LangFlow on GKE Autopilot — Lab Guide

Overview

Estimated time: 45–90 minutes

LangFlow is an open-source, low-code visual builder for AI agents and workflows, built on LangChain — you assemble language-model chains, RAG pipelines, and agents by dragging and wiring components on a canvas, then expose them as APIs. This lab takes you through the full operational lifecycle of the LangFlow 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 LangFlow 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, Cloud SQL, 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

Task 1 — Deploy the module [Automated]

Click Deploy in the RAD platform top navigation, open LangFlow (GKE) from the Platform Modules list to start configuration, set project_id, and review the inputs. Configure only what 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.
The platform deploys the workload into the GKE Autopilot cluster, provisions a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (LANGFLOW_SECRET_KEY, LANGFLOW_SUPERUSER_PASSWORD, and the database password), a Cloud Storage data bucket, builds the container image, and runs a one-shot database-initialisation job that creates the application role, database, and grants. First deploys take roughly 20–35 minutes (Cloud SQL creation dominates), and the first pod start also runs LangFlow's own Alembic migrations plus component loading before it becomes Ready.

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 langflow | head -1 | cut -d/ -f2)
echo "Cluster: $CLUSTER   Namespace: $NS"
kubectl get all -n "$NS"

Task 2 — Access & verify [Manual]

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"

Confirm the service is healthy. LangFlow exposes a public liveness endpoint that returns 200 once the server is fully up (after component loading and Alembic migrations):
```
curl -s -o /dev/null -w "%{http_code}\n" "http://${EXTERNAL_IP}/health"   # expect 200
```
Retrieve the auto-generated admin password from Secret Manager, then open http://${EXTERNAL_IP} in a browser and sign in as admin (or the value you set for langflow_username) with that password — LangFlow has authentication turned on by default (LANGFLOW_AUTO_LOGIN = "false"), so there is no open sign-up step:
```
SECRET=$(gcloud secrets list --project="$PROJECT" \
  --filter="name~langflow AND name~superuser" --format="value(name)" --limit=1)
gcloud secrets versions access latest --secret="$SECRET" --project="$PROJECT"
```

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

Inspect the workload — deployment, pods, and the horizontal autoscaler:
```
kubectl get deploy,pods,hpa -n "$NS"
kubectl describe deploy -n "$NS"
```
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). Keep max_instance_count = 1: LangFlow holds in-process session and flow-editor state, so running more than one replica splits that state and produces inconsistent behaviour. GKE does not scale to zero, so min_instance_count = 1 is the default and floor. Session affinity (ClientIP) is set by default to keep the flow editor sticky to one pod.
Update the application version by changing the version input in the RAD platform and applying it via Update; a new image builds and a rolling update replaces the pods. Pin application_version explicitly rather than leaving it at latest for anything beyond a lab.
Manage secrets, storage, and jobs:
```
kubectl get secrets -n "$NS"
gcloud secrets list --project="$PROJECT" --filter="name~langflow"
kubectl get jobs -n "$NS"          # db-init job
```
Never rotate LANGFLOW_SECRET_KEY after first boot — it encrypts every stored credential embedded in a flow, and rotating it makes them permanently undecryptable.

Open a database session for inspection or maintenance:

INSTANCE=$(gcloud sql instances list --project="$PROJECT" --filter="name~langflow" --format="value(name)" --limit=1)
gcloud sql connect "$INSTANCE" --user=langflowuser --project="$PROJECT"

Task 4 — Observe: Logging & Monitoring [Manual]

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>".
Monitoring — open the GKE / Kubernetes dashboards and review pod CPU and memory utilisation, restart counts, and request metrics. The module can provision an uptime check (disabled by default); enable it for production use, review Monitoring → Uptime checks and Alerting → Policies.

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 LangFlow releases.

Pod not Ready / CrashLoopBackOff: inspect events and logs. The liveness and startup probes target /health; allow time on first boot for component loading and Alembic migrations before the pod becomes Ready.
```
kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container
```
Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret materialised into the namespace, and the db-init job completed. LangFlow connects via the Cloud SQL Auth Proxy sidecar on 127.0.0.1, composing LANGFLOW_DATABASE_URL with sslmode=disable (the proxy terminates TLS) — do not set the DSN manually.

db-init job failed: inspect the job and its pod logs:

kubectl get jobs -n "$NS"
kubectl logs -n "$NS" job/<job-name>

Can't sign in / lost the admin password: re-fetch LANGFLOW_SUPERUSER_PASSWORD from Secret Manager (Task 2, step 3); it is not shown anywhere else.
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 (including the critical rule never to rotate LANGFLOW_SECRET_KEY after first boot, and why max_instance_count must stay at 1).

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, Cloud SQL database, Secret Manager secrets, GCS buckets, and Artifact Registry images. Resources owned by Services_GCP (the VPC, GKE cluster, shared Cloud SQL, registry) are managed separately and are not removed here.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module deploys the GKE workload, Cloud SQL (PostgreSQL 15), secrets, a `data` storage bucket, and runs DB init
2 — Access & verify	Manual	Connect to the cluster; health check passes; sign in with the auto-generated admin password from Secret Manager
3 — Operate	Manual	Inspect workload, scale (keep max=1), update version, manage secrets/storage, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, database, init-job, scheduling, and image-pull issues
6 — Tear down	Automated	Delete (Trash) removes all module resources

By Dr Shiyghan Emmanuel Navti · Google Cloud certified: ACE · PCA · PCD · PDE · PSE · CDL

Overview​

Objectives​

Prerequisites​

Task 1 — Deploy the module [Automated]​

Task 2 — Access & verify [Manual]​

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

Task 4 — Observe: Logging & Monitoring [Manual]​

Task 5 — Troubleshoot & debug [Manual]​

Task 6 — Tear down [Automated]​

Summary​