Agent AI Use-cases Example

Agent AI Use-cases Example

A Streamlit-powered application showcasing multiple agentic AI use cases using LangGraph, Tavily, OpenAI, Groq, and LangChain.

GitHub Repository: agentic-ai-chatbot

Live Demo: hitesh-s0lanki-agentic-ai-chatbot-app

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🚀 Features

• Streamlit UI: Fast, interactive interface for exploring AI use cases.
• LLM Integrations: OpenAI (gpt-4o), Groq via langchain-groq.
• Tool Calls: News & web search using Tavily.
• Agent Framework: Built with LangGraph and LangChain for stateful, tool-enabled agents.
• Organized File Structure: Modular components and clear directory layout for easy extension.

💡 Use Cases

1. Basic Chatbot – Stateful conversational interface.
2. Chatbot with Tool – Calls external tools to fetch and display recent news.
3. AI News – Curated AI news explorer.
4. Weekly AI News Summary – Automated weekly recap of AI developments.

🛠 Tech Stack

• Python 3.9+
• Streamlit
• OpenAI Python SDK
• Groq LLM SDK (via langchain-groq)
• LangChain & LangGraph
• Tavily API for web/news retrieval

📦 Installation

git clone https://github.com/Hitesh-s0lanki/agentic-ai-chatbot.git
cd agentic-ai-chatbot
pip install -r requirements.txt

⚙️ Configuration

Create a .env file in the project root:

OPENAI_API_KEY=your_openai_api_key
TAVILY_API_KEY=your_tavily_api_key
groq_api_key=your_groq_api_key

▶️ Usage

streamlit run app.py

• Select your LLM provider and model.
• Choose a use case from the sidebar.
• Enter your API keys.
• Interact with the agent in the main panel.

📂 Project Structure

agentic-ai-chatbot/
├── app.py              # main file
├── components/ui       # UI components and layouts
├── components/nodes    # Individual node use-case implementations
├── components/graph    # Graph builder Function
├── requirements.txt    # Python dependencies
└── README.md           # This file

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to fork the repo and submit a pull request.

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Enjoy exploring Agent AI!