README.md

metadata

title: Azure Ops Copilot
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: docker
app_port: 7860
pinned: false
license: mit
short_description: AI-Powered DevOps Assistant for Azure Operations
tags:
  - building-mcp-track-enterprise

🤖 Azure Ops Copilot

AI-Powered DevOps Assistant for Azure Operations

Demo • Documentation • Installation • Contributing

---

✨ Features

🔍 Alert Analysis Automatically analyze Azure Monitor alerts with AI-powered root cause identification and remediation suggestions. ⚙️ Configuration Review Inspect resource configurations (ARM/Bicep) and check compliance status across your Azure infrastructure.

🔧 Fix Generation Generate ready-to-use Bicep templates and Azure CLI commands for common issues and optimizations. 💬 Natural Language Interface Chat with your Azure infrastructure using plain English - no complex queries needed!

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🎯 Demo

Try it live on Hugging Face Spaces!

Example queries:

💬 "Analyze alert alert-001"
💬 "Check config for vm-01"
💬 "Suggest a fix for high CPU on vm-01"
💬 "Summarize the recent logs"

---

🏗️ Conversation Flow

Tech Stack

Component	Technology
Frontend
Backend
AI Framework
LLM
MCP Server
Package Manager

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🚀 Quick Start

Prerequisites

• Python 3.10+
• uv package manager
• Azure OpenAI API access

Installation

1. Clone the repository

   git clone https://github.com/mahimairaja/azure-agent-ops-copilot.git
   cd azure-agent-ops-copilot
   

2. Install dependencies

   uv sync --all-groups
   

3. Configure environment

   cp .env.example .env
   

   Edit .env and add your Azure OpenAI credentials:

   AZURE_OPENAI_ENDPOINT=https://your-endpoint.openai.azure.com/
   AZURE_OPENAI_API_KEY=your-api-key-here
   AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o-mini
   

4. Generate sample data

   uv run python scripts/generate_logs.py
   uv run python scripts/generate_configs.py
   

5. Run the application

   Terminal 1 - Start the backend:

   uv run python src/backend/app.py
   

   Terminal 2 - Start the frontend:

   uv run python src/frontend/app.py
   

6. Open your browser

   Navigate to http://localhost:7860 and start chatting!

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🐳 Docker Deployment

Local Docker

# Build the image
docker build -t azure-ops-copilot .

# Run the container
docker run -p 7860:7860 -p 8000:8000 \
  -e AZURE_OPENAI_ENDPOINT=your-endpoint \
  -e AZURE_OPENAI_API_KEY=your-key \
  -e AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o-mini \
  azure-ops-copilot

Hugging Face Spaces

This project is configured for one-click deployment to Hugging Face Spaces:

1. Fork this repository
2. Create a new Space on Hugging Face
3. Choose Docker as the SDK
4. Connect your GitHub repository
5. Add your Azure OpenAI credentials as Space secrets
6. Deploy! 🚀

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📖 Usage Examples

Analyze Alerts

# Ask the copilot
"Analyze alert alert-001"

# Response includes:
# - Alert details
# - Severity and resource info
# - Root cause analysis
# - Suggested remediation

Check Resource Configuration

# Query by short name
"Check config for vm-01"

# Or by full resource ID
"Show configuration for /subscriptions/.../virtualMachines/vm-01"

Generate Fixes

# Request fix generation
"Suggest a fix for high CPU on vm-01"

# Get:
# - Bicep template for VM resize
# - Azure CLI commands
# - Best practices

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• Semantic Kernel - AI orchestration framework
• FastMCP - Model Context Protocol implementation
• Gradio - Beautiful web UI framework
• Azure OpenAI - LLM provider

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📬 Contact

Mahimai Raja - LinkedIn - GitHub

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⭐ Star this repo if you find it helpful!

Made with ❤️ by Mahimai Raja

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