AI / ML: Agentic AI

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🎥 AI YouTube Video Analyzer

Agentic AI System for Structured Video Understanding.

An Agentic AI-powered application that analyzes YouTube videos and generates structured summaries, timestamps, and key insights using LLM reasoning and tool usage.

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📌 Project Overview

The AI YouTube Video Analyzer is an Agentic AI application designed to transform unstructured video content into structured, searchable knowledge.

Using Large Language Models (LLMs) and tool-based agents, the system automatically:

• Extracts video metadata
• Segments content into meaningful sections
• Generates timestamped summaries
• Identifies key concepts and demonstrations

This project demonstrates modern AI engineering patterns, including:

• Agent-based architectures
• Tool-augmented LLM reasoning
• AI-driven content analysis
• Interactive ML applications

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🚀 Key Features

🎥 Automated Video Analysis

Provide any YouTube link and receive a structured analysis of the video.

⏱ Intelligent Timestamp Generation

Breaks long videos into semantic segments with summaries.

🧠 LLM-powered Understanding

Uses Groq-hosted Llama-3.3-70B for deep contextual reasoning.

📚 Multi-Domain Content Support

Content Type	Supported
Programming Tutorials	✅
Academic Lectures	✅
Tech Reviews	✅
Creative Tutorials	✅
Educational Videos	✅

🖥 Interactive Web Interface

Built with Streamlit for simple and intuitive usage.

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🧠 System Architecture

              ┌──────────────────────────┐
              │        User Input        │
              │   YouTube Video URL      │
              └─────────────┬────────────┘
                            │
                            ▼
                ┌────────────────────┐
                │   Streamlit UI     │
                │   (ui.py)          │
                └─────────┬──────────┘
                          │
                          ▼
              ┌───────────────────────────┐
              │     Agentic AI System     │
              │   (youtube_analyzer.py)   │
              └───────────┬───────────────┘
                          │
        ┌─────────────────┴─────────────────┐
        ▼                                   ▼
┌─────────────────┐              ┌──────────────────┐
│  YouTube Tools  │              │   Groq LLM       │
│ (Metadata +     │              │ Llama 3.3 70B    │
│ Video Content)  │              │ Reasoning Engine │
└────────┬────────┘              └─────────┬────────┘
         │                                  │
         └──────────────┬───────────────────┘
                        ▼
            ┌────────────────────────┐
            │ Structured Video Report│
            │ Timestamps + Insights  │
            └─────────────┬──────────┘
                          ▼
                 Displayed in UI

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🖼 Demo

Application Interface

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Example Output

📚 Video Overview
Length: 18 minutes
Type: Python Tutorial

⏱ Timestamp Breakdown

[00:00 - 02:15]
Introduction to Python and environment setup.

[02:16 - 07:40]
Installing Python and configuring IDE.

[07:41 - 12:30]
Writing the first Python program.

[12:31 - 18:10]
Understanding variables and data types.

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🎬 Demo GIF

(You can record this using ScreenToGif or OBS and place it inside a /docs folder.)

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🧰 Technology Stack

Component	Technology
Programming Language	Python
UI Framework	Streamlit
LLM Provider	Groq
Model	Llama-3.3-70B
Agent Framework	Agno
Video Tools	YouTubeTools
Environment Management	python-dotenv

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📂 Project Structure

Video Analyzer
│
├── .gitignore
├── README.md
│
├── ui.py
│   └── Streamlit interface for user interaction
│
├── youtube_analyzer.py
│   └── Agent configuration and analysis pipeline
│
└── docs
    ├── demo_ui.png
    └── demo.gif

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⚙️ Installation

1️⃣ Clone Repository

git clone https://github.com/yourusername/youtube-video-analyzer.git
cd youtube-video-analyzer

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2️⃣ Create Virtual Environment

python -m venv venv

Activate it:

Windows

venv\Scripts\activate

Mac / Linux

source venv/bin/activate

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3️⃣ Install Dependencies

pip install streamlit python-dotenv agno

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4️⃣ Configure Environment Variables

Create .env file:

GROQ_API_KEY=your_groq_api_key

You can obtain the key from:

https://console.groq.com

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▶️ Running the Application

Start the Streamlit server:

python -m streamlit run ui.py

Open in browser:

http://localhost:8501

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🧪 Example Use Cases

📚 Educational Content

Create structured study guides from lectures.

💻 Programming Tutorials

Extract code explanations and practical demonstrations.

📱 Tech Reviews

Identify features, benchmarks, and comparisons.

🎨 Creative Tutorials

Break down art or DIY workflows step-by-step.

