# Project Name > A brief, one-sentence description of what this project does and who it's for. An advanced AI-powered agent that does [X, Y, and Z]. Built with [mention key technologies like CrewAI, Langchain, etc.]. ## 🚀 Features - **Feature 1**: Description of the feature. - **Feature 2**: Description of the feature. - **Feature 3**: Description of the feature. - **User-Friendly Dashboard**: Built with Streamlit for easy interaction. ## 🛠️ Tech Stack - **Python**: Core programming language - **[Framework e.g., Streamlit, FastAPI]**: For the web interface/API - **[AI Library e.g., ScrapeGraph AI, CrewAI]**: For AI-powered workflows - **[LLM Provider e.g., Nebius AI, OpenAI]**: For language model access - **[Other tools e.g., Twilio, APScheduler]**: For notifications, scheduling, etc. - **[Database e.g., JSON, Vector DB]**: For data storage ## Workflow  A brief explanation of how the project works, from input to output. ## 📦 Getting Started ### Prerequisites - Python 3.9+ - [uv](https://github.com/astral-sh/uv) or pip for package management - API keys for the following services: - [Service 1 (e.g., Nebius AI)](https://example.com) - [Service 2 (e.g., Bright Data)](https://example.com) ### Environment Variables Create a `.env` or `api.env` file in the project root and add the following variables. Refer to the specific project's documentation for the exact file name and variables required. ```env SERVICE1_API_KEY="your_service1_api_key" SERVICE2_API_KEY="your_service2_api_key" ``` **Note:** Ensure that any phone numbers or client IDs are correctly formatted and registered with their respective services. ### Installation 1. **Clone the repository:** ```bash git clone https://github.com/Arindam200/awesome-llm-apps.git cd awesome-llm-apps/[project_directory] ``` 2. **Create and activate a virtual environment:** - **Using `venv`:** ```bash python -m venv .venv source .venv/bin/activate # On Windows, use: .venv\Scripts\activate ``` 3. **Install dependencies:** - **Using `uv` (recommended):** ```bash uv sync ``` - **Using `pip`:** ```bash pip install -r requirements.txt ``` ## ⚙️ Usage 1. **Run the application:** ```bash streamlit run app.py ``` or for background services: ```bash python main.py ``` 2. **Open your browser** to `http://localhost:8501` (or as indicated by the application). 3. Follow the on-screen instructions, such as providing an API key or input URL. ## 📂 Project Structure A standardized project structure is recommended for clarity and maintainability. ``` project_name/ ├── agents/ # AI agent definitions ├── assets/ # Static assets (images, GIFs) ├── tools/ # Custom tools for agents ├── .venv/ # Virtual environment ├── .env # Environment variables ├── app.py # Main application file (e.g., Streamlit UI) ├── main.py # Core logic or service entry point ├── requirements.txt # Python dependencies └── README.md # Project-specific README ``` ## 🤝 Contributing Contributions are welcome! Please feel free to submit a Pull Request. See the [CONTRIBUTING.md](https://github.com/Arindam200/awesome-llm-apps/blob/main/CONTRIBUTING.md) for more details. ## 📄 License This project is licensed under the MIT License - see the [LICENSE](https://github.com/Arindam200/awesome-llm-apps/blob/main/LICENSE) file for details. ## 🙏 Acknowledgments - Shoutout to [Library/Framework](https://example.com) for their amazing work. - Inspired by [Project/Article](https://example.com).