Deploy Langflow on Sealos

Langflow is a low-code visual builder for RAG, agents, and AI applications. Build, test, and share AI workflows through a browser-based interface.

Launch a private Langflow AI workspace with one-click deployment.Keep models, prompts, and workflow data in your own Sealos environment.Scale GPU or CPU resources as your AI workload grows.Use an open-source template without managing Kubernetes YAML.

Official Website GitHub README

sealos.io

Why deploy on Sealos

Sealos makes deploying any app effortless, secure, and production-ready. From one-click launch to ongoing operations, we handle the heavy lifting so you can focus on what matters.

One-Click Deploy

Compute

Networking

Storage

Security

Observability

Your Application is Live

One-Click Deployment

Deploy any app template in seconds. No compose setup, manual configure, and go live.

Managed Kubernetes Reliability

Built on Sealos Managed Kubernetes for high availability, auto-scaling, and self-healing by default.

Automatic HTTPS & Security

Every deployment includes a secure domain with automatic SSL. We handle certificates and text protection for you.

Persistent Storage

Attach persistent volumes with ease. Your data stays safe, durable, and always accessible.

Scale when needed

Adjust resources as your app grows, no downtime required.

You Get the Whole Stack

Sealos provisions and connects every resource your application needs. Everything is ready to use.

One-click, fully provisionedSecure by defaultProduction ready

App Service

Your application is running and ready to serve requests.

Public HTTPS URL

Secure, global endpoint to access your application.

Database

Managed database instance for your application.

Persistent Volume

Durable storage for uploads and application data.

Environment Variables

Configuration and secrets injected securely into your app.

Logs & Metrics

Centralized logs and basic metrics are enabled.

README

README.md

Deploy and Host Langflow on Sealos

Langflow is a visual low-code builder for RAG, agentic workflows, and AI applications. This template deploys Langflow 1.9.5 as a single persistent service on Sealos Cloud, with optional PostgreSQL for production storage.

About Hosting Langflow

Langflow provides a browser-based canvas for building AI workflows from reusable components. You can connect language models, tools, vector stores, prompt chains, and agents, then test and iterate flows directly in the web interface.

This Sealos template runs the official langflowai/langflow container, exposes it through a managed HTTPS endpoint, and stores Langflow data on persistent volumes. Auto login is disabled for public deployments, so users sign in with the initial superuser credentials configured during deployment.

When enable_database is turned on, the template provisions PostgreSQL 16 through KubeBlocks and initializes a dedicated langflow database. If it is left off, Langflow uses the built-in SQLite database stored on the persistent data volume.

Common Use Cases

RAG Prototyping: Build retrieval workflows that combine documents, embeddings, vector databases, and LLM responses.
Agent Workflow Design: Compose multi-step agent workflows visually before moving them into production systems.
AI App Experiments: Test prompts, model providers, tools, and data connectors from one interactive workspace.
Team Demos and Education: Share a hosted Langflow instance for workshops, demos, and internal AI enablement.

Dependencies for Langflow Hosting

The Sealos template includes the Langflow application container, persistent storage for /app/data and /app/flows, a managed Ingress endpoint, and optional PostgreSQL.

Deployment Dependencies

Langflow Documentation - Official Langflow documentation
Langflow GitHub Repository - Source code and release notes
Langflow API Keys and Authentication - Authentication and API key behavior
Sealos App Store - Langflow template page

Implementation Details

Architecture Components:

This template deploys the following resources:

Langflow Service: Official Langflow 1.9.5 container serving the web UI and API on port 7860.
Persistent Storage: Two persistent volumes for application data and exported flow files.
PostgreSQL (Optional): KubeBlocks PostgreSQL 16.4.0 with an idempotent initialization Job when enable_database is set to true.
Ingress and App Entry: Sealos-managed HTTPS routing and a dashboard link for the deployed instance.

Configuration:

admin_username sets the initial Langflow superuser username. The default is admin.
admin_password sets the initial superuser password and is required for sign-in.
enable_database switches storage from persistent SQLite to PostgreSQL for larger or production-oriented deployments.
LANGFLOW_AUTO_LOGIN is set to false, so the visual editor requires sign-in instead of anonymous superuser access.
LANGFLOW_SECRET_KEY is generated per deployment for encryption-sensitive Langflow internals.

License Information:

Langflow is released under the MIT License. This Sealos template is provided under the license terms of the templates repository.

Why Deploy Langflow on Sealos?

Sealos is an AI-assisted Cloud Operating System built on Kubernetes that unifies deployment, networking, storage, and lifecycle operations. By deploying Langflow on Sealos, you get:

One-Click Deployment: Launch Langflow from the App Store without writing Kubernetes YAML.
Managed HTTPS Access: Each deployment receives a public HTTPS endpoint automatically.
Persistent Storage Included: Langflow data and flows survive container restarts and upgrades.
Optional Managed Database: Enable PostgreSQL when you need a managed database backend.
Canvas + AI Operations: Adjust resources and configuration later through Canvas, resource cards, or the AI dialog.
Pay-As-You-Go Resources: Start with the included resource profile and scale when your workloads need more capacity.

Deployment Guide

Open the Langflow template and click Deploy Now.
Configure the deployment parameters:
- admin_username: Initial superuser username, default admin.
- admin_password: Initial superuser password used to sign in after deployment.
- enable_database: Set to true to use PostgreSQL, or keep false to use persistent SQLite.
Wait for deployment to complete. Langflow can take several minutes on first start because the container initializes components and the web server.
Access the application from the Sealos-provided URL and sign in with the configured admin_username and admin_password.
After sign-in, create your first flow from the welcome screen or upload an existing flow JSON file.

Configuration

After deployment, you can manage Langflow through:

Langflow UI: Create flows, manage API keys, configure model providers, and run workflows.
Sealos AI Dialog: Describe configuration or scaling changes and let Sealos apply them.
Resource Cards: Open the StatefulSet, Service, Ingress, or PostgreSQL cards in Canvas to inspect or adjust runtime settings.
Environment Variables: Update Langflow settings such as authentication, database, or feature flags from the workload resource card.

For public deployments, keep auto login disabled and use a strong superuser password. Configure provider API keys inside Langflow or through Sealos-managed environment variables when needed.

Scaling

Langflow is deployed as a single persistent StatefulSet. To scale resources:

Open the Canvas for your deployment.
Click the Langflow StatefulSet resource card.
Increase CPU or memory if you run large flows, load many components, or use memory-heavy connectors.
Apply the change and wait for the pod to restart and become ready.

The template uses a cold-start validated profile of 2 CPU and 4Gi memory. Lower memory settings may fail during initialization.

Troubleshooting

The login page is shown after deployment

This is expected. Auto login is disabled, so use the admin_username and admin_password configured during deployment.

The pod restarts during startup

Langflow loads many Python components during cold start. If you manually reduce memory and the pod is OOMKilled, restore the template memory setting or choose a larger Sealos memory tier.