--- title: "Deploy Flowise" description: "[Jul'26] Flowise + persistent storage for visual AI agents & LLM workflows." category: "AI/ML" url: https://railway.com/deploy/flowise-ai --- # Deploy Flowise [Jul'26] Flowise + persistent storage for visual AI agents & LLM workflows. **[Deploy Flowise on Railway](https://railway.com/template/flowise-ai)** - **Creator:** codestorm - **Category:** AI/ML - **Total deploys:** 18 ## Template content ### Flowise AI https://i.imgur.com/PCQwxlP.png - **Source:** FlowiseAI/Flowise - **Public domain:** Yes ## Documentation ![Railway](https://img.shields.io/badge/Railway-Supported-blue?logo=railway) # Deploy and Host Flowise on Railway Flowise is an open-source, low-code platform for building AI agents, chatbots, and LLM workflows visually. It provides a drag-and-drop web UI for connecting language models, tools, memory, retrievers, vector stores, APIs, and custom logic. This template deploys Flowise from the official GitHub repository with public web access, persistent storage, and configurable authentication. ![](https://i.imgur.com/hbOkfpX.png) ## About Hosting Flowise Deploying Flowise on Railway from the official GitHub repository builds the application using the Dockerfile included in `FlowiseAI/Flowise`. Railway handles infrastructure, networking, deployments, and scaling, while Flowise provides a browser-based UI for building and managing AI workflows. The Flowise service listens on port `3000`, and Railway can expose the app through a public Railway domain or custom domain. A Railway Volume should be mounted at `/root/.flowise` so Flowise data, API keys, credentials, logs, and configuration persist across restarts and redeployments. By default, this template sets the Flowise username to `admin` and password to `admin123`; users can change both values before or after deployment from Railway Variables. > Initial deployment may take several minutes because Railway builds Flowise from the official GitHub repository, installs dependencies, and compiles the application before starting the web UI. ## Common Use Cases * Building AI agents, chatbots, and LLM workflows with a visual drag-and-drop web UI * Prototyping RAG pipelines using document loaders, embeddings, and vector stores * Creating internal automation tools that connect LLMs, APIs, memory, and custom logic * Testing and deploying LangChain-style workflows without building a custom frontend ## Dependencies for Flowise Hosting * Official Flowise GitHub repository: `https://github.com/FlowiseAI/Flowise` * Dockerfile from the Flowise repository * Public HTTP access through Railway on port `3000` * Railway Volume mounted at `/root/.flowise` for persistent storage * Default dashboard credentials using `FLOWISE_USERNAME=admin` and `FLOWISE_PASSWORD=admin123` * Secret key encryption value generated by Railway template variables ### Deployment Dependencies * Flowise GitHub repository: [https://github.com/FlowiseAI/Flowise](https://github.com/FlowiseAI/Flowise) * Flowise Railway deployment guide: [https://docs.flowiseai.com/configuration/deployment/railway](https://docs.flowiseai.com/configuration/deployment/railway) * Flowise environment variables: [https://docs.flowiseai.com/configuration/environment-variables](https://docs.flowiseai.com/configuration/environment-variables) * Railway Public Networking: [https://docs.railway.com/networking/public-networking](https://docs.railway.com/networking/public-networking) * Railway Volumes: [https://docs.railway.com/reference/volumes](https://docs.railway.com/reference/volumes) * Railway CLI: [https://docs.railway.com/guides/cli](https://docs.railway.com/guides/cli) * Railway SSH guide: [https://docs.railway.com/guides/ssh](https://docs.railway.com/guides/ssh) ### Environment Variables Use the following environment variables for this Railway template: ```bash PORT="3000" LOG_LEVEL="info" APIKEY_PATH="/root/.flowise" WORKER_PORT="5566" NODE_OPTIONS="--max-old-space-size=8192" FLOWISE_PASSWORD="admin123" FLOWISE_USERNAME="admin" SECRETKEY_ENCRYPT="${{secret(32, "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-_!~*")}}" PUPPETEER_SKIP_DOWNLOAD="true" PUPPETEER_EXECUTABLE_PATH="/usr/bin/chromium-browser" ``` Default login credentials: ```text Username: admin Password: admin123 ``` Users can change `FLOWISE_USERNAME` and `FLOWISE_PASSWORD` from Railway Variables at any time. For public deployments, it is recommended to replace the default password with a stronger value before sharing the Flowise URL. ### Public Web Access Flowise provides a web UI. After deployment, expose the service using Railway Public Networking and open the generated Railway domain or your custom domain in a browser. Recommended port: ```text 3000 ``` The service should use: ```bash PORT="3000" ``` Once the Railway domain is active, visit the public URL in your browser and log in using the configured `FLOWISE_USERNAME` and `FLOWISE_PASSWORD` values. The default credentials are `admin` and `admin123`, unless changed in Railway Variables. ### SSH Usage SSH is optional and mainly useful for diagnostics. Most users should access Flowise through the public web UI instead. #### Prerequisites Ensure you have the necessary setup in place: * The Railway CLI installed on your local machine * Logged in with your Railway account using: ```bash railway login ``` #### Usage You can copy the exact SSH command directly from the Railway dashboard: 1. Navigate to your project in the Railway dashboard. 2. Right-click on the Flowise service you want to connect to. 3. Select **Copy SSH Command** from the dropdown menu. 4. Paste the command into your local terminal to connect to the running Flowise container. Once connected, you can inspect files, review logs, check mounted storage, verify environment variables, or debug your Flowise deployment from inside the container. ### Implementation Details This template deploys Flowise from the official GitHub repository instead of using the prebuilt Docker image: ```text https://github.com/FlowiseAI/Flowise ``` Railway should build the app using the Dockerfile included in the repository. The upstream Dockerfile installs dependencies, builds Flowise, switches to a non-root `node` user, exposes port `3000`, and starts the app with `pnpm start`. This template includes default dashboard credentials so users can deploy quickly: ```text FLOWISE_USERNAME=admin FLOWISE_PASSWORD=admin123 ``` Users can change both values by editing the Railway Variables before deploying or after deployment. The other variables provide the default port, log level, worker port, memory settings, API key path, secret key generation, and Puppeteer/Chromium configuration. For persistent storage, mount the Railway Volume at `/root/.flowise`. This path keeps Flowise data available across restarts and redeployments. ## Why Deploy Flowise on Railway? Railway is a singular platform to deploy your infrastructure stack. Railway will host your infrastructure so you don't have to deal with configuration, while allowing you to vertically and horizontally scale it. By deploying Flowise on Railway, you are one step closer to supporting a complete full-stack application with minimal burden. Host your servers, databases, AI agents, and more on Railway. ## Similar templates - [Chat Chat](https://railway.com/deploy/-WWW5r) — Chat Chat, your own unified chat and search to AI platform. - [stella](https://railway.com/deploy/stella) — Self-host stella with web, API, Postgres, Redis, and object storage. - [Hermes Agent | OpenClaw Alternative with Dashboard](https://railway.com/deploy/hermes-agent-or-openclaw-alternative-wit) — Self-Hosted Hermes AI Agent for Telegram, Discord & Slack Open this page in a browser: https://railway.com/deploy/flowise-ai