🤖 AI Developer Agent

🚀 Local AI-powered code completion and development assistant - A free, privacy-focused alternative to GitHub Copilot

Transform your coding experience with AI-powered assistance that runs entirely on your machine. No cloud dependencies, no data sharing, no subscription fees - just powerful AI helping you code faster and smarter.

✨ Features

🤖 GitHub Copilot-Style Code Completion

• Real-time inline suggestions - Gray text appears as you type
• Context-aware completions - Understands your entire codebase
• Press Tab to accept - Familiar workflow
• Multi-language support - JavaScript, Python, TypeScript, Java, C#, Go, Rust, and more

💬 Interactive AI Chat

• Full conversation interface for coding questions
• Code generation with clickable "Insert" buttons
• Debug assistance and error explanations
• Architecture discussions and best practices

🧪 Smart Code Actions

• 🧪 Generate Unit Tests - Comprehensive test suites with edge cases
• 📝 Auto Documentation - JSDoc, docstrings, and inline comments
• 💡 Code Explanations - Understand complex algorithms instantly
• 🔧 Code Refactoring - Improve performance and readability
• 🩹 Bug Detection & Fixes - Identify and resolve issues automatically

🔒 Privacy & Performance

• 100% Local - Your code never leaves your machine
• No API keys required - No cloud dependencies
• GPU acceleration - Lightning-fast responses with NVIDIA GPUs
• Offline capable - Works without internet connection
• Multiple model sizes - From ultra-fast (300MB) to high-quality (3.8GB)

🚀 Quick Start

1. Install Ollama (AI Runtime)

Option A: Docker (Recommended)

# CPU only
docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama

# With GPU acceleration (NVIDIA)
docker run -d --gpus all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama

Option B: Direct Installation

• Windows/Mac: Download from ollama.ai
• Linux: curl https://ollama.ai/install.sh | sh

2. Pull AI Models

# Ultra-fast model for code completion (300MB)
docker exec -it ollama ollama pull qwen2:0.5b

# Balanced model for code completion (637MB) 
docker exec -it ollama ollama pull tinyllama:1.1b

# High-quality model for chat and explanations (3.8GB)
docker exec -it ollama ollama pull codellama:7b-instruct

3. Install Extension

From VS Code Marketplace:

1. Open VS Code
2. Go to Extensions (Ctrl+Shift+X)
3. Search for "AI Developer Agent"
4. Click "Install"

From GitHub Releases:

1. Download latest .vsix from Releases
2. Ctrl+Shift+P → "Extensions: Install from VSIX"
3. Select the downloaded file

4. Configure Settings

Open VS Code Settings (Ctrl+,) and search for "AI Dev Agent", or add to settings.json:

{
    "aiDevAgent.ollamaUrl": "http://localhost:11434",
    "aiDevAgent.model": "tinyllama:1.1b",
    "aiDevAgent.chat.model": "codellama:7b-instruct",
    "aiDevAgent.codeCompletion.enabled": true
}

5. Start Coding!

• Type code → Gray suggestions appear → Press Tab to accept
• Select code → Right-click → Choose AI action
• Open AI Chat → Ctrl+Shift+P → "AI Agent: Open Chat"

⚙️ Configuration

🔧 Essential Settings

⚡ Performance Settings

🚀 Optimized Configurations

Ultra-Fast Setup (GPU recommended):

{
    "aiDevAgent.model": "qwen2:0.5b",
    "aiDevAgent.codeCompletion.timeoutMs": 3000,
    "aiDevAgent.codeCompletion.maxLines": 1,
    "aiDevAgent.keepAlive": "-1"
}

Balanced Quality & Speed:

{
    "aiDevAgent.model": "tinyllama:1.1b",
    "aiDevAgent.chat.model": "codellama:7b-instruct",
    "aiDevAgent.codeCompletion.timeoutMs": 6000,
    "aiDevAgent.codeCompletion.maxLines": 2,
    "aiDevAgent.keepAlive": "-1"
}

Maximum Quality (Powerful hardware):

{
    "aiDevAgent.model": "codellama:7b-code",
    "aiDevAgent.chat.model": "deepseek-r1:7b",
    "aiDevAgent.codeCompletion.timeoutMs": 10000,
    "aiDevAgent.codeCompletion.maxLines": 3,
    "aiDevAgent.maxTokens": 4000
}

