# 🤖 Gemini AI Agent

Welcome to the **Gemini AI Agent** project!  
A lightweight, modular AI assistant powered by **Google Gemini API** and optimized with **`uv`** for blazing-fast performance. 🚀

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## 📁 Project Structure

📦 gemini-ai-agent/ ├── app.py # 🎯 Main app logic ├── gemini_agent.py # 🧠 Gemini API integration ├── main.py # 🚪 Entry point ├── pyproject.toml # 🛠️ Project configuration ├── requirements.txt # 📦 Python dependencies ├── uv.lock # 🔒 uv lock file └── README.md # 📖 Project documentation

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## 🔐 Gemini API Key Setup

To use the Gemini AI Agent, you need a valid **Google Gemini API key**:

1. 🔑 **Get your API key** from the [Google AI Studio](https://makersuite.google.com/app/apikey).
2. 🛠️ **Set your key as an environment variable**:

   On **Linux/macOS**:
   ```bash
   export GEMINI_API_KEY="your-api-key-here"

> ✅ The app will automatically pick up the API key from the `GEMINI_API_KEY` environment variable.

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## ⚡ Features

* 🔥 Super fast dependency management with [`uv`](https://github.com/astral-sh/uv)
* 🧠 Powered by Google **Gemini API** for intelligent completions
* 🧩 Clean and modular code architecture
* 🛠️ Easy to configure and extend

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## 🚀 Getting Started

### 1. Install `uv` (if not already installed)

```bash
curl -LsSf https://astral.sh/uv/install.sh | sh

Install dependencies

uv pip install -r requirements.txt

Export your Gemini API key

export GEMINI_API_KEY="your-api-key-here"

Run the app

python main.py

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🧪 Example Usage

Once running, the agent can interact with Gemini and return intelligent outputs. Check main.py or app.py for usage examples.

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📌 Requirements

• Python 3.9+
• uv (optional but recommended)
• A valid Google Gemini API key 🔐

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🛠️ Contributing

Found a bug or have a feature request? We welcome contributions! Fork this repo and submit a PR or open an issue. 🙌

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📄 License

MIT License — see the LICENSE file for full details.

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🌟 Support

If you like this project:

• ⭐ Star this repo on GitHub
• 🗣️ Share it with others
• 🧠 Build something cool with it!

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Perfect! Here's an updated macOS setup guide specifically for VS Code or Cursor (instead of PyCharm), since you're using those editors. I'll also show how to integrate your Gemini API key in a clean and dev-friendly way.

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🧠 Gemini AI Agent – macOS + VS Code / Cursor Setup Guide

✅ 1. Clone the Repository

Open Terminal (or use the built-in terminal in VS Code / Cursor) and run:

git clone https://github.com/your-username/gemini-ai-agent.git
cd gemini-ai-agent

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⚡ Install uv – Fast Python Manager

Install uv on macOS with:

curl -LsSf https://astral.sh/uv/install.sh | sh

Verify:

uv --version

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📦 Install Dependencies

Use uv to install all project requirements:

uv pip install -r requirements.txt

Or, if you're using pyproject.toml:

uv pip install .

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🔐 Add Your Gemini API Key

👇 Option 1: Export Key Temporarily (for one terminal session)

export GEMINI_API_KEY="your-api-key-here"

💡 Option 2: Store it in .env (Recommended for VS Code / Cursor)

1. Create a .env file in the root of your project:

touch .env

2. Add your API key:

GEMINI_API_KEY=your-api-key-here

3. In your Python code, use dotenv to load it (add this to main.py or app.py):

from dotenv import load_dotenv
import os

load_dotenv()
api_key = os.getenv("GEMINI_API_KEY")

4. Add .env to .gitignore:

echo ".env" >> .gitignore

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🚀 5. Run the App

Now run your agent:

python main.py

You’re now using Gemini AI securely on macOS with your favorite editor. 🧠💡

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🧑‍💻 Bonus: Use VS Code or Cursor Like a Pro

• Open the project folder:

  code .
  # OR
  cursor .

• Use built-in terminal with Cmd + \ or `Ctrl + ``.

• Install the Python extension for syntax highlighting, linting, and IntelliSense.

• Add .env support in VS Code with the Python extension's built-in support.

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