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LangFlow on Cloud Run — Lab Guide

📖 Configuration 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 Cloud Run 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 Cloud Run 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.
  • Access and verify the running service.
  • Perform day-2 operations — inspect, scale, update, and manage secrets and backups.
  • Observe the service 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, Cloud SQL, Artifact Registry, and shared service accounts this module depends on).
  • A Google Cloud project with billing enabled.
  • gcloud CLI authenticated: gcloud auth login and gcloud auth application-default login.
  • 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]​

  1. In the RAD platform, open LangFlow (Cloud Run), 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.

  2. The platform provisions the Cloud Run service, 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 container start also runs LangFlow's own Alembic migrations plus component loading (2–4 minutes) before the service becomes healthy.

  3. When it completes, discover the resources with name-agnostic filters (so the commands keep working regardless of the deployment suffix):

    SERVICE=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
      --filter="metadata.name~langflow" --format="value(metadata.name)" --limit=1)
    SERVICE_URL=$(gcloud run services describe "$SERVICE" \
      --project="$PROJECT" --region="$REGION" --format="value(status.url)")
    echo "Service: $SERVICE"
    echo "URL:     $SERVICE_URL"

Task 2 — Access & verify [Manual]​

  1. 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" "$SERVICE_URL/health"   # expect 200

  2. Retrieve the auto-generated admin password from Secret Manager, then open $SERVICE_URL 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]​

  1. Inspect the service and its revisions (each deploy creates an immutable revision; traffic shifts to the newest healthy one):

    gcloud run services describe "$SERVICE" --project="$PROJECT" --region="$REGION"
    gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"

  2. Scale by changing the min/max instance inputs and clicking Update on the deployment details page — the module owns the service spec, so scaling is a configuration change, not a manual gcloud edit (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 instance splits that state and produces inconsistent behaviour. Set min_instance_count = 1 if you want to keep the canvas warm for interactive editing instead of scaling to zero.

  3. Update the application version by changing the version input in the RAD platform and applying it via Update; a new image builds and a new revision rolls out. Pin application_version explicitly rather than leaving it at latest for anything beyond a lab.

  4. Manage secrets and backups:

    gcloud secrets list --project="$PROJECT" --filter="name~langflow"
    gcloud run jobs list --project="$PROJECT" --region="$REGION"   # 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.

  5. 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]​

  1. Logs — from the CLI or the Logs Explorer:

    gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=50

    Logs Explorer filter: resource.type="cloud_run_revision" AND resource.labels.service_name="<service>".

  2. Monitoring — open the Cloud Run dashboard for the service and review request count, request latency (P50/P95/P99), instance count (scaling behaviour), and CPU / memory utilisation. Enable the module's uptime check for production use (disabled by default); confirm it is green under Monitoring → Uptime checks, and review 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.

  • Revision unhealthy / service won't serve: inspect the latest revision and its logs for startup errors, and confirm env vars and secrets resolved. The startup probe targets /health and allows a 60-second initial delay plus a 600-second failure window to cover component loading and first-boot Alembic migrations. 
    gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
    gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100
  • Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret exists, and the db-init job completed successfully. LangFlow composes LANGFLOW_DATABASE_URL at runtime from the injected DB_* variables over TCP with sslmode=require — do not set the DSN manually.
  • db-init job failed: list executions and read the failed one's logs: 
    gcloud run jobs executions list --job="${SERVICE}-db-init" \
      --project="$PROJECT" --region="$REGION"
  • Can't sign in / lost the admin password: re-fetch LANGFLOW_SUPERUSER_PASSWORD from Secret Manager (Task 2, step 2); it is not shown anywhere else.
  • Image build failed: review Cloud Build history for the failed build's log.
  • 403 / permission errors: verify the runtime service account's IAM roles.

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

Summary​

TaskTypeOutcome
1 — DeployAutomatedModule provisions Cloud Run, Cloud SQL (PostgreSQL 15), secrets, a data storage bucket, and runs DB init
2 — Access & verifyManualHealth check passes; sign in with the auto-generated admin password from Secret Manager
3 — OperateManualInspect revisions, scale (keep max=1), update version, manage secrets/backups, DB access
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — TroubleshootManualDiagnose revision, database, init-job, build, and IAM issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

By · Google Cloud certified: ACE · PCA · PCD · PDE · PSE · CDL

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