🎮 Usage

Code Completion

def fibonacci(n):
    if n <= 1:
        return n
    # Type here - AI suggests: return fibonacci(n-1) + fibonacci(n-2)

• Gray text appears as you type
• Press Tab to accept suggestion
• Press Escape to dismiss
• Context-aware - understands your entire file

AI Chat Interface

1. Open Chat: Ctrl+Shift+P → "AI Agent: Open Chat"
2. Ask questions: "How do I implement binary search in Python?"
3. Generate code: "Create a React login component with validation"
4. Debug help: "Why is my async function not working?"
5. Insert code: Click "Insert" button to add code to editor

Code Actions (Right-click menu)

• 🧪 Generate Unit Tests - Select function → Right-click → "Generate Unit Tests"
• 📝 Generate Documentation - Select code → Right-click → "Generate Documentation"
• 💡 Explain Code - Select complex code → Right-click → "Explain Code"
• 🔧 Refactor Code - Select code → Right-click → "Refactor Code"
• 🩹 Fix Code Issues - Select buggy code → Right-click → "Fix Code Issues"

🤖 Supported Models

Code Completion Models

Chat & Explanation Models

🔧 Troubleshooting

Common Issues

❌ "Cannot connect to Ollama"

# Check if Ollama is running
docker ps | grep ollama

# Start Ollama if not running
docker start ollama

# Test connection
curl http://localhost:11434/api/tags

❌ "Model not found"

# List available models
docker exec -it ollama ollama list

# Pull missing model
docker exec -it ollama ollama pull tinyllama:1.1b

❌ "Completion timeout"

• Increase timeout: Set aiDevAgent.codeCompletion.timeoutMs to 10000
• Use faster model: Switch to qwen2:0.5b
• Check Docker resources: Allocate 4GB+ memory to Docker

❌ "Code completion not working"

• Check model is loaded: docker exec -it ollama ollama ps
• Verify settings: Ensure aiDevAgent.codeCompletion.enabled is true
• Restart extension: Ctrl+Shift+P → "Developer: Reload Window"

Performance Optimization

🚀 GPU Acceleration (NVIDIA)

# Stop current container
docker stop ollama && docker rm ollama

# Start with GPU support
docker run -d --gpus all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama

# Verify GPU usage
nvidia-smi  # Should show GPU activity during completion

💾 Memory Optimization

• Docker Desktop: Settings → Resources → Memory (6GB+)
• Keep models loaded: Set aiDevAgent.keepAlive to "-1"
• Use smaller models: qwen2:0.5b for resource-constrained systems

🌍 Language Support

Fully Supported Languages:

• JavaScript/TypeScript
• Python
• Java
• C#
• Go
• Rust
• C/C++
• PHP
• Ruby
• Swift
• Kotlin

Partial Support:

• HTML/CSS
• SQL
• Shell/Bash
• Markdown
• JSON/YAML

🤝 Contributing

We welcome contributions! Please see our Contributing Guide.

Development Setup

# Clone repository
git clone https://github.com/hariprasathys22/AI-Agent.git
cd AI-Agent

# Install dependencies
npm install

# Build extension
npm run compile

# Test extension
# Press F5 in VS Code to open Extension Development Host

Submitting Issues

• 🐛 Bug reports: Create issue
• 💡 Feature requests: Create issue
• ❓ Questions: GitHub Discussions

📈 Roadmap

• [ ] Multi-file context - Understand project structure
• [ ] Jupyter Notebook support - AI assistance in notebooks
• [ ] Git integration - Smart commit messages and PR descriptions
• [ ] Custom model training - Fine-tune on your codebase
• [ ] Collaborative features - Team model sharing
• [ ] More languages - Expand language support

📊 Comparison

📄 License

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

🙏 Acknowledgments

• Ollama - For providing the local AI runtime
• VS Code team - For the excellent extension API
• Hugging Face - For open-source model hosting
• Contributors - Thank you to all who have contributed!

📞 Support

• 📚 Documentation: GitHub Wiki
• 💬 Community: GitHub Discussions
• 🐛 Issues: GitHub Issues
• 🔄 Updates: Release Notes

⭐ If this extension helps you code faster, please star the repository!

Made with ❤️ for developers who value privacy and performance