OpenClaw on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview

Estimated time: 45–90 minutes

OpenClaw is a multi-tenant AI agent gateway for running isolated, persistent AI assistants backed by Anthropic models, with dedicated GCS-Fuse workspaces and optional Telegram or Slack channel integration. This lab takes you through the full operational lifecycle of the OpenClaw on GKE Autopilot module on Google Cloud: deploy it, access and verify it, run it day-to-day, observe it, diagnose common problems, and tear it down.

The lab focuses on operating the GKE module and the Google Cloud platform, not on OpenClaw product features. For the complete list of provisioned services and every configuration input (organised by group), see the Configuration Guide — this lab deliberately does not duplicate that detail so it stays accurate over time.

Objectives

By the end of this lab you will be able to:

• Deploy the module from the RAD platform and locate the resources it provisions.
• Connect to the GKE cluster and access the running workload.
• Perform day-2 operations — inspect, scale, update, and manage secrets and storage.
• Observe the workload with Cloud Logging and Cloud Monitoring.
• Diagnose and resolve the most common deployment and runtime issues.
• Tear the deployment down cleanly.

Prerequisites

• Services_GCP deployed in the target project (provides the VPC, GKE Autopilot cluster, Artifact Registry, and shared service accounts this module depends on).
• A Google Cloud project with billing enabled.
• gcloud CLI and kubectl installed; gcloud auth login and gcloud auth application-default login completed.
• Project Owner (or equivalent) IAM on the project.
• RAD platform access with permission to deploy modules into the project.

Set these shell variables once; every task below reuses them:

export PROJECT="<your-gcp-project-id>"
export REGION="us-central1"           # the region you deploy into

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Task 1 — Deploy the module [Automated]

1. Click Deploy in the RAD platform top navigation, open OpenClaw (GKE) from the Platform Modules list to start configuration, set project_id, and review the inputs. An Anthropic API key is required on the first deploy — set it in the corresponding input field. Configure only what else you need — the Configuration Guide documents every input by group, with defaults. Review the estimated cost (if credits are enabled) and click Deploy, which opens the deployment status page with real-time logs.

2. The platform builds a custom container image (layering entrypoint.sh onto the upstream OpenClaw image), creates a GCS workspace bucket mounted at /data via the GCS Fuse CSI driver, stores the Anthropic API key and gateway token in Secret Manager, and deploys the Kubernetes workload. OpenClaw requires no Cloud SQL or init job — agent state lives entirely on GCS. First deploys take roughly 15–25 minutes (Cloud Build dominates; GKE node provisioning adds time for new clusters).

3. Connect to the cluster and discover the namespace with name-agnostic filters:

   CLUSTER=$(gcloud container clusters list --project="$PROJECT" --format="value(name)" --limit=1)
   gcloud container clusters get-credentials "$CLUSTER" --region="$REGION" --project="$PROJECT"
   
   NS=$(kubectl get ns -o name | grep openclaw | head -1 | cut -d/ -f2)
   echo "Cluster: $CLUSTER   Namespace: $NS"
   kubectl get all -n "$NS"

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Task 2 — Access & verify [Manual]

1. Confirm the workload is running and find its external address:

   kubectl get pods,svc -n "$NS"
   EXTERNAL_IP=$(kubectl get svc -n "$NS" \
     -o jsonpath='{.items[?(@.spec.type=="LoadBalancer")].status.loadBalancer.ingress[0].ip}')
   echo "External IP: $EXTERNAL_IP"

   If service_type is ClusterIP (internal only), use port-forward instead:

   kubectl port-forward svc/$(kubectl get svc -n "$NS" -o jsonpath='{.items[0].metadata.name}') \
     8080:8080 -n "$NS"
   # Access at http://localhost:8080

2. Confirm the service is healthy:

   curl -s "http://${EXTERNAL_IP}/health"   # expect {"status":"ok"}

3. Retrieve the gateway token from Secret Manager to authenticate API calls:

   GW_SECRET=$(gcloud secrets list --project="$PROJECT" \
     --filter="name~openclaw~gateway-token" --format="value(name)" --limit=1)
   gcloud secrets versions access latest --secret="$GW_SECRET" --project="$PROJECT"

   The gateway token is the credential used by OpenClaw clients and integrations. The Anthropic API key can similarly be retrieved from its Secret Manager secret if needed (filter on ~openclaw~anthropic-api-key).

