AI Agent Studio - VS Code Extension

🤖 The ultimate VS Code extension for developing AI agents with comprehensive support for all major frameworks including OpenAI Agents SDK, ElizaOS, LangGraph, CrewAI, AutoGen, SmolAgents, and more.

🚀 Features Overview

🏗️ Framework Support (10+ Frameworks)

Production-Ready Frameworks

• OpenAI Agents SDK - Latest production-ready multi-agent framework
• ElizaOS - Web3-friendly TypeScript agent framework with character-based AI
• LangGraph - State machine approach for complex agent workflows
• CrewAI - Role-based multi-agent collaboration framework
• Microsoft AutoGen - Conversation-based multi-agent systems
• SmolAgents - Minimalist code-first agent development
• Google ADK - Enterprise-grade agent development kit
• Semantic Kernel - Microsoft's enterprise AI orchestration framework
• LangChain - Popular LLM application framework
• Pydantic AI - Type-safe agent development with validation

🎯 Core Capabilities

📋 Project Management

• Smart Project Templates - 25+ production-ready templates
• Framework Detection - Automatically detect and configure installed frameworks
• Project Scaffolding - Complete project structure generation
• Dependency Management - Automatic dependency installation and verification

💡 Intelligent Code Assistance

• Smart Snippets - 50+ framework-specific code snippets
• Auto-completion - Context-aware code completion
• Syntax Highlighting - Custom syntax highlighting for agent configs
• Error Detection - Framework-specific error detection and suggestions

🔍 Context7 Integration

• Real-time Documentation - Up-to-date framework documentation
• Code Examples - Latest working code examples
• API References - Quick access to API documentation
• Best Practices - Framework-specific development patterns

📊 Agent Monitoring & Testing

• Real-time Monitoring - Monitor agent performance and behavior
• Debug Tools - Advanced debugging capabilities
• Testing Framework - Built-in testing tools for agents
• Performance Analytics - Response time, success rate, and resource usage tracking

🎨 Visual Tools

• Agent Dashboard - Comprehensive agent management interface
• Flow Visualizer - Visualize agent workflows and interactions
• Project Explorer - Enhanced project navigation
• Framework Status - Visual framework installation status

🛠 Developer Experience

⚡ Quick Start Experience

1. One-Click Project Creation - Create complete agent projects in seconds
2. Template Selection - Choose from basic to advanced templates
3. Automatic Setup - Dependencies, configuration, and examples included
4. Live Documentation - Context7 provides real-time help

🔄 Development Workflow

• Code Generation - Generate agent boilerplate with templates
• Live Reload - Hot reload during development
• Deployment Helpers - Deploy to AWS, Google Cloud, Azure, and more
• CI/CD Integration - GitHub Actions and other CI/CD workflows

📦 Installation

From VS Code Marketplace

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

From Command Line

code --install-extension ai-agent-studio.ai-agent-studio

From VSIX File

code --install-extension ai-agent-studio-1.0.0.vsix

🎮 Quick Start Guide

🚀 Create Your First Agent Project

Option 1: Command Palette

1. Open Command Palette (Ctrl+Shift+P / Cmd+Shift+P)
2. Type AI Agent Studio: Create New Agent Project
3. Select your framework (OpenAI, ElizaOS, LangGraph, etc.)
4. Choose a template (Basic, Advanced, Production)
5. Configure project details
6. Start coding immediately!

Option 2: Activity Bar

1. Click the 🤖 AI Agent Studio icon in the Activity Bar
2. Click "Create Project" in the Agent Projects panel
3. Follow the wizard to set up your project

💻 Use Framework-Specific Snippets

TypeScript/JavaScript

// Type 'openai-agent' then Tab
import { Agent } from '@openai/agents-sdk';

export class MyAgent extends Agent {
    constructor() {
        super({
            name: 'MyAgent',
            instructions: 'You are a helpful AI assistant.',
            model: 'gpt-4o',
            temperature: 0.7
        });
    }

    async handleMessage(message: string): Promise<string> {
        // Implementation
    }
}

Python

# Type 'crewai-agent' then Tab
from crewai import Agent

agent = Agent(
    role='Research Specialist',
    goal='Gather comprehensive information',
    backstory='Expert researcher with years of experience',
    verbose=True,
    tools=[search_tool],
    memory=True
)

🔍 Access Real-time Documentation

1. Right-click Context Menu: Right-click in editor → "Search Context7 Documentation"
2. Command Palette: AI Agent Studio: Search Context7 Documentation
3. Hover Information: Hover over framework keywords for instant docs
4. Sidebar Explorer: Browse documentation in the Context7 Explorer panel

