Update README.md

README.md

@@ -1,13 +1,17 @@
-# Self‑correcting LangGraph Agent
+# Self‑Correcting LangGraph Agent
 
-This repository contains a small demo of a **self‑correcting LangGraph agent**. The agent receives a *task* string, executes it via an unreliable tool, then asks an LLM to judge the result. If the judge says the result is **failed**, the agent retries until it reaches a maximum number of attempts.
+This repository contains a minimal example of a **self‑correcting agent** built with
+[LangGraph](https://langchain-ai.github.io/langgraph/) and
+[LangChain](https://langchain.com/).  The agent:
 
-## Features
+1. **Receives a natural‑language task** from the user.
+2. **Executes the task** via an *unreliable* tool that fails 30 % of the time.
+3. **Asks an LLM** (OpenAI GPT‑4o‑mini) to judge whether the result is correct.
+4. **Retries automatically** until the judge says *success* or the maximum number
+   of attempts is reached.
 
-- **Unreliable tool** – 30 % chance of raising an exception.
-- **LLM judge** – forces the model to answer only `success` or `failed`.
-- **Retry logic** – automatically retries until success or a maximum number of attempts.
-- **LangGraph** – low‑level graph with three nodes: `execute_task`, `verify_result`, `handle_error`.
+The code demonstrates how to build a small state machine with LangGraph, how to
+use a LLM as a *judge*, and how to implement retry logic.
 
 ## Setup
 
@@ -18,34 +22,43 @@ source venv/bin/activate   # Windows: venv\Scripts\activate
 
 # Install dependencies
 pip install -r requirements.txt
+
+# Set your OpenAI API key
+export OPENAI_API_KEY=YOUR_KEY   # Windows: set OPENAI_API_KEY=YOUR_KEY
 ```
 
-## Running the Agent
+## Running the agent
 
 ```bash
-python agent.py
+python agent.py "Вычисли 2+2"
 ```
 
-You will be prompted to enter a task. The agent will then perform the task, verify the result, and retry if necessary. Example output:
+You can also run the script without arguments – it will prompt you for a task.
+
+## Example output
 
 ```
-Self‑correcting LangGraph agent demo
-Enter a task: 2+2
+Введите задачу: 2+2
+Попытка 1: результат Result of 2+2
+Попытка 2: результат Result of 2+2
 
---- Result ---
-Task: 2+2
-Attempts: 2
-Status: success
-Result: 22
+Итог:
+Успех за 2 попыток. Результат: Result of 2+2
 ```
 
-## Project Structure
+The exact number of attempts may vary because the tool fails randomly.
 
-- `agent.py` – main implementation.
-- `README.md` – this documentation.
-- `requirements.txt` – Python dependencies.
+---
 
-## Notes
+### How it works
 
-- The LLM used is OpenAI's `gpt-4o-mini`. If you prefer Ollama, change the `ChatOpenAI` import to `ChatOllama` and adjust the model name accordingly.
-- The unreliable tool is a toy example; replace it with a real tool for production use.
+- **State** – `AgentState` tracks the task, result, number of attempts, status and
+  any error.
+- **Nodes** – `execute_task`, `verify_result`, `handle_error`.
+- **LLM judge** – a simple prompt that forces the model to answer only
+  "success" or "failed".
+- **Graph** – a conditional router that loops back to `execute_task` on failure
+  until the maximum attempts are reached.
+
+Feel free to adapt the tool, the judge prompt, or the retry policy to fit your
+needs.