🖥 Live Dashboard Screenshots

🤖 AI DevOps Automation System

AI-powered infrastructure monitoring, root cause analysis, and auto-remediation

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Architecture

┌──────────────────┐
│ Monitoring Agent │  — Collects metrics from Prometheus / Docker / K8s
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│ Diagnosis Agent  │  — AI (LLaMA3 / GPT-4 / Claude) finds root cause
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│ Remediation Agent│  — Generates + executes fix plans
└────────┬─────────┘
         │
         ▼
  Infrastructure
 (Docker / K8s / AWS)

Quick Start

1. Clone & Configure

cp .env.example .env
# Edit .env with your LLM API keys and infrastructure details

2. Docker Compose (Recommended)

cd infra/docker
docker-compose up -d

# Pull Ollama model (for local LLM)
docker exec -it ai-devops-ollama ollama pull llama3

3. Manual Setup

# Install Python deps
pip install -r requirements.txt

# Start backend
uvicorn backend.api.main:app --host 0.0.0.0 --port 8000 --reload

# Start frontend (new terminal)
cd frontend/dashboard
npm install && npm run dev

Access Points

Service	URL
Dashboard	http://localhost:5173
API Docs	http://localhost:8000/docs
Grafana	http://localhost:3001
Prometheus	http://localhost:9090
Kibana	http://localhost:5601

API Endpoints

GET  /api/metrics/current       — Latest metrics snapshot
GET  /api/metrics/history       — Historical metrics
GET  /api/alerts/active         — Active alerts
POST /api/alerts/action         — Acknowledge/resolve alerts
POST /api/diagnosis/analyze     — AI root cause analysis
GET  /api/diagnosis/demo        — Demo RCA
POST /api/remediation/plan      — Generate remediation plan
POST /api/remediation/execute   — Execute remediation
POST /api/chat/message          — DevOps chatbot

LLM Configuration

Set LLM_PROVIDER in .env:

Provider	Value	Notes
Ollama	ollama	Local, free, private
OpenAI	openai	GPT-4o — needs API key
Anthropic	anthropic	Claude — needs API key

Kubernetes Deployment

kubectl apply -f infra/kubernetes/namespace.yaml
kubectl apply -f infra/kubernetes/configmap.yaml
kubectl apply -f infra/kubernetes/secret.yaml
kubectl apply -f infra/kubernetes/deployment.yaml
kubectl apply -f infra/kubernetes/service.yaml
kubectl apply -f infra/kubernetes/hpa.yaml
kubectl apply -f infra/kubernetes/ingress.yaml

Features

• Real-time monitoring — CPU, memory, disk, network, pods, containers
• AI diagnosis — LLM-powered root cause analysis with evidence
• Auto-remediation — Scale, restart, rollback, clear logs
• Predictive alerts — ML trend analysis to predict future failures
• Incident chatbot — Ask AI about any infrastructure issue
• Self-healing — Automatic fix execution (dry-run by default)
• Multi-provider LLM — Ollama (local) / OpenAI / Anthropic

Tech Stack

Layer	Technology
Backend	Python, FastAPI, asyncio
AI Layer	LangChain, Ollama/Llama3, OpenAI, Anthropic
Frontend	React 18, Recharts, Vite
Monitoring	Prometheus, Grafana
Infra	Docker, Kubernetes, HPA
Logs	ELK Stack (Elasticsearch + Kibana)
Database	PostgreSQL, Redis

Safety

• DRY_RUN=true by default — all remediation is simulated
• High-risk actions require manual approval
• Full execution log for audit trail
• Rollback commands included in every plan