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🔬 AI Engineering Concepts Demonstrated

This project demonstrates several modern AI system design concepts:

• Agentic AI architecture
• Tool-augmented LLM workflows
• LLM orchestration
• Structured knowledge extraction
• Interactive AI applications

These are key skills sought by AI/ML recruiters and AI product teams.

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🔮 Future Improvements

• 🎙 Automatic speech transcription
• 📊 Knowledge graph extraction
• 📥 Export summaries as PDF
• 📚 Playlist analysis
• 🧠 Multi-agent analysis pipeline

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🤝 Contributing

Contributions are welcome.

1. Fork repository
2. Create feature branch

git checkout -b feature/new-feature

3. Commit changes

git commit -m "Add new feature"

4. Push branch and create Pull Request.

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📜 License

This project is licensed under the MIT License.

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👤 Author

Satinder Singh Sall

AI / Machine Learning Developer Focused on Agentic AI, LLM Applications, and Intelligent Systems

GitHub: https://github.com/SatinderSinghSall

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⭐ Support

If you found this project useful:

⭐ Star the repository 🍴 Fork the project 📢 Share with others

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🎥 AI YouTube Video Analyzer

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🎥 AI YouTube Video Analyzer

An AI-powered YouTube Video Analyzer built using Agentic AI, Groq LLM, and Streamlit. This tool analyzes any YouTube video and automatically generates a structured breakdown with timestamps, summaries, and key insights.

It is especially useful for:

• 📚 Students analyzing lectures
• 💻 Developers studying tutorials
• 📱 Tech enthusiasts reviewing product videos
• 🎨 Creators studying creative workflows

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🚀 Features

✅ Automatic Video Analysis Provide any YouTube video URL and the AI will analyze the content.

✅ Smart Timestamp Generation Breaks the video into meaningful sections with timestamps.

✅ Structured Content Breakdown

• Video overview
• Topic segmentation
• Key learning points
• Practical demonstrations

✅ Multiple Content Types Supported

• 📚 Educational lectures
• 💻 Programming tutorials
• 📱 Tech reviews
• 🎮 Gaming videos
• 🎨 Creative content

✅ Interactive UI with Streamlit

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🧠 Tech Stack

• Python
• Streamlit – UI framework
• Groq LLM (Llama 3.3 70B) – Language model
• Agno Agent Framework – Agentic AI system
• YouTube Tools (Agno) – Extracts video data
• dotenv – Environment variable management

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📂 Project Structure

Video Analyzer
│
├── .gitignore
├── README.md
├── ui.py                 # Streamlit user interface
└── youtube_analyzer.py   # AI agent setup and analysis logic

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⚙️ Installation

1️⃣ Clone the Repository

git clone https://github.com/yourusername/youtube-video-analyzer.git
cd youtube-video-analyzer

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2️⃣ Create a Virtual Environment

python -m venv venv

Activate it:

Windows

venv\Scripts\activate

Mac / Linux

source venv/bin/activate

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3️⃣ Install Dependencies

pip install streamlit python-dotenv agno

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4️⃣ Setup Environment Variables

Create a .env file in the project root.

GROQ_API_KEY=your_groq_api_key_here

You can get a key from: 👉 https://console.groq.com

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▶️ Running the Application

Run the Streamlit app:

python -m streamlit run ui.py

Then open the browser at:

http://localhost:8501

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🧪 How It Works

1️⃣ User enters a YouTube video URL 2️⃣ The Agentic AI system processes the video 3️⃣ The Groq LLM analyzes the content 4️⃣ The system generates:

• Video overview
• Timestamp breakdown
• Topic summaries
• Key insights

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📊 Example Output

📚 Video Overview
Length: 18 minutes
Type: Python Tutorial

⏱ Timestamp Breakdown

[00:00 - 02:15]
Introduction to Python and setup.

[02:16 - 07:40]
Installing Python and IDE configuration.

[07:41 - 12:30]
Writing the first Python program.

[12:31 - 18:10]
Understanding variables and data types.

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🎯 Use Cases

• Learn faster from long YouTube tutorials
• Generate study guides automatically
• Summarize technical talks
• Extract key insights from reviews
• Create structured notes from lectures

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🔮 Future Improvements

• 🔊 Audio transcription support
• 📊 Video summarization charts
• 🧠 Multi-video playlist analysis
• 📥 Export notes as PDF / Markdown
• 📑 Save timestamp summaries

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🤝 Contributing

Contributions are welcome!

1. Fork the repository
2. Create a feature branch

git checkout -b feature-name

3. Commit changes

git commit -m "Added new feature"

4. Push and open a Pull Request

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📜 License

This project is licensed under the MIT License.

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⭐ Support

If you find this project useful:

⭐ Star the repository 🍴 Fork it 🧑‍💻 Share it with others