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Deploy Langflow on Kubernetes

This guide demonstrates deploying Langflow on a Kubernetes cluster.

Two charts are available at the Langflow Helm Charts repository:

  • Deploy the Langflow IDE for the complete Langflow development environment.
  • Deploy the Langflow runtime to deploy a standalone Langflow application in a more secure and stable environment.

Deploy the Langflow IDE​

The Langflow IDE deployment is a complete environment for developers to create, test, and debug their flows. It includes both the API and the UI.

The langflow-ide Helm chart is available in the Langflow Helm Charts repository.

Prerequisites​

Prepare a Kubernetes cluster​

This example uses Minikube, but you can use any Kubernetes cluster.

  1. Create a Kubernetes cluster on Minikube.


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    minikube start

  2. Set kubectl to use Minikube.


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    kubectl config use-context minikube

Install the Langflow IDE Helm chart​

  1. Add the repository to Helm and update it.


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    helm repo add langflow https://langflow-ai.github.io/langflow-helm-charts
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    helm repo update

  2. Install Langflow with the default options in the langflow namespace.


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    helm install langflow-ide langflow/langflow-ide -n langflow --create-namespace

  3. Check the status of the pods


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    kubectl get pods -n langflow


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    NAME READY STATUS RESTARTS AGE
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    langflow-0 1/1 Running 0 33s
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    langflow-frontend-5d9c558dbb-g7tc9 1/1 Running 0 38s

Configure port forwarding to access Langflow​

Enable local port forwarding to access Langflow from your local machine.

  1. To make the Langflow API accessible from your local machine at port 7860:

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kubectl port-forward -n langflow svc/langflow-service-backend 7860:7860

  1. To make the Langflow UI accessible from your local machine at port 8080:

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kubectl port-forward -n langflow svc/langflow-service 8080:8080

Now you can access:

  • The Langflow API at http://localhost:7860
  • The Langflow UI at http://localhost:8080

Configure the Langflow version​

Langflow is deployed with the latest version by default.

To specify a different Langflow version, set the langflow.backend.image.tag and langflow.frontend.image.tag values in the values.yaml file.


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langflow:
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backend:
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image:
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tag: "1.0.0a59"
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frontend:
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image:
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tag: "1.0.0a59"

Configure external storage​

By default, the chart deploys a SQLite database stored in a local persistent disk. If you want to use an external PostgreSQL database, you can configure it in two ways:

  • Use the built-in PostgreSQL chart:

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postgresql:
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enabled: true
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auth:
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username: "langflow"
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password: "langflow-postgres"
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database: "langflow-db"

  • Use an external database:

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postgresql:
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enabled: false
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langflow:
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backend:
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externalDatabase:
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enabled: true
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driver:
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value: "postgresql"
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port:
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value: "5432"
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user:
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value: "langflow"
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password:
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valueFrom:
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secretKeyRef:
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key: "password"
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name: "your-secret-name"
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database:
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value: "langflow-db"
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sqlite:
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enabled: false

Configure scaling​

Scale the number of replicas and resources for both frontend and backend services:


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langflow:
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backend:
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replicaCount: 1
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resources:
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requests:
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cpu: 0.5
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memory: 1Gi
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# limits:
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# cpu: 0.5
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# memory: 1Gi
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frontend:
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enabled: true
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replicaCount: 1
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resources:
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requests:
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cpu: 0.3
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memory: 512Mi
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# limits:
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# cpu: 0.3
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# memory: 512Mi

Deploy the Langflow runtime​

The runtime chart is tailored for deploying applications in a production environment. It is focused on stability, performance, isolation, and security to ensure that applications run reliably and efficiently.

The langflow-runtime Helm chart is available in the Langflow Helm Charts repository.

Prerequisites​

Install the Langflow runtime Helm chart​

  1. Add the repository to Helm.

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helm repo add langflow https://langflow-ai.github.io/langflow-helm-charts
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helm repo update

  1. Install the Langflow app with the default options in the langflow namespace.

If you have a created a custom image with packaged flows, you can deploy Langflow by overriding the default values.yaml file with the --set flag.

  • Use a custom image with bundled flows:

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helm install my-langflow-app langflow/langflow-runtime -n langflow --create-namespace --set image.repository=myuser/langflow-hello-world --set image.tag=1.0.0

  • Alternatively, install the chart and download the flows from a URL with the --set flag:

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helm install my-langflow-app-with-flow langflow/langflow-runtime \
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-n langflow \
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--create-namespace \
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--set 'downloadFlows.flows[0].url=https://raw.githubusercontent.com/langflow-ai/langflow/dev/tests/data/basic_example.json'

important

You may need to escape the square brackets in this command if you are using a shell that requires it:


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helm install my-langflow-app-with-flow langflow/langflow-runtime \
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-n langflow \
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--create-namespace \
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--set 'downloadFlows.flows\[0\].url=https://raw.githubusercontent.com/langflow-ai/langflow/dev/tests/data/basic_example.json'

  1. Check the status of the pods.

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kubectl get pods -n langflow

Access the Langflow app API​

  1. Get your service name.

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kubectl get svc -n langflow

The service name is your release name followed by -langflow-runtime. For example, if you used helm install my-langflow-app-with-flow the service name is my-langflow-app-with-flow-langflow-runtime.

  1. Enable port forwarding to access Langflow from your local machine:

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kubectl port-forward -n langflow svc/my-langflow-app-with-flow-langflow-runtime 7860:7860

  1. Confirm you can access the API at http://localhost:7860/api/v1/flows/ and view a list of flows.

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curl -v http://localhost:7860/api/v1/flows/

  1. Execute the packaged flow.

The following command gets the first flow ID from the flows list and runs the flow.


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# Get flow ID
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id=$(curl -s "http://localhost:7860/api/v1/flows/" | jq -r '.[0].id')
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# Run flow
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curl -X POST \
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"http://localhost:7860/api/v1/run/$id?stream=false" \
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-H 'Content-Type: application/json' \
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-d '{
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"input_value": "Hello!",
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"output_type": "chat",
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"input_type": "chat"
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}'

Configure secrets​

To inject secrets and Langflow global variables, use the secrets and env sections in the values.yaml file.

For example, the example flow JSON uses a global variable that is a secret. When you export the flow as JSON, it's recommended to not include the secret.

Instead, when importing the flow in the Langflow runtime, you can set the global variable in one of the following ways:


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env:
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- name: openai_key_var
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valueFrom:
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secretKeyRef:
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name: openai-key
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key: openai-key

Or directly in the values file (not recommended for secret values):


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env:
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- name: openai_key_var
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value: "sk-...."

Configure the log level​

Set the log level and other Langflow configurations in the values.yaml file.


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env:
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- name: LANGFLOW_LOG_LEVEL
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value: "INFO"

Configure scaling​

To scale the number of replicas for the Langflow appplication, change the replicaCount value in the values.yaml file.


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replicaCount: 3

To scale the application vertically by increasing the resources for the pods, change the resources values in the values.yaml file.


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resources:
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requests:
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memory: "2Gi"
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cpu: "1000m"

Deploy Langflow on AWS EKS, Google GKE, or Azure AKS and other examples​

For more information, see the Langflow Helm Charts repository.

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