AutoContentor: AI-Powered Content Research Automation

AutoContentor is a sophisticated, multi-agent platform designed to automate the entire content research process. By leveraging Google's Agent Development Kit (ADK), it coordinates a team of specialized AI agents to perform in-depth analysis on keywords, audience demographics, competitor strategies, and market trends. The result is a comprehensive, structured report that provides actionable insights for content creators, marketers, and strategists.

✨ Features

• Multi-Agent System: A team of specialized agents for keywords, audience, competitors, and trends.
• Automated Research: Automates the entire content research workflow, from data gathering to report generation.
• Comprehensive Reports: Generates detailed reports including audience personas, SWOT analyses, and keyword metrics.
• Scalable Architecture: Built on a lightweight, file-based system that is easy to deploy and scale.
• Developer-Friendly: Run all services locally with a single command using the ADK Web UI for real-time monitoring.
• Docker Support: Optional Docker Compose setup available for containerized deployments.

🏛️ Architecture

AutoContentor is built on a microservices-style architecture, with each agent operating as a separate service. The Orchestrator acts as the central hub, managing research campaigns and delegating tasks to the specialized agents. The system is designed to be stateless and uses a file-based approach for data persistence, making it highly portable and easy to manage.

🚀 Getting Started

Follow these steps to get AutoContentor up and running on your local machine for development.

Prerequisites

• Python 3.11+
• Pip

Installation

1. Clone the repository:

   git clone https://github.com/your-username/AutoContentor.git
   cd AutoContentor

2. Set up a virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Configure your environment:

   • Copy the example environment file:

     cp .env.example .env

   • Open the .env file and add your API keys and any other necessary configurations.

5. Run the ADK Web UI:

   adk web

   This will start all the backend services and the developer UI.

⚙️ Configuration

The following environment variables need to be set in your .env file:

Variable	Description
GOOGLE_API_KEY	Your Google API key for Trends, Search, etc.
GOOGLE_SEARCH_ENGINE_ID	Your Google Custom Search Engine ID.
OPENAI_API_KEY	Your OpenAI API key.
GEMINI_API_KEY	Your Gemini API key.
SERPAPI_KEY	Your SerpAPI key for search results.
SECRET_KEY	A secret key for securing the application.

Usage

Once the services are running via adk web, you can interact with the system through the ADK Web UI or by sending API requests directly.

Using the ADK Web UI

1. Open your browser and navigate to the ADK Web UI (typically http://127.0.0.1:8000).
2. Use the interface to start a new campaign and monitor the real-time logs and events from the agents.

Using the API

You can start a new research campaign by sending a POST request to the Orchestrator's API.

• Endpoint: http://127.0.0.1:8000/api/start-campaign
• Method: POST
• Body:

  {
    "campaignId": "your-campaign-id",
    "seedKeywords": ["keyword1", "keyword2"],
    "competitorList": ["competitor1.com", "competitor2.com"],
    "region": "US"
  }

The Orchestrator will then start the research process.

📁 Project Structure

AutoContentor/
├── .env.example                # Environment variables template
├── docker-compose.yaml         # Docker Compose configuration
├── pyproject.toml              # Project dependencies and metadata
├── src/
│   └── auto_contentor/
│       ├── orchestrator/       # Orchestrator service
│       ├── agents/             # Specialized agent services
│       │   ├── keyword/
│       │   ├── audience/
│       │   ├── competitor/
│       │   ├── trend/
│       │   └── aggregator/
│       └── shared/             # Shared libraries and utilities
└── ui/                         # Frontend application (Next.js)

🗺️ Roadmap

• Implement real-time progress updates on the frontend.
• Add support for more data sources and APIs.
• Enhance report generation with more customization options.
• Develop a user authentication system.
• Add comprehensive test coverage.

📄 License

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