OpenAI Status Monitor (Event-Driven Python App) Overview
This project is a lightweight, event-driven Python application that automatically tracks and logs service updates from the OpenAI Status Page.
The application detects new incidents, outages, or degradations related to OpenAI products (such as Chat Completions, Responses API, Images, etc.) and prints clean, readable logs to the console.
It is intentionally designed as a long-running background worker, not a web application.
Key Highlights
✅ Event-driven monitoring using OpenAI’s official Atom feed
✅ Detects new updates across restarts
✅ Avoids inefficient polling and HTML scraping
✅ Supports both local interactive mode and hosted non-interactive mode
✅ Clean console output (HTML stripped)
✅ Defensive handling of network and data edge cases
✅ Designed to scale to 100+ status providers
✅ No database, no UI, minimal dependencies
Why Atom Feed Instead of the Website?
OpenAI’s public status page:
is designed for human users.
This project instead uses the official machine-readable Atom feed:
https://status.openai.com/history.atom Why this matters
Atom feeds are purpose-built for automation and monitoring
Avoids fragile HTML scraping
Naturally event-driven
Efficient and scalable
Matches real-world SRE / monitoring practices
This directly aligns with the assignment requirement to avoid manual refreshes and inefficient polling.
How It Works (High-Level Design)
Fetches the OpenAI status Atom feed
Generates a stable unique ID for the latest update
Compares it with the last seen update ID (persisted locally)
Behavior:
If a new update exists → prints the latest update
If no new update exists → prints a “No new updates” message and the last known update
In local mode, provides an option to:
View full historical logs
Exit the application
In hosted mode, runs continuously without user input
Execution Modes 1️⃣ Local Interactive Mode (Default)
Used when running locally on a developer machine.
Features:
Interactive console menu
Option to view full logs
Useful for testing and exploration
Run locally:
python main.py 2️⃣ Hosted / Non-Interactive Mode (Production)
Used when running on cloud platforms (Railway, Render, Fly.io).
Features:
No input() calls
Runs continuously as a background worker
Periodically checks for new updates
Logs output to platform log stream
Enabled via environment variable:
HOSTED_MODE=true Project Structure openai-status-tracker/ ├── main.py ├── requirements.txt ├── README.md └── .gitignore Runtime State
last_seen.txt is auto-generated at runtime
Stores the last processed update ID
Not committed to Git
Requirements
Python 3.10+
Dependencies:
feedparser
Install dependencies:
pip install -r requirements.txt Example Output New Update Detected 🔍 OpenAI Status Monitor
✅ New update detected!
[2026-02-24 16:42:11] Product: OpenAI API – Chat Completions Status: Degraded performance due to upstream issue No New Updates ℹ️ No new updates.
Last known update: [2026-02-24 16:42:11] Product: OpenAI API – Chat Completions Status: Degraded performance due to upstream issue Deployment (Railway)
This application is deployed as a background worker on Railway.
Why Railway?
Supports long-running Python processes
Persistent filesystem
Live log streaming
Suitable for monitoring workloads
Railway Configuration
Build Command
pip install -r requirements.txt
Start Command
python main.py
Environment Variables
HOSTED_MODE=true
No web server or exposed ports are required.
Error Handling & Edge Cases Covered
Network or feed fetch failures
Empty or malformed feed responses
HTML-rich summaries cleaned for readability
Duplicate updates avoided across restarts
Cloud environments without stdin (EOFError handled)
Graceful degradation instead of crashes
Scalability
This design can easily be extended to monitor:
Multiple providers
Multiple feeds
Additional alerting mechanisms (email, Slack, etc.)
The core logic remains unchanged.
What This Project Demonstrates
Event-driven system design
Practical Python backend skills
Cloud deployment awareness
Defensive coding practices
Clear separation of concerns
Production-ready thinking without over-engineering
Future Enhancements (Optional)
Support multiple status providers
Structured logging
Alerting integrations
Configurable polling intervals
Containerized deployment
Summary
This project intentionally prioritizes:
Correctness
Clarity
Scalability
Operational realism
over unnecessary complexity.
It reflects how real monitoring and observability tools are built in production environments.
Author
Built as part of a technical assignment to demonstrate event-driven monitoring using Python.