Day 35: FlowiseAI – Low-code developer tool for building LLM Applications – 7 Days of Docker

LAST MODIFIED: 14. August 2025

29. July 2025

Development / Agents / AI / Automation / Deployment / Docker

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What is FlowiseAI?

FlowiseAI is an open-source generative AI development platform for building AI agents and LLM workflows. Here are its key features:

Core Capabilities:

Visual drag-and-drop interface for creating AI workflows without coding
Built on LangChain framework with Node.js and React
Low-code/no-code workflow automation specifically for LLM applications
Supports conversational agents, templates, and cloud deployment

Key Features:

Ready-to-use app templates
Conversational agents with memory capabilities
Seamless deployment on cloud platforms
API integration capabilities
Cross-platform compatibility
Comprehensive monitoring and reporting
Visual workflow builder for connecting large language models and tools

FlowiseAI Docker Compose Installation Guide

Prerequisites

Before starting the installation, ensure you have:

Docker and Docker Compose installed on your system
A VPS or server where you can host FlowiseAI
Basic knowledge of Docker and command line operations
(Optional) Reverse proxy setup for SSL and domain access

Step 1: Create Docker Compose File

Create a new directory for your FlowiseAI installation and create a docker-compose.yml file:


version: "3.9"

services:
  flowise-db:
    image: postgres:16-alpine
    hostname: flowise-db
    environment:
      POSTGRES_DB: ${POSTGRES_DB}
      POSTGRES_USER: ${POSTGRES_USER}
      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
    volumes:
      - ./flowise-db-data:/var/lib/postgresql/data
    restart: unless-stopped
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER} -d ${POSTGRES_DB}"]
      interval: 5s
      timeout: 5s
      retries: 5

  flowise:
    image: flowiseai/flowise:latest
    container_name: flowiseai
    hostname: flowise
    healthcheck:
      test: wget --no-verbose --tries=1 --spider http://localhost:${PORT}
    ports:
      - 5023:${PORT}
    volumes:
      - ./flowiseai:/root/.flowise
    environment:
      DEBUG: false
      PORT: ${PORT}
      FLOWISE_USERNAME: ${FLOWISE_USERNAME}
      FLOWISE_PASSWORD: ${FLOWISE_PASSWORD}
      APIKEY_PATH: /root/.flowise
      SECRETKEY_PATH: /root/.flowise
      LOG_LEVEL: info
      LOG_PATH: /root/.flowise/logs
      DATABASE_TYPE: postgres
      DATABASE_PORT: 5432
      DATABASE_HOST: flowise-db
      DATABASE_NAME: ${POSTGRES_DB}
      DATABASE_USER: ${POSTGRES_USER}
      DATABASE_PASSWORD: ${POSTGRES_PASSWORD}
    restart: on-failure:5
    depends_on:
      flowise-db:
        condition: service_healthy
    entrypoint: /bin/sh -c "sleep 3; flowise start"

  # Optional: Database Backup Service
  flowise-db-backup:
    container_name: flowise-db-backup
    image: tiredofit/db-backup
    volumes:
      - ./backups:/backup
    environment:
      DB_TYPE: postgres
      DB_HOST: flowise-db
      DB_NAME: ${POSTGRES_DB}
      DB_USER: ${POSTGRES_USER}
      DB_PASS: ${POSTGRES_PASSWORD}
      DB_BACKUP_INTERVAL: 720
      DB_CLEANUP_TIME: 72000
      CHECKSUM: SHA1
      COMPRESSION: GZ
      CONTAINER_ENABLE_MONITORING: false
    depends_on:
      flowise-db:
        condition: service_healthy
    restart: unless-stopped

version: "3.9"

services:

flowise-db:

image: postgres:16-alpine

hostname: flowise-db

environment:

POSTGRES_DB: ${POSTGRES_DB}

POSTGRES_USER: ${POSTGRES_USER}

POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}

volumes:

- ./flowise-db-data:/var/lib/postgresql/data

restart: unless-stopped

healthcheck:

test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER} -d ${POSTGRES_DB}"]

interval: 5s

timeout: 5s

retries: 5

flowise:

image: flowiseai/flowise:latest

container_name: flowiseai

hostname: flowise

healthcheck:

test: wget --no-verbose --tries=1 --spider http://localhost:${PORT}

ports:

- 5023:${PORT}

volumes:

- ./flowiseai:/root/.flowise

environment:

DEBUG: false

PORT: ${PORT}

FLOWISE_USERNAME: ${FLOWISE_USERNAME}

FLOWISE_PASSWORD: ${FLOWISE_PASSWORD}

APIKEY_PATH: /root/.flowise

SECRETKEY_PATH: /root/.flowise

LOG_LEVEL: info

LOG_PATH: /root/.flowise/logs

DATABASE_TYPE: postgres

DATABASE_PORT: 5432

DATABASE_HOST: flowise-db

DATABASE_NAME: ${POSTGRES_DB}

DATABASE_USER: ${POSTGRES_USER}

DATABASE_PASSWORD: ${POSTGRES_PASSWORD}

restart: on-failure:5

depends_on:

flowise-db:

condition: service_healthy

entrypoint: /bin/sh -c "sleep 3; flowise start"

# Optional: Database Backup Service

flowise-db-backup:

container_name: flowise-db-backup

image: tiredofit/db-backup

volumes:

- ./backups:/backup

environment:

DB_TYPE: postgres

DB_HOST: flowise-db

DB_NAME: ${POSTGRES_DB}

DB_USER: ${POSTGRES_USER}

DB_PASS: ${POSTGRES_PASSWORD}

DB_BACKUP_INTERVAL: 720

DB_CLEANUP_TIME: 72000

CHECKSUM: SHA1

COMPRESSION: GZ

CONTAINER_ENABLE_MONITORING: false

depends_on:

flowise-db:

condition: service_healthy

restart: unless-stopped

Step 2: Create Environment File

Create a .env file in the same directory with your configuration:


# Application Configuration
PORT=3000
FLOWISE_USERNAME=admin
FLOWISE_PASSWORD=your_secure_password

# Database Configuration
POSTGRES_USER=flowiseuser
POSTGRES_PASSWORD=your_db_password
POSTGRES_DB=flowise

# Application Configuration

PORT=3000

FLOWISE_USERNAME=admin

FLOWISE_PASSWORD=your_secure_password

# Database Configuration

POSTGRES_USER=flowiseuser

POSTGRES_PASSWORD=your_db_password

POSTGRES_DB=flowise

Important Security Notes:

Replace your_secure_password and your_db_password with strong, unique passwords
Consider using environment-specific passwords for production deployments

Step 3: Deploy FlowiseAI

Navigate to your project directory and run:


# Create necessary directories
mkdir -p flowise-db-data flowiseai backups

# Start the services
docker-compose up -d

# Create necessary directories

mkdir -p flowise-db-data flowiseai backups

# Start the services

docker-compose up -d

Step 4: Verify Installation

Check if all containers are running:


docker ps

docker ps

You should see three containers running:

flowiseai (main application)
flowise-db (PostgreSQL database)
flowise-db-backup (optional backup service)

Check the logs to ensure everything started correctly:


# Check FlowiseAI logs
docker-compose logs flowise

# Check database logs
docker-compose logs flowise-db

# Check FlowiseAI logs

docker-compose logs flowise

# Check database logs

docker-compose logs flowise-db

Step 5: Access FlowiseAI

Once deployed, you can access FlowiseAI at:

Local access: http://localhost:5023
Login credentials: Use the username and password from your .env file

Directory Structure

After installation, your directory structure should look like:


flowiseai/
├── docker-compose.yml
├── .env
├── flowise-db-data/          # PostgreSQL data
├── flowiseai/                # FlowiseAI application data
│   ├── logs/
│   └── ...
└── backups/                  # Database backups (if enabled)
    └── ...

flowiseai/

├── docker-compose.yml

├── .env

├── flowise-db-data/ # PostgreSQL data

├── flowiseai/ # FlowiseAI application data

│ ├── logs/

│ └── ...

└── backups/ # Database backups (if enabled)

└── ...

Next Steps

Create Your First Flow: Access the web interface and start building AI workflows
Explore Templates: Check the marketplace for pre-built flow templates
API Integration: Use the REST API for external integrations
Security: Review and strengthen authentication settings for production use

Let’s Talk!

Looking for a reliable partner to bring your project to the next level? Whether it’s development, design, security, or ongoing support—I’d love to chat and see how I can help.

Get in touch,
and let’s create something amazing together!

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Server, Network and Hosting Environments
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Business Logic
API Layer / Action layer / MVC
User Interfaces
User Experience
Understand what the customer and the business needs

I have a deep passion for programming, design, and server architecture—each of these fuels my creativity, and I wouldn’t feel complete without them.

With a broad range of interests, I’m always exploring new technologies and expanding my knowledge wherever needed. The tech world evolves rapidly, and I love staying ahead by embracing the latest innovations.

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I firmly believe in the principle: Help others, and help will find its way back to you when you need it.

Day 35: FlowiseAI – Low-code developer tool for building LLM Applications – 7 Days of Docker

Table of Contents

What is FlowiseAI?

FlowiseAI Docker Compose Installation Guide

Prerequisites

Step 1: Create Docker Compose File

Step 2: Create Environment File

Step 3: Deploy FlowiseAI

Step 4: Verify Installation

Step 5: Access FlowiseAI

Directory Structure

Next Steps

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