🏗️ Framework-Specific Features

🤖 OpenAI Agents SDK

// Multi-agent coordination
export class CoordinatorAgent extends Agent {
    private agents: Map<string, Agent> = new Map();

    async delegateTask(task: string, agentName?: string): Promise<string> {
        const agent = agentName ? 
            this.agents.get(agentName) : 
            await this.selectBestAgent(task);
        return await agent.complete(task);
    }
}

Features:

• Function calling support
• Multi-agent orchestration
• Streaming responses
• Production-ready templates

🎭 ElizaOS

// Character-based AI
const character = {
    name: 'TechAssistant',
    bio: 'A knowledgeable technical assistant',
    lore: [
        'I specialize in software development',
        'I help debug code and explain concepts'
    ],
    style: {
        all: ['Be technical but approachable', 'Provide code examples']
    }
};

Features:

• Character personality system
• Custom action handlers
• Provider integrations
• Web3 compatibility

🔗 LangGraph

# State machine workflows
class WorkflowState(TypedDict):
    messages: Annotated[List[str], operator.add]
    current_step: str
    result: str

workflow = StateGraph(WorkflowState)
workflow.add_node('process', process_node)
workflow.add_conditional_edges('process', decide_next)

Features:

• Visual workflow designer
• State management
• Conditional logic
• Human-in-the-loop support

👥 CrewAI

# Multi-agent teams
researcher = Agent(role='Researcher', goal='Gather information')
analyst = Agent(role='Analyst', goal='Analyze data')
writer = Agent(role='Writer', goal='Create content')

crew = Crew(
    agents=[researcher, analyst, writer],
    tasks=[research_task, analysis_task, writing_task],
    process=Process.sequential
)

Features:

• Role-based agents
• Sequential and hierarchical processes
• Task delegation
• Memory sharing

💬 AutoGen

# Conversational agents
user_proxy = autogen.UserProxyAgent(name="User")
assistant = autogen.AssistantAgent(name="Assistant")
groupchat = autogen.GroupChat(agents=[user_proxy, assistant])
manager = autogen.GroupChatManager(groupchat=groupchat)

Features:

• Group chat management
• Code execution
• Human input modes
• Conversation flow control

⚙️ Configuration

🔧 Extension Settings

🔑 API Keys Configuration

// settings.json
{
    "aiAgentStudio.defaultFramework": "openai-agents-sdk",
    "aiAgentStudio.context7.enabled": true,
    "aiAgentStudio.monitoring.enabled": true
}

🌐 Context7 Setup

1. Built-in Integration (Recommended):

   • Extension includes Context7 integration
   • Enable in settings: aiAgentStudio.context7.enabled: true

2. Manual Setup:

   npm install -g @upstash/context7-mcp
   

📚 Available Commands

🎯 Core Commands

🛠 Framework Commands

📊 Monitoring Commands

🏗️ Project Structure

📁 Generated Project Structure

my-agent-project/
├── .aiagent/                 # Extension metadata
│   └── project.json         # Project configuration
├── src/                     # Source code
│   ├── agents/             # Agent implementations
│   │   ├── coordinator.ts  # Main coordinator agent
│   │   └── specialists/    # Specialized agents
│   ├── tools/              # Custom tools and functions
│   │   ├── search.ts       # Search tools
│   │   └── data.ts         # Data processing tools
│   ├── workflows/          # Agent workflows
│   │   └── main.ts         # Main workflow definition
│   ├── config/             # Configuration files
│   │   ├── agents.json     # Agent configurations
│   │   └── env.ts          # Environment setup
│   └── utils/              # Utility functions
├── tests/                   # Test suites
│   ├── unit/               # Unit tests
│   ├── integration/        # Integration tests
│   └── e2e/                # End-to-end tests
├── docs/                    # Documentation
│   ├── README.md           # Project documentation
│   ├── API.md              # API documentation
│   └── deployment.md       # Deployment guide
├── examples/                # Usage examples
├── .env.example            # Environment template
├── package.json            # Dependencies and scripts
├── tsconfig.json           # TypeScript configuration
└── docker-compose.yml      # Docker setup

🎨 Template Categories

🟢 Basic Templates

• Single agent setup
• Simple conversation flow
• Basic tool integration

🟡 Advanced Templates

• Multi-agent systems
• Complex workflows
• Custom tool development

🔴 Production Templates

• Enterprise-ready setup
• CI/CD integration
• Monitoring and logging
• Security best practices

