Deploying Langflow: An Open-Source Visual Framework for Building AI Applications

Langflow is an open-source, low-code visual framework for building artificial intelligence (AI) agents, workflows, and retrieval-augmented generation (RAG) applications. Developers use its visual builder to assemble large language model (LLM) pipelines from prebuilt components and test them in an interactive Playground, and finished flows run as API endpoints or Model Context Protocol (MCP) servers without extra boilerplate code. This guide walks through self-hosting a production-ready Langflow instance on a Linux server with Docker Compose, covering PostgreSQL persistence, Traefik reverse proxying with automatic HTTPS certificates, authentication for the visual editor, and validation of the deployment through a RAG chatbot that answers questions from an uploaded document. By the end, you'll have a secured Langflow deployment running behind HTTPS with a working RAG chatbot proving that ingestion, retrieval, and generation all work end to end.

Before you begin, you need a Linux-based server with at least 2 CPU cores and 4 GB of RAM as a non-root user with sudo privileges, Docker and Docker Compose installed, a domain A record pointing to the server's public IP address (for example, langflow.example.com), and an API key from a supported LLM provider — this deployment uses OpenAI models for embeddings and chat responses.

Langflow reads its runtime configuration from environment variables, so a dedicated project directory with a .env file keeps credentials out of the Compose manifest.

1. Create the project directory and switch into it:

$ mkdir ~/langflow && cd ~/langflow

2. Generate a Langflow secret key and write it to the environment file:

$ python3 -c "from secrets import token_urlsafe; print(f'LANGFLOW_SECRET_KEY={token_urlsafe(32)}')" >> .env

Langflow encrypts stored credentials with this Fernet key. Without an explicit key, Langflow generates a random one at startup and encrypted values become unreadable after a restart.

3. Verify that the file contains the key without displaying its value:

$ grep -c "LANGFLOW_SECRET_KEY" .env

Output:

1

4. Open the .env file with a text editor such as nano:

$ nano .env

5. Add the following variables below the existing LANGFLOW_SECRET_KEY line, replacing every placeholder with your own values:

LANGFLOW_HOSTNAME=langflow.example.com
LETSENCRYPT_EMAIL=admin@example.com

POSTGRES_USER=langflow
POSTGRES_PASSWORD=DATABASE_PASSWORD
POSTGRES_DB=langflow

LANGFLOW_CONFIG_DIR=/app/langflow
LANGFLOW_KNOWLEDGE_BASES_DIR=/app/langflow/knowledge_bases

LANGFLOW_AUTO_LOGIN=False
LANGFLOW_SUPERUSER=administrator
LANGFLOW_SUPERUSER_PASSWORD=ADMIN_PASSWORD
LANGFLOW_NEW_USER_IS_ACTIVE=False
LANGFLOW_ENABLE_SUPERUSER_CLI=False

OPENAI_API_KEY=OPENAI_API_KEY

LANGFLOW_HOSTNAME and LETSENCRYPT_EMAIL supply the domain for the Traefik routing rule and the contact address for certificate expiry notices. The POSTGRES_* variables initialize the database container on first boot and are reused in the Langflow connection string — use only letters and numbers in the password, because symbols require %-encoding and $ conflicts with Compose interpolation. LANGFLOW_CONFIG_DIR and LANGFLOW_KNOWLEDGE_BASES_DIR place application data and knowledge base vectors on the same volume-mapped path; without the second variable, Langflow writes knowledge bases outside that volume and a container replacement deletes your vector data. LANGFLOW_AUTO_LOGIN=False disables anonymous access, LANGFLOW_SUPERUSER/ LANGFLOW_SUPERUSER_PASSWORD define the administrator account Langflow creates at startup, LANGFLOW_NEW_USER_IS_ACTIVE=False keeps new accounts inactive until approved, and LANGFLOW_ENABLE_SUPERUSER_CLI=False blocks superuser creation from the command line. OPENAI_API_KEY supplies the LLM provider credential, which Langflow stores as an encrypted global variable.

6. Restrict the environment file so only its owner can read or modify it:

$ chmod 600 .env

The stack runs three services. Traefik terminates HTTPS, Langflow serves the application on internal port 7860, and PostgreSQL stores flows, users, and settings. Langflow joins the proxy network with Traefik and the internal network with PostgreSQL, so the database stays unreachable from outside.

