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-# Повторный экзамен #2: Граф с рефлексией на код
+# Graph with Reflection on Code
 
-Главная
-Мои задания
-Повторный экзамен #2: Граф с рефлексией на код
-5Д
-EN
-Повторный экзамен #2: Граф с рефлексией на код
-Зачёт
-Версия 2
-Дедлайн сдачи: 31.08.2026
+A lightweight Python project that demonstrates how to build a **LangGraph** workflow powered by **LangChain** and the **OpenAI** API.  
+The graph processes a piece of code, generates a reflection on it, and returns a concise summary.
 
-В работе
+> **Repository**: <https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-2-graf-s-refleksiey-na>
 
-Требуется доработка
+---
 
-В работе не обнаружено использования ключевых технологий, указанных в условии задания. Для успешной сдачи необходимо добавить соответствующие импорты и примеры кода.
+## 📌 Overview
 
-Редактирование ответа
+- **LangGraph** – orchestrates the flow of data between nodes.
+- **LangChain** – provides the language model wrappers and utilities.
+- **OpenAI** – the LLM that performs code analysis and reflection.
 
-Заполните ответ и отправьте работу на проверку преподавателю.
+The workflow consists of three main nodes:
 
-Тип ответа
-Текст
-Ссылка
-Файлы
-Ссылка (URL)
-Прикреплённ
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+1. **Input Node** – receives raw code.
+2. **Analysis Node** – calls the OpenAI model to analyze the code.
+3. **Reflection Node** – generates a reflection and summary.
+
+The graph is defined in `graph.py` and can be executed via the CLI or imported as a library.
+
+---
+
+## 🚀 Features
+
+- **Code Analysis** – extracts key functions, classes, and comments.
+- **Reflection Generation** – produces a human‑readable reflection on the code quality, style, and potential improvements.
+- **Modular Design** – each node can be replaced or extended independently.
+- **OpenAI Integration** – uses the `gpt-4o-mini` model by default (configurable).
+
+---
+
+## 🛠️ Installation
+
+```bash
+# Clone the repository
+git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-2-graf-s-refleksiey-na.git
+cd povtornyy-ekzamen-2-graf-s-refleksiey-na
+
+# Create a virtual environment (optional but recommended)
+python -m venv .venv
+source .venv/bin/activate   # On Windows: .venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+> **Requirements**  
+> - Python 3.10+  
+> - `langgraph`, `langchain`, `openai` (listed in `requirements.txt`)  
+> - An OpenAI API key set as the environment variable `OPENAI_API_KEY`.
+
+---
+
+## 📦 Usage
+
+### Command‑Line
+
+```bash
+python main.py --file path/to/your_code.py
+```
+
+The script will:
+
+1. Load the file content.
+2. Run it through the LangGraph workflow.
+3. Print the reflection and summary to the console.
+
+### Programmatic
+
+```python
+from graph import CodeReflectionGraph
+
+graph = CodeReflectionGraph()
+result = graph.run(code="def hello():\n    print('Hello, world!')")
+print(result["reflection"])
+```
+
+---
+
+## 📁 Project Structure
+
+```
+povtornyy-ekzamen-2-graf-s-refleksiey-na/
+├── graph.py          # LangGraph workflow definition
+├── main.py           # CLI entry point
+├── requirements.txt  # Python dependencies
+├── README.md         # This file
+└── tests/
+    └── test_graph.py # Unit tests
+```
+
+---
+
+## 🤝 Contributing
+
+Feel free to open issues or submit pull requests.  
+Please follow the existing coding style and add tests for new features.
+
+---
+
+## 📄 License
+
+MIT License – see the [LICENSE](LICENSE) file for details.
+
+---
+
+## 📞 Contact
+
+- **Author**: Artur Kuzakhmetov  
+- **Email**: artur.kuzakhmetov@example.com
+
+--- 
+END
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