Langflow on Ubuntu 24.04 on Azure User Guide

Overview

Langflow is the open-source, low-code visual builder for AI agents and retrieval-augmented-generation (RAG) applications. You design flows by dragging and connecting components on a canvas, test them interactively, and serve them as APIs. The cloudimg image installs Langflow 1.9 in a Python virtual environment at /opt/langflow, fronts it with an nginx reverse proxy on TCP 80, and generates a unique administrator password and credential-encryption key on the first boot of every VM. Backed by 24/7 cloudimg support.

What is included:

• Langflow 1.9 in a Python venv at /opt/langflow
• nginx reverse proxy on :80 in front of the Langflow service (bound to loopback)
• A dedicated Azure data disk at /var/lib/langflow holding the SQLite database, saved flows, uploads and the credential-encryption key — separate from the OS disk and re-provisioned with every VM
• Per-VM administrator password + Fernet LANGFLOW_SECRET_KEY generated at first boot, written to /root/langflow-credentials.txt
• langflow.service + nginx.service as systemd units, enabled and active
• 24/7 cloudimg support

Prerequisites

An active Azure subscription, an SSH key pair, and a VNet + subnet in the target region. Standard_B2ms (2 vCPU / 8 GiB RAM) is a good starting point; scale up for heavier flows or more concurrent users. NSG inbound: allow 22/tcp from your management network and 80/tcp from the clients that need the Langflow UI (front port 80 with TLS for public exposure — see Enabling HTTPS).

Step 1 — Deploy from the Azure Marketplace

Sign in to the Azure Portal, choose Create a resource, search the Marketplace for Langflow by cloudimg, and select Create. On Basics pick your subscription, resource group, region, and size (Standard_B2ms or larger); under Administrator account choose SSH public key and paste your key; under Inbound port rules allow SSH (22) and HTTP (80). Review the dedicated data disk on the Disks tab, then Review + create → Create.

Step 2 — Deploy from the Azure CLI

az vm create \
  --resource-group <your-rg> \
  --name langflow \
  --image <marketplace-image-urn> \
  --size Standard_B2ms \
  --admin-username azureuser \
  --ssh-key-values ~/.ssh/id_ed25519.pub \
  --vnet-name <your-vnet> --subnet <your-subnet> \
  --public-ip-sku Standard

az vm open-port --resource-group <your-rg> --name langflow --port 80 --priority 1010

Step 3 — Connect to your VM

ssh azureuser@<vm-public-ip>

Step 4 — Confirm the services are running

systemctl is-active langflow.service nginx.service
ss -tln | grep -E ':80 |:7860 '
curl -s http://127.0.0.1/health

Both services report active, nginx listens on port 80, the Langflow service listens on loopback 7860, and the health endpoint returns OK.

Step 5 — Retrieve your per-VM credentials

The administrator password is generated uniquely on the first boot of your VM and written to a root-only file:

sudo cat /root/langflow-credentials.txt

The username is admin; the password is the LANGFLOW_SUPERUSER_PASSWORD value. Keep this file safe and change the password after first sign-in for production use.

Step 6 — Open Langflow and sign in

Browse to http://<vm-public-ip>/ and sign in with username admin and the password from Step 5. You land on the Langflow workspace where you can create and manage flows.

Step 7 — Build your first flow

In the UI choose New Flow, start from a template or a blank canvas, and drag components (inputs, prompts, models, retrievers, outputs) onto the canvas, connecting them to form a pipeline. Use the Playground to test the flow interactively.

Step 8 — Connect a model provider and serve your flow

Add your model-provider API key to the relevant component in the UI (for example an OpenAI or Anthropic component). You can confirm the login API works end-to-end from the shell:

PASS=$(sudo grep '^LANGFLOW_SUPERUSER_PASSWORD=' /root/langflow-credentials.txt | cut -d= -f2-)
curl -s -X POST http://127.0.0.1/api/v1/login \
  -H 'Content-Type: application/x-www-form-urlencoded' \
  --data-urlencode 'username=admin' --data-urlencode "password=$PASS" \
  | python3 -c "import sys,json; print('token_type:', json.load(sys.stdin)['token_type'])"

Once a flow is built, click API in the UI to get the endpoint and a ready-made curl/Python snippet to call it.

Step 9 — Confirm the runtime

/opt/langflow/venv/bin/langflow --version

Enabling HTTPS

For production, terminate TLS at nginx with a real domain pointed at the VM's public IP. Install certbot and request a certificate (replace the domain):

sudo apt-get update && sudo apt-get install -y certbot python3-certbot-nginx
sudo certbot --nginx -d your-domain.example.com

certbot updates the nginx configuration and sets up automatic renewal.

Backup and maintenance

All Langflow state — the SQLite database, saved flows, uploads and the encryption key — lives on the dedicated data disk at /var/lib/langflow. Snapshot that disk in Azure to back up your flows, and keep the OS patched with sudo apt update && sudo apt upgrade. The Langflow service restarts cleanly with sudo systemctl restart langflow.

Support

This image is backed by 24/7 cloudimg support. Contact us by email and chat for help with deployment, flow design, model-provider integration, scaling, TLS and backups.

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