How to Install Langflow (OpenAI Visual Tool) on Ubuntu 24.04

Langflow (OpenAI Visual Tool) on Ubuntu 24.04 (Python + Docker + Nginx + SSL)

Langflow is an open-source visual programming interface for building and orchestrating Large Language Model (LLM) applications. It provides an intuitive, drag-and-drop environment for connecting AI components such as OpenAI models, LangChain, Hugging Face Transformers, and custom APIs, allowing developers to design workflows visually without writing complex backend code.

Running Langflow on Ubuntu 24.04 LTS (Noble Numbat) offers a stable, secure, and modern base for developing, testing, and deploying AI-driven applications. With Python 3.12, Docker Engine, and OpenSSL 3, Ubuntu 24.04 provides all the necessary components to host Langflow behind Nginx with SSL, ensuring both performance and security.

Architecture Overview

Layer	Component	Role
OS	Ubuntu 24.04 LTS	Long-term supported base for stable deployments
Runtime	Python 3.12 / Docker	Executes Langflow backend and containers
Framework	Langflow (LangChain UI)	Visual interface for LLM pipelines and agents
Backend API	FastAPI + LangChain	Handles data flow, LLM calls, and component integration
Frontend	React / Vite	Provides web-based drag-and-drop interface
Reverse Proxy	Nginx	TLS termination, static asset delivery, and routing
TLS	Let’s Encrypt / PKI	Provides HTTPS for secure access to the Langflow interface

Why Use Langflow?

Visual AI pipeline builder – connect prompts, tools, and APIs using blocks.
LangChain integration – design LLM workflows without code.
OpenAI support – easily connect GPT-3.5, GPT-4, or custom API keys.
Custom nodes and extensions – build reusable components for advanced use cases.
Local or remote hosting – deploy Langflow anywhere, from development machines to production servers.
Collaborative environment – share, export, and version AI workflows visually.

Langflow vs Other AI Development Platforms

Feature/Capability	Langflow (Self-hosted)	FlowiseAI (Self-hosted)	Dust.tt (Cloud)	OpenAI Playground (Cloud)
Hosting	Self-hosted / Cloud	Self-hosted	SaaS only	SaaS only
Interface type	Visual builder	Visual builder	Web UI	Web UI
LLM Integration	OpenAI, Anthropic, HF	OpenAI, HF	Proprietary	OpenAI only
Extensibility	High (custom nodes)	Moderate	Limited	None
Cost	Free, open-source	Free, open-source	Subscription	Pay-per-use

Langflow stands out as a powerful, open-source alternative for teams that want to build and iterate on AI applications locally with full customization and data control.

Security & Best Practices

Always run Langflow behind Nginx with HTTPS for secure access.
Store API keys (like OpenAI tokens) in environment variables or .env files, never in public scripts.
Restrict access using firewall (UFW/nftables) and basic authentication if public.
Keep Python dependencies and Docker images updated.
Use systemd services or Docker Compose for process supervision.
Backup project and configuration directories regularly.
Monitor application logs for API usage and performance bottlenecks.

Typical Use Cases

AI workflow prototyping – visually test prompt chains, tools, and retrieval models.
LLM orchestration – build conversational or autonomous agents using LangChain.
Internal AI tools – create no-code dashboards for data teams.
Education and research – teach LLM architectures and prompt engineering visually.
Enterprise deployments – host secure, local AI environments without sending data to the cloud.

Deploying Langflow on Ubuntu 24.04 gives you a powerful, flexible, and visual environment for building AI applications. With Docker or Python, Nginx, and SSL integration, you can create, test, and deploy complex LLM workflows — all within a secure, self-hosted ecosystem.

Step 1: Create a Server Instance on Shape.Host

Click Create → Instance.

Choose your preferred server location.

Shape.host instance form with the data center location selector highlighted.

Select a plan with at least 2 CPUs, 4 GB RAM, and 20 GB SSD.

Choose Ubuntu 24.04 (64-bit) as the operating system.

Shape.host instance form with the operating system image selector highlighted.

Click Create Instance.

Shape.host instance form with the Create Instance button highlighted.

Once deployed, note down your public IP address under Resources — you’ll use it to access Langflow later.

