The 3 AM Container Crash Problem Every Developer Knows
Picture this scenario: you're running five containerized services on a ₹1,500/month VPS — a Flask API, Postgres database, Nginx reverse proxy, Redis cache, and a background worker. Everything runs fine for a week. Then at 3 AM, one of them crashes. Your phone buzzes with a generic alert. You SSH in, dig through logs, Google the error, restart the container, and go back to sleep. Next week — same story, different container.
What if an AI agent could do all of this automatically? That's exactly what we're building: a Container Doctor — a Python-based AI agent that connects to the Docker socket, monitors containers in real-time, sends error logs to Claude AI for diagnosis, and takes corrective action automatically.
The total cost? About ₹50–100/month in Claude API calls. Compare that to the engineering hours you'd burn debugging at 3 AM.
Architecture Overview
The Container Doctor has four components working together:
| Component | Role | File |
|---|---|---|
| Monitor | Watches Docker containers via API | monitor.py |
| Diagnosis Engine | Sends logs to Claude AI for analysis | diagnosis.py |
| Action Handler | Restarts containers, sends Slack alerts | actions.py |
| Config | Environment variables and settings | .env |
Project Structure
container-doctor/
├── monitor.py # Main monitoring loop
├── diagnosis.py # Claude AI diagnosis engine
├── actions.py # Auto-restart + Slack alerts
├── requirements.txt # Python dependencies
├── Dockerfile # Containerize the monitor itself
├── docker-compose.yml # Deploy alongside your services
└── .env # API keys and configStep 1: Install Dependencies
pip install docker anthropic slack-sdk python-dotenvOr create a requirements.txt:
Premium Content
Full Python source code, Claude AI prompt engineering, Docker Compose setup, Slack alerts integration, and cost breakdown inside — copy-paste ready for your own server.
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