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Task 3 — Operate & keep it running (Day-2) [Manual]

1. Inspect the workload — deployment, pods, and the horizontal autoscaler:

   kubectl get deploy,pods,hpa,pvc -n "$NS"
   kubectl describe deploy -n "$NS"

2. Scale by changing the min/max instance inputs and clicking Update on the deployment details page — the module owns the workload spec, so scaling is a configuration change, not a manual kubectl scale (a manual edit would be reverted on the next apply). Note that OpenClaw is stateful; the Service uses ClientIP session affinity so that WebSocket connections are consistently routed to the same pod.

3. Update the application version by changing the version input via Update on the deployment details page; a new image builds and a rolling update replaces the pods.

4. Manage secrets, storage, and jobs:

   kubectl get secrets -n "$NS"
   gcloud secrets list --project="$PROJECT" --filter="name~openclaw"
   kubectl get jobs,cronjobs -n "$NS"   # any scheduled backup jobs

5. Inspect the GCS workspace that backs all agent state, and confirm it is mounted inside the pod:

   BUCKET=$(gcloud storage buckets list --project="$PROJECT" \
     --filter="name~openclaw~storage" --format="value(name)" --limit=1)
   gcloud storage ls "gs://${BUCKET}/"
   kubectl exec -n "$NS" \
     deploy/$(kubectl get deploy -n "$NS" -o jsonpath='{.items[0].metadata.name}') \
     -- ls /data

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Task 4 — Observe: Logging & Monitoring [Manual]

1. Logs — from kubectl or the Logs Explorer:

   kubectl logs -n "$NS" deploy/"$(kubectl get deploy -n "$NS" -o jsonpath='{.items[0].metadata.name}')" --tail=50

   Logs Explorer filter: resource.type="k8s_container" AND resource.labels.namespace_name="<namespace>".

2. Monitoring — open the GKE / Kubernetes dashboards and review pod CPU and memory utilisation, restart counts, and request metrics. When the uptime check is enabled, review Monitoring → Uptime checks and Alerting → Policies.

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Task 5 — Troubleshoot & debug [Manual]

Durable techniques for the failure modes you are most likely to hit. These are platform-level diagnostics and do not change with OpenClaw releases.

• Pod not Ready / CrashLoopBackOff: inspect events and logs. The startup probe targets GET /health on port 8080 and allows roughly 3 minutes for GCS Fuse mount and Node.js startup (36 × 5 s attempts).

  kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
  kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container

• GCS Fuse mount failure: confirm the workspace bucket exists and that the pod's Workload Identity service account has Storage Object Admin on it.
• Anthropic API errors (401): confirm the anthropic-api-key secret has a valid version materialised in the namespace. Retrieve and verify it via Secret Manager.
• Gateway token errors: if clients receive auth failures after a secret rotation, the pods must be recycled (rolling restart) to pick up the new token value.
• Skills repository clone failure: an unreachable or non-existent skills_repo_url / skills_repo_ref puts the pod into CrashLoopBackOff. Check logs for skill-library entries and correct the URL/ref in the RAD platform.
• Pending pod / no external IP: check kubectl describe pod events for resource or quota issues, and confirm the LoadBalancer Service has an assigned IP.
• Image pull errors: confirm the image exists in Artifact Registry and the node service account can pull it.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas.

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Task 6 — Tear down [Automated]

On the Deployments page, open the deployment and click the Trash icon (Delete). Delete runs terraform destroy and is irreversible (the deployment record is retained for history). If a deployment is stuck and the RAD platform can no longer manage it (for example after manual changes that conflict with the Terraform state), use Purge instead — it removes the deployment from RAD's records without destroying the cloud resources (it makes RAD forget the project). This removes everything the module created — the Kubernetes workload and namespace, GCS workspace bucket, Secret Manager secrets, and Artifact Registry images. Resources owned by Services_GCP (the VPC, GKE cluster, shared registry) are managed separately and are not removed here.

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Summary

Task	Type	Outcome
1 — Deploy	Automated	Module builds the image, provisions the GCS workspace, stores secrets, and deploys the GKE workload
2 — Access & verify	Manual	Connect to the cluster; health check passes; gateway token retrieved
3 — Operate	Manual	Inspect workload, scale, update version, manage secrets/storage, inspect GCS workspace
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, GCS Fuse, Anthropic API, gateway token, skills-sync, scheduling, and image-pull issues
6 — Tear down	Automated	Delete (Trash) removes all module resources