AI Staff Development Agent 🤖

A modern platform for creating, managing, and distributing AI agents that can replace traditional offshore staffing roles.

🚀 Project Overview

AI Staff Development Agent (AiStaff) is a revolutionary platform with two core divisions:

1. 🏢 Enterprise Division

   • A managed service providing custom AI agents to businesses looking to replace or augment their offshore teams
   • Specialized agents that can perform specific job functions with consistency and reliability

2. 🛒 Marketplace Division

   • An "App Store" for AI agents where users can buy, sell, and distribute pre-built agents
   • Fosters an ecosystem of specialized AI workers for various business needs

✨ Key Features

• Agent Creation - Build customized AI agents with specific capabilities
• Agent Management - Monitor, control, and update your AI workforce
• Capability Selection - Choose from a variety of specialized skills for each agent
• Intuitive Dashboard - Easy-to-use interface for managing your AI staff
• API Integration - Connect your agents with existing business systems

🛠️ Technology Stack

Backend

• FastAPI - High-performance Python web framework
• SQLAlchemy - SQL toolkit and ORM
• Pydantic - Data validation and settings management
• SQLite (dev) / PostgreSQL (prod) - Database

Frontend

• React 18 - Modern UI library with hooks
• TypeScript - Type-safe JavaScript
• Chakra UI - Component library for consistent design
• React Router - Navigation and routing
• Axios - API client for data fetching

📋 Prerequisites

• Python 3.9 or newer
• Node.js 16.x or newer
• npm 8.x or newer

🚀 Quick Start

Backend Setup

# Clone the repository
git clone /~https://github.com/yourusername/ai-staff-dev-agent.git
cd ai-staff-dev-agent

# Create and activate a virtual environment
python -m venv venv

# On Windows
venv\Scripts\activate
# On macOS/Linux
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run the backend server
uvicorn backend.main:app --reload

The API will be available at: http://localhost:8000

API Documentation: http://localhost:8000/docs

Frontend Setup

# Navigate to the frontend directory
cd frontend

# Install dependencies
npm install

# Run the development server
npm run dev

The frontend will be available at: http://localhost:3000

📊 Project Structure

ai-staff-dev-agent/
├── backend/                 # Backend Python code
│   ├── api/                 # API endpoints
│   ├── core/                # Core intelligence
│   │   ├── intelligence.py  # Intelligence engine
│   │   └── factory.py       # Agent factory
│   ├── agents/              # Agent definitions
│   ├── db/                  # Database models
│   └── services/            # Business logic
├── frontend/                # Frontend React code
│   ├── src/
│   │   ├── components/      # UI components
│   │   ├── pages/           # Page components
│   │   ├── services/        # API services
│   │   └── hooks/           # Custom React hooks
└── private/                 # Protected IP
    ├── config/              # Private configuration
    └── templates/           # Agent templates

🔍 Core Components

Intelligence Engine

The central brain that powers all AI agents, providing various capabilities like:

• Text Processing
• Data Analysis
• Customer Service
• Code Generation
• Automation

Agent Factory

Creates standardized agent instances with selected capabilities, ensuring consistent behavior across the platform.

Agent Manager

Handles the lifecycle of agents, including creation, monitoring, and termination.

📝 API Documentation

The API documentation is automatically generated and available at /docs when the backend is running.

Key endpoints:

• GET /api/agents - List all agents
• POST /api/agents - Create a new agent
• GET /api/agents/{id} - Get details for a specific agent
• DELETE /api/agents/{id} - Delete an agent
• GET /api/agents/capabilities/available - Get available capabilities

🌐 Deployment

Backend Deployment

# Production deployment using Gunicorn
pip install gunicorn
gunicorn -w 4 -k uvicorn.workers.UvicornWorker backend.main:app

Frontend Deployment

# Build for production
cd frontend
npm run build

# The build files will be in the dist/ directory
# These can be served by any static file server

🔧 Troubleshooting

Common Issues

Backend Issues

• Missing Modules: Ensure all __init__.py files are present in your directory structure
• Database Errors: Check that SQLite file permissions are correct and path is valid
• Import Errors: Verify your virtual environment is activated and all packages are installed

Frontend Issues

• Module Not Found: Run npm install to ensure all dependencies are installed
• Type Errors: Make sure vite-env.d.ts is present in the frontend/src directory
• Build Errors: Clear the build cache with npm run clean (if available) or delete the dist directory

Solutions to Common Errors

• Run npm install @types/react @types/react-dom if you encounter React type issues
• Create empty __init__.py files in all subdirectories of the backend folder
• Check CORS settings if the frontend can't connect to the backend

🔮 Future Roadmap

• Agent Marketplace - Buy and sell pre-configured agents
• Natural Language Interface - Configure agents using conversational language
• Performance Analytics - Track and optimize your AI staff performance
• Multi-Modal Capabilities - Support for voice, image, and video processing
• Workflow Integration - Connect agents to create complex business processes

🤝 Contributing

Contributions, issues and feature requests are welcome! Feel free to check the issues page.

Development Workflow

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

📜 License

This project is MIT licensed.

📞 Contact

If you have any questions or need support, please reach out to:

• Project Maintainer: Your Team
• Email: team@aistaff.example.com
• Website: aistaff.example.com

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Made with ❤️ by Your Organization - Transforming the future of work with AI