GenieOps 🧞

1. Install GenieOps from the VS Code Marketplace
2. Click the 🧞 icon in the Activity Bar (left sidebar)
3. Configure your Gemini API Key:
   • Get your free key from Google AI Studio
   • Paste it when prompted (or in Settings → GenieOps → Google API Key)
4. Start chatting! Press Cmd+Shift+O (Mac) or Ctrl+Shift+O (Windows/Linux)

Just type what you want in plain English:

"Deploy my app to AWS"
"Show me all running containers"
"Create a CI/CD pipeline for this Next.js project"
"Scale my Kubernetes deployment to 5 replicas"
"What's using the most CPU in production?"

• Docker: Build, run, stop, logs, inspect containers and images
• Kubernetes: Deploy, scale, update pods, services, and deployments
• Helm: Install and manage charts

• GitHub: Repos, PRs, Issues, Actions workflows
• GitLab: Pipelines, Merge Requests
• Jenkins: Jobs and builds

• Slack: Send deployment notifications and alerts
• Jira: Create issues, update tickets, sprint management

GenieOps features a modern chat interface inspired by GitHub Copilot:

• Multiple AI Models: Choose between Gemini Pro, Gemini Flash, Claude, GPT-4, and Perplexity
• Agent Modes: Agent, Assistant, Code, or DevOps specialized modes
• Smart Suggestions: Context-aware command suggestions
• Real-time Typing Indicators: See when GenieOps is thinking
• Visual Message Groups: Clean, organized conversation view

• 🎯 Project Auto-Detection: Automatically detects your stack (Next.js, React, Node.js, Python, etc.)
• 🔐 Secure Credential Storage: All API keys safely stored in VS Code's secure storage
• 📊 Real-time Monitoring: Live status updates for deployments and infrastructure
• 🔄 Workflow Automation: Pre-built templates for common DevOps tasks
• 📝 Command History: Track and replay previous operations
• 🌐 Multi-Cloud: Switch between AWS, GCP, and Azure seamlessly

1. Open VS Code Settings (Cmd+, on Mac, Ctrl+, on Windows/Linux)
2. Search for "GenieOps"
3. Enter your Gemini API Key (get one free at Google AI Studio)

Configure additional models for specialized tasks:

• Claude API Key: For complex reasoning tasks
• OpenAI API Key: For GPT-4 support
• Anthropic API Key: For Claude 3.5

Connect your cloud accounts:

1. Click the 🧞 icon in the Activity Bar
2. Go to "Connections" view
3. Click "+" to add AWS, GCP, or Azure credentials

Open Chat (Cmd+Shift+O) and type:
"Deploy my Next.js app to Vercel"

"Show me all running containers"
"Stop the nginx container"
"Build a Docker image from the current directory"

"Scale my web deployment to 5 replicas"
"Show me pods in the production namespace"
"Update the config map with new environment variables"

"Generate a GitHub Actions workflow for this project"
"Create a Dockerfile optimized for production"

GenieOps is built with:

• TypeScript for type safety
• VS Code Extension API for native integration
• Gemini AI as the primary intelligence layer
• MCP (Model Context Protocol) for tool orchestration
• Secure credential storage in VS Code's SecretStorage

Want to contribute or customize GenieOps?

# Clone the repository
git clone https://github.com/Kartikvyas1604/GenieOps.git
cd GenieOps

# Install dependencies
npm install

# Compile TypeScript
npm run compile

# Watch mode for development
npm run watch

Then press F5 in VS Code to launch the Extension Development Host.

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to your fork
5. Open a Pull Request

MIT License - see LICENSE file for details

Go to Settings → GenieOps → Google API Key and enter your Gemini API key

Check your credentials in the Connections panel. Click the service to re-authenticate.

Try reloading VS Code (Cmd+R or Ctrl+R)

Make sure you've clicked the 🧞 genie icon in the Activity Bar (left sidebar)

Q: Is GenieOps free?
A: Yes! GenieOps is open source. You only pay for your cloud/API usage (Gemini has a generous free tier).

Q: What's the difference between Agent and Assistant modes?
A: Agent mode can execute actions automatically, while Assistant mode provides guidance without making changes.

Q: Can I use this in production?
A: Yes, but always review commands before execution and test in staging first.

Q: Which AI model should I use?
A: Gemini Pro is the best balance of speed and capability. Use Gemini Flash for quick queries or Claude/GPT-4 for complex reasoning.

Built with:

• Gemini AI - Primary AI engine
• VS Code Extension API
• Model Context Protocol

The extension exposes 35+ tools via Model Context Protocol:

• docker_list_containers - List all containers
• docker_run_container - Run a new container
• docker_stop_container - Stop running container
• docker_build_image - Build Docker image
• docker_push_image - Push image to registry

• k8s_get_pods - List pods in namespace
• k8s_apply_manifest - Apply YAML manifest
• k8s_scale_deployment - Scale deployment replicas
• k8s_rollout_status - Check rollout status
• helm_install - Install Helm chart

• aws_lambda_invoke - Invoke Lambda function
• aws_s3_list_buckets - List S3 buckets
• gcp_run_deploy - Deploy to Cloud Run
• azure_function_invoke - Invoke Azure Function

• git_clone - Clone repository
• git_create_branch - Create new branch
• git_create_pr - Create pull request
• github_trigger_workflow - Trigger GitHub Action

Built-in patterns for production reliability:

import { resilience, CircuitBreaker, RetryManager } from './utils';

// Combined resilience policy
const policy = resilience()
  .withCircuitBreaker({ failureThreshold: 5 })
  .withRetry({ maxAttempts: 3, backoffMultiplier: 2 })
  .withBulkhead({ maxConcurrent: 10 })
  .withTimeout(30000);

const result = await policy.execute(() => apiCall());

• Circuit Breaker: Prevents cascading failures
• Retry with Backoff: Exponential backoff with jitter
• Bulkhead: Concurrency isolation
• Rate Limiting: Token bucket & sliding window
• Timeout Control: Configurable operation timeouts

Comprehensive metrics collection:

import { metrics, commandMetrics } from './utils';

// Track command execution
commandMetrics.executed.inc();
commandMetrics.duration.time(async () => {
  await executeCommand();
});

// Export Prometheus metrics
const prometheusOutput = metrics.exportPrometheus();

• Counter: Monotonically increasing values
• Gauge: Values that can go up/down
• Histogram: Distribution of values
• Timer: Duration measurements with percentiles

# Run all tests
npm test

# Run specific test suite
npm test -- --grep "GitHub"

# Run with coverage
npm run test:coverage

Test coverage includes:

• Unit tests for all integrations
• AI orchestrator tests
• NLP parsing tests
• Workflow engine tests
• MCP tool tests

We welcome contributions! Please see our Contributing Guide for details.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'feat: add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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

• Model Context Protocol for the MCP specification
• Anthropic for Claude AI
• VS Code for the extension API