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

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2. System Architecture

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

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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.

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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

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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.

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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

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7. Demo Videos

Demo videos are available in the following directory:

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

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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.

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