The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions.This project demonstrates the integration of the OpenAI-compatible SDK with Google's Gemini API to build an intelligent, conversational AI agent. The agent is designed to understand user prompts and generate insightful, human-like responses. In this implementation, the AI agent is tasked with recommending high-income skills for 2025, showcasing its ability to deliver relevant and valuable information. By leveraging the Gemini 2.0 Flash model, the system benefits from fast and efficient language generation.This setup reflects a modular, production-ready approach for integrating third-party large language models (LLMs) into custom AI solutions.
- 🤖 Custom AI Agent: Built using the Agent and Runner pattern for modularity and scalability.
- 🌐 Google Gemini 2.0 Flash Integration: Utilizes Google's API for access to the powerful Gemini language model.
- 🔑 Secure API Key Management: Employs
python-dotenvfor safe and convenient API key handling. - 🧠 Dynamic Prompt Handling: Processes and responds effectively to a wide range of user prompts.
- ⚡ Asynchronous Communication: Leverages
AsyncOpenAIfor optimized asynchronous communication with OpenAI, improving speed and responsiveness. - 🐍 Clean Python Codebase: Maintained with a focus on readability, modularity, and best practices.
Before getting started, make sure you have the following:
- Python 3.8+ installed on your system.
- Basic knowledge of Python programming.
- Familiarity with virtual environments and dependency management.
- Git installed to clone the repository.
- Clone the Repository:
git clone https://github.com/waheed444/OpenAI_SDK_Simple_Agent.git
cd OpenAI_SDK_Simple_Agent- Create and Activate a Virtual Environment:
python -m venv venv
source venv/bin/activate # For Windows: venv\Scripts\activate- Install Dependencies:
pip install -r requirements.txtOR Install Dependencies:
pip install openai-agents python-dotenv
(If a requirements file is not available, check pyproject.toml for dependency instructions.)
- Set up
.envfile:
Create a .env file in the root directory and add your Gemini API key:
GIMINI_API_KEY = your_actual_gemini_api_key_here
Note: Replace your_actual_gemini_api_key_here with your actual Gemini API key.
After setting up the project, you can run the AI agent by executing the main script in the src/agent_01 directory.For example:
python src/agent_01/main.pyThe agent is designed to provide insightful responses to various prompts. For example, a prompt such as "What are the most in-demand skills for high income in 2025?" might produce output similar to:
"The most in-demand skills for massive income in 2025 include... [list of skills]"
More detailed usage instructions and example prompts will be provided in the project's documentation.
This project is licensed under the MIT License - see the LICENSE file for details.
We welcome contributions to improve this project! Please follow these steps:
- Fork the repository.
- Create a new branch:
git checkout -b feature-name - Make your changes and ensure they adhere to the project's coding style and best practices.
- Commit your changes:
git commit -m "Add feature" - Push to the branch:
git push origin feature-name - Submit a pull request with a clear description of your changes and their benefits. If you find any issues or want to improve this project, feel free to open a GitHub issue or submit a pull request.
This repo is only for learning and exploring new things, feel free to fork it, explore, or give suggestions!
Star ⭐ the repo if it helps you!
