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AI Student Helper

1. Introduction

AI Student Helper is a multi-agent automation system built with LangGraph to assist students and professionals in automating academic and career-related tasks. It follows a graph-based workflow execution model, ensuring efficient task routing and execution.

Refer https://nselvar.github.io/ai-student-helper/ for in depth tech document

2. System Architecture

Architecture Diagram

Architecture Diagram The AI Student Helper follows a modular multi-agent architecture, where:

  • A Supervisor Agent acts as a task router.
  • Specialized Agents execute specific tasks based on user input.
  • A graph-based workflow (LangGraph) dynamically determines execution paths.

2.1 High-Level Diagram

Flow Diagram

3. Key Components

3.1 Supervisor Agent

  • Role: Routes user requests to the correct specialized agent.
  • Logic:
    • Analyzes user input using an LLM.
    • Keyword-based mapping triggers corresponding agents.
    • Returns processed results to the user.

3.2 Specialized Agents

Each agent operates independently, executing tasks based on LLM processing, automation scripts, and external API interactions.

📄 PDF Summarizer (pdf_agent)

  • Prompts the user for a PDF file path.
  • Extracts text content using PyMuPDF or pdfplumber.
  • Uses an LLM-based summarization function to generate key points.
  • Returns a structured summary.

🎥 YouTube Video Summarizer (youtube_agent)

  • Uses pytube to download transcripts (if available).
  • If no transcript is available, leverages Whisper for speech-to-text conversion.
  • Summarizes content using LangGraph-based AI model.

💼 Job Application Assistant (job_agent)

  • Extracts resume information.
  • Uses Selenium or LinkedIn API to automate job applications.
  • Matches jobs based on LLM-powered profile analysis.

📚 Homework Helper (homework_agent)

  • Uses Streamlit for an interactive UI.
  • Implements LLM-based step-by-step problem-solving.
  • Provides explainable responses for better understanding.

4. Technologies Used

Technology Role
Python Core programming language
LangGraph Manages workflow execution
ChatOpenAI Provides AI-powered responses
Streamlit Enables interactive UI
Selenium Automates job applications
pytube Extracts YouTube video transcripts
PyMuPDF Parses PDF files

5. Workflow Execution

5.1 Request Handling Flow

  1. User enters a request (e.g., "summarize a PDF").
  2. Supervisor Agent extracts intent.
  3. Relevant agent is activated.
  4. Task is processed, and output is generated.
  5. Response is returned to the user.

5.2 Graph Execution with LangGraph

  • Each node in the graph represents an agent.
  • The edges define execution logic based on user input.
  • This structure allows dynamic branching without unnecessary processing.

6. Deployment

6.1 Local Setup

Prerequisites

  • Python 3.8+
  • OpenAI API Key
  • Required Python libraries

Installation Steps

  1. Clone the repository:

    git clone https://github.com/your-repo/ai-student-helper.git
cd ai-student-helper

  2. Install dependencies:

    pip install -r requirements.txt

  3. Run the application:

    python3 SupervisorLangGraph.py

Environment Setup

  • Set the OpenAI API key: 
    export OPENAI_API_KEY="your_open_ai_key"
  • Configure LinkedIn credentials in config.yml.
  • Execute the main script: 
    python3 SupervisorLangGraph.py

7. Demo Videos

Demo videos are available in the following directory:

https://github.com/nselvar/AIStudentHelper/tree/main/demo

8. Conclusion

The AI Student Helper is a multi-agent automation system that efficiently handles student and professional tasks using AI-based interactions. It leverages LangGraph, LangChain, and LLM-powered processing to automate complex workflows.