Digital Life Engine: A Tamagotchi-as-a-Service API built with Xano

This is a submission for the Xano AI-Powered Backend Challenge: Production-Ready Public API

What I Built

The Digital Life Engine is a stateful, production-ready API that allows developers to integrate "living" digital creatures into their applications without managing the complex backend logic.

Unlike static data APIs (like To-Do lists), this engine features real-time entropy. Creatures get hungry, lose energy, and can even die if neglected, thanks to background CRON tasks running on the server. It features logic for species generation, validated interactions (feeding/playing), and immutable event logging.

API Documentation

The API is fully documented using Swagger/OpenAPI. You can explore the endpoints, see the schema models, and test the "Incubator" logic directly in the browser here:

🔗 https://xcej-diqj-m3ns.n7e.xano.io/api:iCZklw7Y

Demo

Here is the core logic that powers the interaction engine. Instead of simple CRUD operations, the API calculates the outcome of interactions based on the creature's current state.

Logic Flow (Function Stack):
![Interaction Logic]

The "Heartbeat" (Background Task):
A background task runs periodically to simulate the passage of time (increasing hunger), ensuring the digital pets are "alive" even when no user is logged in.
![Cron Job]

The AI Prompts I Used

I utilized Xano's AI at three critical stages to speed up development while maintaining architectural integrity.

1. The Database Schema (The Foundation)
To generate the relational structure, I used this "Master Architect" prompt:

"Act as a Senior Backend Architect. Design a production-ready database schema for a 'Digital Life Engine' (a Tamagotchi-as-a-Service API).
I need tables for: Keepers (users with api_key), Species (config with base stats), Creatures (stateful entities with hunger/happiness/status), Items (consumables), and Events (audit log). Ensure relationships are linked correctly."

2. The Incubator Logic (POST /creature)
To transform the basic creation endpoint into a secure factory:

"Update this endpoint to be a creature incubator. Remove all inputs except name and species_id. Logic: Fetch species record. Add record to creature with default stats (Hunger=50, Status='alive') and inherit base_max_health from the species. Set keeper_id to 1."

3. The Interaction Logic (POST /interact)
To build the complex game rules:

"Build a complex interaction logic endpoint.

1. Inputs: creature_id, item_id.
2. Logic: Fetch data from creature and item. If creature.status is 'dead', throw error.
3. Apply effects: If food, decrease hunger. If toy, increase happiness. Log the event."

How I Refined the AI-Generated Code

Before: The AI originally provided standard CRUD endpoints where a user could manually set a creature's health or hunger to any value.

After: I transformed the POST /creature endpoint into a secure Incubator.

• I removed the ability for users to set stats manually.
• I added a database lookup to fetch the Species base stats.
• I implemented server-side validation to ensure new creatures start with correct values (Hunger: 50, Status: Alive).

This transformation moved the business logic from the client to the server, preventing cheating and ensuring data integrity.

My Experience with Xano

Using Xano was incredibly fast. The "Edit with AI" feature in the Function Stack was a game-changer—it allowed me to write business logic in plain English and have the visual nodes generated instantly.

The most helpful feature was the Background Tasks. Being able to run server-side logic on a timer (to simulate hunger decay) turned a simple database into a living simulation engine.

---

Submitted for the Xano API Challenge.