MOODLEAGENT

Empowering Smarter Learning Through AI

Built with the tools and technologies:

Overview

MoodleAgent is an all-in-one automation tool designed to streamline educational content management within Moodle. It leverages web automation, PDF processing, and AI-driven analysis to facilitate data extraction, document summarizing, and interactive flash-cards.

What is MoodleAgent?
This project simplifies workflows by integrating multiple tools for web scraping, PDF content extraction, OTP management, and AI interactions.

The core features include:

• 🔍 Web & PDF Automation: Automates navigation and content extraction from Moodle courses, including PDF text processing.
• 🧠 AI Content Analysis: Generates summaries, flashcards, and quizzes using multiple AI providers for flexible insights.
• 🎛️ User Interface: Provides an intuitive web interface for configuring tasks, monitoring progress, and reviewing results.
• 🔑 OTP Migration: Facilitates secure transfer of 2FA secrets, enhancing authentication management.
• ⚙️ Environment Management: Ensures all dependencies are installed for scalable, robust functionality across the project.

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Getting Started

Prerequisites

• Python 3.12
• pip
• Internet access (for Moodle + AI APIs)
• Appropriate Moodle credentials (user, password; possibly permission to download course resources)
• API keys / credentials for AI provider(s) you intend to use

Installation

git clone https://github.com/ShadowsAdi/MoodleAgent.git
cd MoodleAgent
pip install -r requirements.txt

Configuration

1. Copy .env.example to .env
2. Fill in necessary environment variables:
   • Moodle login credentials (URL, username, password, OTP secret if needed)
   • AI API key(s)

Running the Application

python app.py

• Access the web UI (default: http://127.0.0.1:8000)
• From UI, configure tasks (e.g. which Moodle course(s), what content to extract), trigger or schedule them.

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Usage

Here are a few example workflows:

• Generate flashcards from PDF lectures:

  1. Give MoodleAgent the name of course folder.
  2. It searches for lecture's PDF, extracts text.
  3. Uses AI to create flashcards automatically or generate a quiz.

• Summarize a course module:
  Extract all lesson pages + PDFs, produce a summary document (or set of summaries per section) to help students review.

• Backup / migrate 2FA secrets:
  Use the OTP secret extractor to export or migrate OTP secrets safely. See Google Authenticator Exporter

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MoodleAgent REST API Usage

The Flask app exposes several REST endpoints that external systems can call to trigger automations, check progress, and retrieve results.

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🔹 1. Start Automation

Endpoint:

POST /start_automation

Description:
Starts a new Moodle automation task (summary, flashcards, or quiz generation).

Request Parameters (form-data / x-www-form-urlencoded):

Parameter	Type	Required	Description
course_name	string	✅ Yes	Name of the Moodle course
subject_name	string	✅ Yes	Name of the subject within the course
action_type	string	❌ No	Task type: "summary", "flashcards", "quiz" (default: "summary")
ai_provider	string	❌ No	AI backend: "google" or "openai" (default: "google")

Example Request (cURL):

curl -X POST http://localhost:8000/start_automation   -d "course_name=Computer Science"   -d "subject_name=AI Fundamentals"   -d "action_type=summary"   -d "ai_provider=openai"

Example Response:

{
  "message": "Started successfully"
}

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🔹 2. Get Task Status

Endpoint:

GET /status

Description:
Returns the current state of the running (or last) automation task.

Example Request:

curl http://localhost:8000/status

Example Response:

{
  "running": true,
  "progress": 35,
  "message": "Extracting PDF content...",
  "result": null,
  "error": null,
  "action_type": "summary"
}

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🔹 3. Get Task Result

Endpoint:

GET /result

Description:
Fetches the result of the last task.

• For summary → returns HTML rendered summary
• For flashcards or quiz → returns JSON content
• If no result yet, redirects to /

Example Request:

curl http://localhost:8000/result

(Note: This returns rendered HTML — better accessed via browser or embedded in another system.)

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🔹 4. Reset Task State

Endpoint:

POST /reset

Description:
Resets the internal task state (useful for starting fresh).

Example Request:

curl -X POST http://localhost:8000/reset

Example Response:

{
  "message": "Data reset successfully"
}

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Typical Workflow

1. Trigger a task → POST /start_automation
2. Poll for progress every seconnd → GET /status
3. Retrieve result → GET /result
4. (Optional) Reset state → POST /reset

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TO DOs

1. Improve Agent's Memory

   • Implement a more persistent memory layer (e.g., vector database or file-based context).
   • Enable contextual recall across multiple runs.

2. Switch to Ollama's Python API

   • Replace direct OpenAI calls with Ollama’s Python API.

3. Enhance Agentic Capabilities

   • Add more tools (web search, file operations, scheduling).
   • Support flexible tool orchestration (planning + execution).

4. Extend REST API

   • Add endpoints for managing tasks, retrieving logs, and exporting results (JSON/PDF).
   • Provide authentication (API keys, JWT, etc).

5. Deliver a Docker Image

   • Create a production-ready Dockerfile.
   • Publish prebuilt images to Docker Hub.

6. Maybe Switching to LangChain

   • Compare current architecture with LangChain’s agents & memory.
   • Prototype a LangChain integration for multi-tool workflows.

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