# Self‑Correcting LangGraph Agent

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:

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.

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

```bash
# Optional: create a virtual environment
python -m venv venv
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

```bash
python agent.py "Вычисли 2+2"
```

You can also run the script without arguments – it will prompt you for a task.

## Example output

```
Введите задачу: 2+2
Попытка 1: результат Result of 2+2
Попытка 2: результат Result of 2+2

Итог:
Успех за 2 попыток. Результат: Result of 2+2
```

The exact number of attempts may vary because the tool fails randomly.

---

### How it works

- **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.