♊ Multi-Agent Code Explainer & Optimizer

An AI-driven multi-step workflow that automatically explains or optimizes code using LangGraph and Gemini (Google Generative AI).
This project demonstrates how multi-agent systems can dynamically route tasks — first analyzing user intent, then executing the appropriate agent to deliver results.

---

✨ Features

• 🧠 Intent Detection: Determines if the user wants explain or optimize for any code snippet.
• 💬 Code Explanation Agent: Provides clear, line-by-line explanations.
• ⚙️ Optimization Agent: Refactors code for efficiency, readability, and best practices.
• 🔁 Conditional Workflow: Built with LangGraph for modular multi-step AI processing.
• 🌐 Streamlit UI: Interactive front-end for running and visualizing results.

---

🧠 Tech Stack

Component	Purpose
Python	Core programming language
LangGraph	Multi-agent workflow graph
LangChain	LLM prompt handling
Gemini (Google Generative AI)	Code explanation and optimization
Streamlit	Web-based user interface
dotenv	Securely load environment variables

---

⚙️ Installation

1️⃣ Clone the Repository

git clone https://github.com/yourusername/multi-agent-code-explainer-optimizer.git
cd multi-agent-code-explainer-optimize

2. Create a Virtual Environment

# Create venv
python -m venv venv

# Activate venv
# Linux / Mac
source venv/bin/activate
# Windows
venv\Scripts\activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Setup Environment Variables

GOOGLE_API_KEY=your_google_gemini_api_key_here

5️⃣ Run the App

streamlit run app.py

🎥 Project Demo

[![Watch the video](https://img.shields.io/badge/▶️%20Watch%20Demo-FF0000?style=for-the-badge)](https://drive.google.com/file/d/1DFpgUT_9_BfHUOYKzKfSp-r-_Dy3uB7n/view?usp=drive_link)