🧪 Testing & Debugging

🔍 Built-in Testing Tools

Unit Testing

// Automatic test generation
describe('MyAgent', () => {
    it('should handle basic queries', async () => {
        const agent = new MyAgent();
        const response = await agent.handleMessage('Hello');
        expect(response).toBeDefined();
    });
});

Integration Testing

• Multi-agent interaction tests
• Workflow validation
• Tool integration verification

Performance Testing

• Response time monitoring
• Memory usage tracking
• Concurrency testing

🐛 Debugging Features

• Breakpoint Support - Set breakpoints in agent code
• Step-through Debugging - Debug agent execution step-by-step
• Variable Inspection - Inspect agent state and variables
• Call Stack Analysis - Trace agent execution flow

🚀 Deployment Options

☁️ Cloud Platforms

AWS Deployment

# Using AWS Lambda
npm run deploy:aws

Google Cloud Deployment

# Using Cloud Functions
npm run deploy:gcp

Azure Deployment

# Using Azure Functions
npm run deploy:azure

🐳 Container Deployment

# Generated Dockerfile
FROM node:18-alpine
WORKDIR /app
COPY . .
RUN npm install && npm run build
CMD ["npm", "start"]

🏠 Local Development

# Development server
npm run dev

# Production build
npm run build && npm start

🔧 Advanced Features

🎛️ Agent Dashboard

• Real-time Metrics - Monitor agent performance live
• Visual Workflows - See agent interactions graphically
• Resource Usage - Track CPU, memory, and API usage
• Alert System - Get notified of issues or anomalies

📈 Analytics & Monitoring

• Performance Metrics - Response time, throughput, error rates
• Usage Statistics - API calls, user interactions, resource consumption
• Custom Dashboards - Create custom monitoring views
• Export Capabilities - Export data for external analysis

🔒 Security Features

• API Key Management - Secure storage and rotation
• Access Control - Role-based permissions
• Audit Logging - Track all agent activities
• Compliance - GDPR, SOC2 compliance helpers

🤝 Contributing

We welcome contributions from the community! Here's how to get involved:

🐛 Bug Reports

1. Check existing issues on GitHub
2. Create detailed bug report with reproduction steps
3. Include system information and extension version

💡 Feature Requests

1. Discuss new features in GitHub Discussions
2. Create feature request with use case and requirements
3. Consider contributing implementation

🔧 Development

# Clone repository
git clone https://github.com/ai-agent-studio/vscode-extension
cd vscode-extension

# Install dependencies
npm install

# Start development
npm run watch

# Run tests
npm test

# Build extension
npm run package

📝 Documentation

• Improve existing documentation
• Add framework-specific guides
• Create video tutorials
• Translate documentation

🆘 Support & Community

💬 Get Help

• GitHub Issues - Bug reports and feature requests
• GitHub Discussions - Questions and community support
• Discord Server - Real-time chat with developers and users
• Documentation - Comprehensive guides and API references

🌟 Community Resources

• Example Projects - Community-contributed examples
• Blog Posts - Development tips and best practices
• Video Tutorials - Step-by-step guides
• Webinars - Live development sessions

📧 Contact

• Email: support@ai-agent-studio.com
• Twitter: @aiagentStudio
• LinkedIn: AI Agent Studio

📄 License

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

🔄 Roadmap & Updates

🎯 Current Version (1.0.0)

• ✅ 10+ framework support
• ✅ Project templates and snippets
• ✅ Context7 integration
• ✅ Agent monitoring and testing
• ✅ Visual dashboard and flow visualizer

🚀 Upcoming Features (1.1.0)

• 🔄 More framework integrations
• 🔄 Advanced debugging tools
• 🔄 Team collaboration features
• 🔄 Cloud IDE integration
• 🔄 Mobile agent development

🌟 Future Plans (2.0.0)

• 🔄 Visual agent builder (drag-and-drop)
• 🔄 AI-powered code generation
• 🔄 Marketplace for agent components
• 🔄 Enterprise features
• 🔄 Multi-language support

🏆 Recognition

📊 Stats

• 10+ Supported frameworks
• 25+ Project templates
• 50+ Code snippets
• 100+ Example projects

🥇 Awards & Recognition

• VS Code Extension of the Month (Coming Soon)
• Developer Choice Award (Coming Soon)
• Community Favorite (Coming Soon)