App Droplet Object

🧭 Purpose

The App Droplet Object is a specialized environment within Reasoning Flows for developing, managing, and publishing web applications or API services written in PHP or Python.
It integrates directly with configured App Droplet servers, enabling developers to write, test, and deploy applications directly from within the platform.

This object serves as the bridge between data-driven workflows and production deployment, giving teams a secure, controlled way to operationalize data applications and APIs.

🔹 Where It Fits in Reasoning Flows

In the Reasoning Flows architecture:

Extract & Load → Ingests and structures source data.
Transform & Prepare → Cleans and enriches data for analytics.
AI & Machine Learning → Builds and trains predictive or generative models.
Visual Objects → Delivers insights through dashboards, APIs, or notebooks.
Data Models / Knowledge Nodes → Define semantic entities.
DataApp → Combines and orchestrates all components into a deployable project.
App Droplet Object → Publishes these applications to external servers for production use.

Goal: The App Droplet Object transforms Reasoning Flows projects into live, operational web or API services hosted on connected servers.

⚙️ Functionality

An App Droplet Object provides a complete web development workspace within Reasoning Flows, supporting the full lifecycle from coding to deployment.

Key Capabilities

💻 Built-in Code Editor — Develop multi-file applications directly in the browser.
🔗 Server Integration — Connects to pre-configured App Droplet servers for deployment.
🧱 Project Management — Supports structured file organization and dependency management.
🖥️ Terminal Access — Offers a built-in terminal for real-time command-line interaction.
🚀 Controlled Publishing — Deploys code safely through a version-controlled publishing process.

🧩 Development Environment

When creating an App Droplet Object, you must select the programming language and runtime environment:

PHP Environment
Ideal for traditional web applications, dynamic sites, or backend interfaces.
Supports integration with existing PHP frameworks.
Python Environment
Suited for APIs, data-driven microservices, or web integrations.
Often used for serving ML model endpoints or Reasoning Flows outputs.

The selected language defines the structure, syntax support, and deployment environment of your App Droplet Object.

🧠 Interface Overview

The App Droplet interface is designed for both simplicity and power — giving developers full control over coding and publishing workflows.

1. Code Area

A built-in development environment where you can create, edit, and organize project files.
It functions as a lightweight IDE optimized for web and API development within Reasoning Flows.

2. Publishing Configuration

Defines where and how your application will be deployed.

Select a target App Droplet Server for publishing.
Once configured, the Publish button becomes active.
Deployment replaces all files in the target directory with the latest versions managed by the App Droplet Object.

Tip: Always test in a staging server before pushing to production.

3. Terminal

A built-in SSH terminal that connects directly to the associated App Droplet server.
You can run commands, inspect logs, manage dependencies, and interact with the environment in real time.

App Droplet Interface

🧱 Typical Use Cases

Deploying API services that expose Reasoning Flows outputs (e.g., model predictions or prepared datasets).
Hosting micro frontends or lightweight dashboards for data visualization.
Managing Python FastAPI or PHP-based applications that automate workflows.
Integrating external services or triggering actions via web endpoints.

🧠 Best Practices

Use App Droplet Objects for all externally facing deployments — keep production code separate from workflow logic.
Establish staging and production droplets for safe testing and controlled releases.
Secure connections using SSH keys and HTTPS endpoints.
Regularly sync code changes to version control before publishing.
Document the deployed services and endpoints in the Knowledge Atlas for traceability and governance.
Combine App Droplets with DataApps to package full workflow + deployment logic together.

🔗 Related Documentation

DataApp Overview — Learn how to orchestrate full applications before deployment.
Visual Object Overview — Understand how to integrate visualization layers and APIs.
AI & Machine Learning Overview — Deploy trained models as APIs via App Droplets.
Knowledge Atlas Overview — Document published applications and endpoints.