feat: solution for 'Экзамен: Самокорректирующийся агент'

README.md

@@ -1,25 +1,40 @@
-# Самокорректирующийся агент
+# Self-Correcting Agent
 
-This repository contains a minimal setup for a self-correcting agent using LangChain and OpenAI.  
-The `requirements.txt` file includes all necessary dependencies.
+This repository contains a simple implementation of a self‑correcting agent using **LangGraph**.  
+The agent follows these steps:
 
-## Setup
+1. **Ask** – Generates an answer to the user’s question.  
+2. **Check** – Evaluates the answer’s quality.  
+3. **Correct** – If the answer is flagged as poor, it rewrites it.  
+4. **Final** – Returns the final answer.
+
+## Installation
 
 ```bash
-# 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
 ```
 
-## Running the Test Script
+> **Note**: The implementation uses deterministic placeholders instead of real LLM calls, so no API keys are required.
 
-```bash
-python main.py
+## Usage
+
+```python
+from src.agent import run_agent
+
+question = "What is the capital of France?"
+answer = run_agent(question)
+print(answer)
 ```
 
-You should see a message confirming that the LangChain OpenAI import was successful and an LLM instance was created.
+## Project Structure
 
----
+```
+├── requirements.txt
+├── src
+│   └── agent.py
+└── README.md
+```
+
+## License
+
+MIT License