🚀 InfraWatch AI – Multi-LLM DevOps Copilot

AI-Powered Infrastructure Monitoring, Incident Analysis & Multi-LLM Platform

Monitor infrastructure, analyze logs with Gemini AI, receive intelligent alerts, and compare responses across multiple LLMs from a single platform.

Built with Next.js, Clerk, Supabase, Docker, Redis, Nginx, Python, Gemini API, and GitHub Actions.

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📌 Overview

InfraWatch AI started as a Multi-LLM Chat System and evolved into a complete DevOps + AIOps platform.

The system enables users to:

• Compare responses from multiple LLMs simultaneously
• Persist chat history securely
• Monitor infrastructure health
• Analyze logs using AI
• Detect incidents automatically
• Receive email alerts
• Track API usage and system metrics
• Manage deployments using DevOps best practices

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🏗 Architecture

User
 │
 ▼
Clerk Authentication
 │
 ▼
Nginx Reverse Proxy
 │
 ▼
Next.js Application
 │
 ├────────► Gemini API
 │
 ├────────► OpenAI API
 │
 ├────────► Claude API
 │
 ├────────► Supabase PostgreSQL
 │
 └────────► Redis Cache
                 │
                 ▼
          Monitoring Service
                 │
                 ▼
            Gmail Alerts
                 │
                 ▼
         AI Log Analyzer
                 │
                 ▼
        Incident Dashboard

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✨ Core Features

🤖 Multi-LLM Chat System

• GPT Support
• Gemini Support
• Claude Support
• Side-by-side response comparison
• Streaming responses
• Custom temperature settings
• Model selection panel

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🔐 Authentication

Powered by Clerk.

Features:

• Google Login
• GitHub Login
• Secure Session Management
• Protected Dashboard Routes

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💬 Persistent Chat History

Stored in Supabase PostgreSQL.

Features:

• Conversation storage
• Message history
• Search conversations
• Continue previous chats

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📊 Usage Analytics

Track:

• Total Requests
• Token Consumption
• Model Usage
• Response Statistics

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⚡ Redis Caching

Features:

• Gemini response caching
• Faster repeated queries
• Reduced API costs
• Session optimization

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🐳 Dockerized Infrastructure

Application runs in isolated containers.

Services:

• Next.js
• Redis
• Monitoring Service
• Nginx

Benefits:

• Reproducible environments
• Easy deployment
• Simplified scaling

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🔄 CI/CD Pipeline

GitHub Actions automatically:

• Install dependencies
• Run lint checks
• Run type checking
• Build application
• Build Docker image
• Validate deployment readiness

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📈 Infrastructure Monitoring

Python monitoring service tracks:

• CPU Usage
• Memory Usage
• Disk Usage
• Container Health
• Service Availability

Metrics are stored in Supabase and displayed in real time.

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🧠 AI Log Analysis

Powered by Gemini API.

The system automatically analyzes:

• Application Logs
• Nginx Logs
• Docker Logs
• Infrastructure Errors

Generates:

• Root Cause Analysis
• Severity Classification
• Suggested Fixes
• Confidence Score

Example:

{
  "root_cause": "Redis Container Unavailable",
  "severity": "High",
  "suggested_fix": "Restart Redis Container",
  "confidence": 91
}

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📧 Smart Email Alerts

Automatic Gmail notifications for:

• High CPU Usage
• High Memory Usage
• Disk Space Issues
• Container Failures
• Critical Incidents

Example:

🚨 InfraWatch Alert

Service:
Redis

Issue:
Container Down

Suggested Action:
Restart Service

Severity:
High

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📊 DevOps Dashboard

Route:

/admin/devops

Features:

• Live CPU Metrics
• RAM Usage
• Disk Usage
• Container Status
• Alert History
• AI Incidents
• Gemini Usage Statistics
• Redis Cache Metrics

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📱 Progressive Web App (PWA)

Features:

• Installable
• Responsive
• Offline Support
• Mobile Friendly

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🛠 Tech Stack

Frontend

• Next.js 15
• React
• TypeScript
• Tailwind CSS
• Zustand

Authentication

• Clerk

Database

• Supabase PostgreSQL

Caching

• Redis

DevOps

• Docker
• Docker Compose
• Nginx
• GitHub Actions

Monitoring

• Python
• psutil

AI

• Gemini API
• OpenAI API
• Claude API

Deployment

• WSL Ubuntu
• Docker

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📂 Project Structure

infra-watch-ai/

├── app/
├── components/
├── lib/
│
├── services/
│   ├── monitoring/
│   │   ├── cpu.py
│   │   ├── memory.py
│   │   ├── disk.py
│   │   └── alerts.py
│   │
│   └── ai-analysis/
│       └── analyze_logs.py
│
├── docker/
│   ├── Dockerfile
│   └── nginx.conf
│
├── .github/
│   └── workflows/
│       └── ci.yml
│
├── supabase/
│   └── migrations/
│
└── README.md

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⚙️ Installation

Prerequisites

• Node.js 22+
• Docker Desktop
• WSL Ubuntu
• Redis
• Supabase Project
• Clerk Account
• Gemini API Key

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Clone Repository

git clone https://github.com/Darshan3690/Multi-LLM-Chat-System.git

cd Multi-LLM-Chat-System

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Install Dependencies

npm install

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Environment Variables

Create .env.local

# Clerk
NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY=
CLERK_SECRET_KEY=

# Supabase
NEXT_PUBLIC_SUPABASE_URL=
NEXT_PUBLIC_SUPABASE_ANON_KEY=
SUPABASE_SERVICE_KEY=

# Gemini
GEMINI_API_KEY=

# Redis
REDIS_URL=redis://localhost:6379

# Gmail
GMAIL_USER=
GMAIL_APP_PASSWORD=
ALERT_EMAIL=

# App
NEXT_PUBLIC_APP_URL=http://localhost:3000

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🐳 Run With Docker

Build:

docker compose build

Start:

docker compose up

Stop:

docker compose down

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💻 Local Development

npm run dev

Open:

http://localhost:3000

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🔄 CI/CD Workflow

Push Code
     │
     ▼
GitHub Actions
     │
     ▼
Lint
     │
     ▼
Type Check
     │
     ▼
Build
     │
     ▼
Docker Build
     │
     ▼
Deployment Ready

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📸 Screenshots

Add:

• Multi-LLM Chat UI
• DevOps Dashboard
• AI Incident Analysis
• Monitoring Dashboard
• Gmail Alerts

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🚀 Future Enhancements

• Kubernetes Deployment
• Grafana Dashboard
• Prometheus Integration
• Slack Notifications
• AWS Deployment
• AI Auto-Remediation
• Multi-Server Monitoring
• Incident Prediction

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🤝 Contributing

Contributions are welcome.

1. Fork Repository
2. Create Feature Branch

git checkout -b feature/amazing-feature

3. Commit Changes

git commit -m "Add amazing feature"

4. Push

git push origin feature/amazing-feature

5. Create Pull Request

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📄 License

Distributed under the MIT License.

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Made with ❤️ by Darshan Rajput

Building the future of AI-powered DevOps 🚀