Agenix - An AI Agent Marketplace

Prototype Demo: https://youtu.be/sq5AJ2MbMgY?si=hHuUwBSU3L0Kaa_1

🚀 Project Overview

Agenix is an advanced AI-powered marketplace platform that integrates multiple specialized AI agents to provide comprehensive marketing solutions. The platform combines a modern React frontend with a powerful Python backend, enabling users to create, configure, and execute complex marketing workflows seamlessly.

🎯 Key Features

Workflow Management

• Drag-and-Drop Interface: Intuitive workflow builder with visual drag-and-drop functionality
• Sequential Processing: Agents execute in user-defined order with real-time progress tracking
• Dynamic Forms: Automatically generated input forms based on selected agents
• Real-time Results: Instant visualization of agent outputs with markdown support
• Multi-Agent Support: Integration with various specialized AI agents

AI-Powered Agents

1. Email Generator Agent

   • Automated email content generation
   • Personalized email campaigns
   • A/B testing support

2. Case Study Agent

   • Automated case study generation
   • Data-driven insights
   • Visual report generation

3. Doc Summarizer

   • Intelligent document summarization
   • Key point extraction
   • Content analysis

4. Job Agent

   • Automated job posting analysis
   • Candidate matching
   • Market trend analysis

5. Web Crawler

   • Automated web data collection
   • Content scraping
   • Data analysis

6. YT Summarizer

   • YouTube video analysis
   • Content summarization
   • Trend identification

🛠️ Installation Guide

Prerequisites

• Node.js (v18 or higher)
• Python 3.9+
• npm (Node Package Manager)
• Git

Frontend Setup

1. Clone the repository:

   git clone https://github.com/ddihora1604/MercadoVista.git
   cd MercadoVista

2. Install dependencies:

   npm install

3. Create a .env file in the root directory with the following variables:

   NEXT_PUBLIC_API_URL=http://localhost:5000
   

4. Start the development server:

   npm run dev

5. Open http://localhost:3000 in your browser

Backend Setup

1. Navigate to the backend directory:

   cd marketing_ai

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Python dependencies:

   pip install -r requirements.txt

4. Start the backend server:

   python app.py

📁 Project Structure

Agenix/
├── .env                     # Environment configuration
├── .eslintrc.json          # ESLint configuration
├── .git/                    # Git repository files
├── .gitignore              # Git ignore rules
├── .next/                   # Next.js build output
├── CaseStudyAgent/         # Case study generation agent
│   └── src/                # Agent source code
├── DocSummarizer/          # Document summarization agent
│   ├── src/               # Agent source code
│   └── tests/             # Unit tests
├── Email_Generator_Agent/  # Email generation agent
│   ├── src/               # Agent source code
│   ├── templates/         # Email templates
│   └── tests/             # Unit tests
├── Fluxai/                 # AI orchestration module
│   ├── src/               # Core AI orchestration code
│   ├── models/            # AI model configurations
│   └── utils/             # Utility functions
├── JobAgent/               # Job analysis agent
│   ├── src/               # Agent source code
│   ├── data/              # Sample job data
│   └── tests/             # Unit tests
├── YTSummarizer/          # YouTube video analysis agent
│   ├── src/               # Agent source code
│   ├── data/              # Sample video data
│   └── tests/             # Unit tests
├── blog/                   # Blog content management
│   ├── content/          # Blog posts
│   ├── templates/        # Blog templates
│   └── static/           # Blog static assets
├── marketing_ai/          # Main backend directory
│   ├── app.py           # Main backend application
│   ├── config/          # Backend configuration
│   ├── models/          # Database models
│   ├── routes/          # API routes
│   └── utils/           # Utility functions
├── public/                # Static assets
│   ├── images/          # Project images
│   ├── fonts/           # Web fonts
│   └── icons/           # UI icons
├── scripts/               # Utility scripts
│   └── create-sample-markdown.js  # Sample markdown generator
├── src/                   # Frontend source code
│   ├── components/      # React components
│   ├── pages/          # Next.js pages
│   ├── styles/         # CSS/SCSS files
│   ├── utils/          # Utility functions
│   └── hooks/          # Custom React hooks
├── webcrawler/           # Web scraping module
│   ├── src/            # Crawler source code
│   ├── config/         # Crawler configuration
│   └── tests/          # Unit tests
├── eslint.config.js     # ESLint configuration
├── next-env.d.ts       # Next.js TypeScript configuration
├── next.config.js      # Next.js configuration
├── package-lock.json   # Node package dependencies
├── package.json        # Frontend dependencies
├── postcss.config.js   # PostCSS configuration
├── tailwind.config.js  # Tailwind CSS configuration
├── tsconfig.json       # TypeScript configuration
└── README.md          # Project documentation

🛠️ Technical Stack

Frontend

• Framework: Next.js 14
• Styling: Tailwind CSS
• State Management: React Context
• UI Components: Custom components with Tailwind
• Data Visualization: Chart.js
• Linting: ESLint
• Type Checking: TypeScript

Backend

• Framework: FastAPI
• AI Integration: CrewAI
• Language Models: OpenAI GPT-4
• Data Processing: Pandas
• Web Scraping: BeautifulSoup
• Task Scheduling: Celery

📝 Usage Guide

1. Getting Started

   • Navigate to the Dashboard
   • Select your desired workflow type
   • Configure agents using drag-and-drop interface
   • Set up required parameters

2. Creating Workflows

   • Drag agents from the sidebar
   • Arrange them in desired order
   • Connect agents using workflow builder
   • Save your workflow configuration

3. Executing Workflows

   • Select your saved workflow
   • Fill in required input parameters
   • Click "Execute" to run analysis
   • View real-time results in the dashboard

4. Managing Results

   • View markdown reports
   • Export results
   • Generate visualizations
   • Save workflow configurations