiter_allowed_total[1m]))
sum(rate(openclaw_limiter_rejected_total[1m]))
Panel 3 – Bucket Fill Level
If you expose a gauge openclaw_limiter_current_tokens, visualise it as a gauge:
openclaw_limiter_current_tokens
Arrange the panels in a 2‑column layout using Tailwind’s grid utilities:
c. Save and share
Click Save dashboard, give it a name like OpenClaw Limiter Monitoring, and enable Snapshot for quick sharing with teammates.
4. Configuring alerting rules for capacity and error conditions
Prometheus alerting rules fire when a metric crosses a threshold for a defined period. Add the following rules to alerts.yml (or embed them in prometheus.yml under rule_files).
# Alert when the bucket is > 80% full for 5 minutes
- alert: LimiterHighUtilization
expr: (openclaw_limiter_current_tokens / openclaw_limiter_capacity) > 0.8
for: 5m
labels:
severity: warning
annotations:
summary: "OpenClaw limiter utilization is high"
description: "Token bucket is {{ $value | printf \"%.2f\" }}% full for more than 5 minutes."
# Alert when rejected requests exceed 5% of total requests over 2 minutes
- alert: LimiterExcessiveRejections
expr: (sum(rate(openclaw_limiter_rejected_total[2m])) / sum(rate(openclaw_limiter_requests_total[2m])) > 0.05
for: 2m
labels:
severity: critical
annotations:
summary: "High rejection rate on OpenClaw limiter"
description: "Rejection rate is {{ $value | printf \"%.2%\" }} over the last 2 minutes."
Reload Prometheus alerts and verify them in the UI. In Grafana, create an Alerting → Notification channel (e.g., Slack or email) and bind the alerts to the channel.
5. Referencing the earlier rate‑limiter guide
The rate‑limiter fundamentals are covered in the Self‑Hosting OpenClaw on UBOS guide. That article explains how the token bucket works, how to tune capacity and refill_rate, and why observability matters for production workloads. By combining those concepts with the monitoring steps above, you achieve a full‑stack observability loop: configuration → metrics → visualization → alerting.
6. Extending the monitoring stack (optional)
UBOS offers a suite of complementary services that can enrich your observability pipeline:
- AI marketing agents – integrate usage metrics for campaign‑driven AI calls.
- Workflow automation studio – trigger remediation playbooks when alerts fire.
- Web app editor on UBOS – build a custom dashboard that merges OpenClaw metrics with business KPIs.
- Enterprise AI platform by UBOS – scale monitoring across multiple OpenClaw clusters.
- UBOS solutions for SMBs – a cost‑effective monitoring bundle for smaller teams.
Conclusion and next steps
By exposing the Edge Token Bucket limiter’s metrics, wiring them into Prometheus, visualising with Grafana, and defining precise alerting rules, you gain real‑time insight into rate‑limit health and can react before traffic spikes cause service degradation. The workflow aligns with UBOS’s UBOS pricing plans, ensuring you only pay for the resources you actually use.
Ready to take the next step?
- Deploy the configuration changes described above on your OpenClaw instance.
- Validate metric collection in Prometheus and fine‑tune the scrape interval.
- Customize the Grafana dashboard to match your team’s KPIs.
- Set up notification channels that fit your incident‑response workflow.
- Explore UBOS’s broader ecosystem – for example, the UBOS templates for quick start include pre‑built monitoring stacks.
With observability in place, you can focus on building smarter AI agents rather than firefighting rate‑limit outages.