1. Create the docker-compose.yml file in the project directory:

$ nano docker-compose.yml

2. Add the following service definitions to the file:

services:
  traefik:
    image: traefik:v3.7
    restart: unless-stopped
    command:
      - --providers.docker=true
      - --providers.docker.exposedbydefault=false
      - --providers.docker.network=proxy
      - --entryPoints.web.address=:80
      - --entryPoints.websecure.address=:443
      - --entryPoints.websecure.http.tls=true
      - --entryPoints.web.http.redirections.entryPoint.to=websecure
      - --entryPoints.web.http.redirections.entryPoint.scheme=https
      - --certificatesresolvers.le.acme.email=${LETSENCRYPT_EMAIL}
      - --certificatesresolvers.le.acme.storage=/letsencrypt/acme.json
      - --certificatesresolvers.le.acme.httpchallenge.entrypoint=web
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock:ro
      - ./letsencrypt:/letsencrypt
    networks:
      - proxy

  langflow:
    image: langflowai/langflow:1.11.3
    restart: unless-stopped
    depends_on:
      postgres:
        condition: service_healthy
    environment:
      - LANGFLOW_DATABASE_URL=postgresql://${POSTGRES_USER}:${POSTGRES_PASSWORD}@postgres:5432/${POSTGRES_DB}
      - LANGFLOW_CONFIG_DIR=${LANGFLOW_CONFIG_DIR}
      - LANGFLOW_KNOWLEDGE_BASES_DIR=${LANGFLOW_KNOWLEDGE_BASES_DIR}
      - LANGFLOW_AUTO_LOGIN=${LANGFLOW_AUTO_LOGIN}
      - LANGFLOW_SUPERUSER=${LANGFLOW_SUPERUSER}
      - LANGFLOW_SUPERUSER_PASSWORD=${LANGFLOW_SUPERUSER_PASSWORD}
      - LANGFLOW_SECRET_KEY=${LANGFLOW_SECRET_KEY}
      - LANGFLOW_NEW_USER_IS_ACTIVE=${LANGFLOW_NEW_USER_IS_ACTIVE}
      - LANGFLOW_ENABLE_SUPERUSER_CLI=${LANGFLOW_ENABLE_SUPERUSER_CLI}
      - OPENAI_API_KEY=${OPENAI_API_KEY}
    volumes:
      - langflow-data:/app/langflow
    networks:
      - proxy
      - internal
    labels:
      - traefik.enable=true
      - traefik.http.routers.langflow.rule=Host(`${LANGFLOW_HOSTNAME}`)
      - traefik.http.routers.langflow.entrypoints=websecure
      - traefik.http.routers.langflow.tls.certresolver=le
      - traefik.http.services.langflow.loadbalancer.server.port=7860

  postgres:
    image: postgres:16-trixie
    restart: unless-stopped
    environment:
      - POSTGRES_USER=${POSTGRES_USER}
      - POSTGRES_PASSWORD=${POSTGRES_PASSWORD}
      - POSTGRES_DB=${POSTGRES_DB}
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER} -d ${POSTGRES_DB}"]
      interval: 5s
      timeout: 5s
      retries: 10
    volumes:
      - langflow-postgres:/var/lib/postgresql/data
    networks:
      - internal
networks:
  proxy:
    name: proxy
  internal:

volumes:
  langflow-data:
  langflow-postgres:

The traefik service publishes ports 80 and 443, discovers only explicitly labeled containers through the read-only Docker socket, and registers a certificate resolver named le that completes the ACME challenge on port 80 and redirects plain HTTP to HTTPS. The langflow service pins the langflowai/langflow:1.11.3 image; the Traefik labels route your domain to port 7860 inside the container, and the langflow-data volume persists LANGFLOW_CONFIG_DIR across restarts. The postgres service pins postgres:16-trixie, joins only the internal network, and its pg_isready health check gates the Langflow start.

3. Start the stack in detached mode:

$ docker compose up -d

4. Verify that all containers are running:

$ docker compose ps

The output displays three running containers, with Traefik listening on ports 80 and 443 and PostgreSQL reporting a healthy status.

5. Check the Langflow logs to verify that the application started:

$ docker compose logs -f langflow

The first start takes a few minutes because Langflow runs its database migrations against PostgreSQL. The log stream ends with a startup banner when the application is ready.

Open Langflow → http://localhost:7860

Press Ctrl+C to stop following the logs. The localhost address applies inside the container only, and Traefik forwards your domain traffic to the same listener.

The stack now runs behind HTTPS, so the remaining configuration happens in the browser.

https://langflow.example.com. Traefik requests a Let's Encrypt certificate after the stack starts — if the browser shows a certificate warning, wait a minute and reload. Because automatic login is off, Langflow redirects you to the /login page.LANGFLOW_SUPERUSER and LANGFLOW_SUPERUSER_PASSWORD. The Langflow OPENAI_API_KEY variable at startup. Enable the models you plan to use under A RAG chatbot answers questions from your own documents instead of relying only on the model's training data. Langflow ships a Vector Store RAG template that pairs a retrieval flow with a knowledge base, which chunks a document, embeds it, and stores the vectors locally. A grounded answer in the Playground proves that ingestion, retrieval, and generation all work on the deployed stack.

langflow_demo, select an OpenAI embedding model, and keep {question} variable. {context}. For the full guide with additional tips, visit the original article on Vultr Docs.