Incident Response Playbook for OpenClaw Rating API Edge CRDT Token‑Bucket

Introduction

OpenClaw’s Rating API powers real‑time content moderation and recommendation scoring at the network edge. The service relies on a Conflict‑Free Replicated Data Type (CRDT) backed token‑bucket algorithm to enforce rate limits while preserving eventual consistency across distributed nodes. When the bucket misbehaves—either over‑filling or draining too quickly—clients experience latency spikes, request rejections, or silent failures.

This playbook equips you with a repeatable, MECE‑structured workflow that covers Detection, Diagnosis, and Remediation. Each phase is broken into concrete metrics, log‑patterns, and corrective actions, ensuring you can restore service health within minutes rather than hours.

For a broader view of UBOS capabilities that complement OpenClaw, explore the UBOS platform overview and the Enterprise AI platform by UBOS.

1. Detection

Monitoring Metrics

The first line of defense is a robust observability stack. The following metrics should be visualized on a real‑time dashboard (e.g., Grafana, Datadog) and have alert thresholds defined:

• bucket_fill_rate – tokens added per second per edge node.
• bucket_drain_rate – tokens consumed per request.
• bucket_current_level – current token count (gauge).
• request_throttle_rate – percentage of requests rejected due to empty bucket.
• latency_p99 – 99th percentile response time for Rating API calls.

Spike detection can be automated with a custom alerting guide that triggers when request_throttle_rate > 5% or latency_p99 > 300ms for more than two consecutive minutes.

Alerting Signals

Alerts should be routed to on‑call channels (PagerDuty, Slack) with clear run‑book links. A well‑crafted alert payload includes:

{
  "alert_name": "OpenClaw Token‑Bucket Saturation",
  "severity": "critical",
  "node_id": "edge‑us‑east‑1a",
  "current_fill": 12,
  "current_drain": 150,
  "throttle_rate": "7.4%"
}

The alert message should embed a direct link to the OpenClaw host page for quick context switching.

2. Diagnosis

Log Analysis

Once an alert fires, pull the last 15 minutes of structured logs from the affected edge node. Key log fields to filter:

• event_type=token_bucket_update
• status=throttled
• request_id – correlates with tracing spans.

Example query for Elasticsearch:

GET openclaw-logs-*/_search
{
  "size": 100,
  "query": {
    "bool": {
      "must": [
        { "match": { "event_type": "token_bucket_update" }},
        { "range": { "@timestamp": { "gte": "now-15m" }}}
      ]
    }
  }
}

Look for patterns such as a sudden surge in status=throttled entries or missing token_bucket_update events, which may indicate a replication lag in the CRDT.

Root Cause Identification

Common root causes for token‑bucket anomalies include:

1. Configuration drift – a recent deployment changed bucket_capacity without updating the edge config.
2. Network partition – CRDT state diverges because nodes cannot exchange deltas.
3. Burst traffic – a legitimate traffic spike exceeds the designed fill rate.
4. Bug in the token‑bucket library – race conditions causing double‑decrement.

Cross‑reference the incident with the OpenClaw design guide to verify that the bucket parameters match the documented limits.

For deeper insight, you can spin up a temporary Web app editor on UBOS to simulate traffic patterns and reproduce the issue in a sandbox environment.

3. Remediation

Throttling Adjustments

If the root cause is a mis‑configured bucket, apply the following steps:

1. Locate the bucket_config.yaml for the affected region.
2. Increase bucket_capacity by 20% and adjust fill_rate to match observed traffic.
3. Commit the change and trigger a rolling restart via the Workflow automation studio.
4. Validate the new metrics for at least 10 minutes before closing the incident.

When traffic bursts are expected (e.g., product launch), pre‑emptively raise the bucket limits using the AI marketing agents to forecast demand and auto‑scale the token‑bucket parameters.

Token‑Bucket Reset Procedures

In cases where the bucket is stuck in an empty state due to a CRDT sync failure, perform a safe reset:

• Pause incoming traffic on the affected edge node using a temporary maintenance_mode=true flag.
• Run the reset command: clawctl bucket reset --node=edge‑us‑east‑1a.
• Force a state reconciliation by invoking clawctl crdt sync --node=edge‑us‑east‑1a.
• Re‑enable traffic and monitor the bucket_current_level gauge for stability.

Document the reset in the incident ticket and add a post‑mortem entry to the UBOS partner program knowledge base for future reference.

4. References

Design Guide

The comprehensive design specifications for the Rating API, including CRDT topology and token‑bucket mathematics, are available in the OpenClaw design guide.

Observability Guide

Detailed instructions for metric collection, dashboard creation, and tracing integration can be found in the Observability guide.

Alerting Guide

For alert rule templates, escalation policies, and on‑call rotation setup, refer to the Alerting guide.

5. Conclusion

A disciplined incident response process—rooted in real‑time detection, systematic diagnosis, and precise remediation—reduces Mean Time To Recovery (MTTR) for OpenClaw Rating API edge failures. By leveraging UBOS’s observability stack, automation studio, and the rich ecosystem of templates (e.g., AI Article Copywriter, AI Survey Generator), teams can iterate on playbooks and stay ahead of traffic‑induced throttling events.

Keep this playbook handy, integrate it into your run‑book repository, and regularly rehearse the steps during chaos engineering exercises. When the next token‑bucket anomaly surfaces, you’ll have a proven, GEO‑optimized response plan that both humans and AI assistants can follow instantly.