Shape.host Resources dashboard with the server IP address highlighted.

Step 2: Connect to Your Server

Use SSH to connect to your Shape.Host VPS:

ssh root@your_server_ip

Now you’re ready to install the dependencies.

Step 3: Update Packages and Install Prerequisites

Update the system repositories:

apt update

Terminal showing APT package management commands and output.

Install the required tools:

apt install curl wget git python3 python3-venv python3-pip

curl / wget – for downloading files
git – for version control (optional, but useful)
python3 / venv / pip – Python runtime, virtual environment manager, and package installer

Check Python version:

python3 --version

It should display something like Python 3.12.x (the default in Ubuntu 24.04).

Terminal showing python3 commands or output during Update Packages and Install Prerequisites.

Step 4: Create a Dedicated Directory

Create a directory for Langflow and navigate to it:

mkdir -p /opt/langflow
cd /opt/langflow

This keeps the installation organized in one central location.

Step 5: Set Up a Python Virtual Environment

Create a virtual environment (isolated Python space):

python3 -m venv venv

Activate the environment:

source venv/bin/activate

Once activated, your prompt will show (venv) at the start — this ensures all packages are installed locally to Langflow.

Terminal showing Django or Python virtual environment commands.

Step 6: Install Langflow and Dependencies

Upgrade pip first:

pip install --upgrade pip

Terminal showing pip commands or output during Install Langflow and Dependencies.

Then install Langflow:

pip install langflow

Terminal showing python commands or output during Install Langflow and Dependencies.

Install optional integrations (for LangChain and OpenAI):

pip install --upgrade langflow langchain openai

You can also add the Composio-LangChain connector:

pip install composio-langchain

Check for dependency issues:

pip check

If everything is OK, no errors will be shown.

Step 7: Run Langflow Manually

To test your setup:

langflow run --host 0.0.0.0 --port 7860

--host 0.0.0.0 makes Langflow accessible from any IP address.
--port 7860 defines the port.

Terminal displaying Langflow startup messages and its local access address.

Now open your browser and visit:

http://YOUR_SERVER_IP:7860

You should see the Langflow UI.

Press Ctrl + C to stop it before proceeding to create a background service.

Langflow welcome screen with account access options.

Step 8: Create a Systemd Service for Langflow

To make Langflow start automatically on boot, create a service file:

nano /etc/systemd/system/langflow.service

Paste the following configuration:

[Unit]
Description=Langflow Visual AI Tool
After=network.target

[Service]
# Run as root because you installed Langflow as root
User=root
Group=root

# Working directory where Langflow is installed
WorkingDirectory=/opt/langflow

# Start command
ExecStart=/opt/langflow/venv/bin/langflow run --host 0.0.0.0 --port 7860

# Restart if it crashes
Restart=always
RestartSec=5

# Environment variables
Environment="OPENAI_API_KEY=your_openai_key_here"
Environment="LANGFLOW_CORS_ORIGINS=http://your_server_ip:7860"
Environment="DO_NOT_TRACK=true"

[Install]
WantedBy=multi-user.target

Save and close (CTRL + O, ENTER, CTRL + X).

Terminal showing apt commands or output during Create a Systemd Service for Langflow.

Terminal showing a systemd service unit configuration.

Step 9: Enable and Start Langflow Service

Reload systemd to recognize the new service:

systemctl daemon-reload

Start Langflow:

systemctl start langflow

Enable it to start automatically on reboot:

systemctl enable langflow

Check status:

systemctl status langflow

If it says active (running), Langflow is now live.

Terminal showing systemd service management commands or output.

Step 10: Access Langflow

Open your web browser and navigate to:

http://YOUR_SERVER_IP:7860

or, using your example:

http://51.89.69.203:7860

You’ll now see the Langflow Visual Interface, ready to build AI pipelines visually using LangChain + OpenAI.

You’ve successfully installed Langflow on Ubuntu 24.04 with systemd integration and OpenAI support.
Your server now runs a visual AI builder that integrates with LangChain and OpenAI, ideal for prototyping and deploying intelligent workflows.

For optimal uptime and performance, host Langflow on a Shape.Host Linux SSD VPS, offering SSD-powered infrastructure and full root access — perfect for AI development environments.