From f5594c25c530200d5cfea19ebb5ed58ebc5d4051 Mon Sep 17 00:00:00 2001
From: kuzakhmetovartur <kuzakhmetov99@mail.ru>
Date: Sun, 28 Jun 2026 12:46:52 +0300
Subject: [PATCH] =?UTF-8?q?feat:=20solution=20for=20'=D0=AD=D0=BA=D0=B7?=
 =?UTF-8?q?=D0=B0=D0=BC=D0=B5=D0=BD:=20=D0=A1=D0=B0=D0=BC=D0=BE=D0=BA?=
 =?UTF-8?q?=D0=BE=D1=80=D1=80=D0=B5=D0=BA=D1=82=D0=B8=D1=80=D1=83=D1=8E?=
 =?UTF-8?q?=D1=89=D0=B8=D0=B9=D1=81=D1=8F=20=D0=B0=D0=B3=D0=B5=D0=BD=D1=82?=
 =?UTF-8?q?'?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 README.md    | 102 +++++++++++++--------------------------------------
 src/state.py |   9 +++++
 src/tools.py |  12 ++++++
 3 files changed, 47 insertions(+), 76 deletions(-)
 create mode 100644 src/state.py
 create mode 100644 src/tools.py

diff --git a/README.md b/README.md
index 65d6849..41da028 100644
--- a/README.md
+++ b/README.md
@@ -1,84 +1,34 @@
-# Assignment Card Extraction
+# Экзамен: Самокорректирующийся агент
 
-This project demonstrates how to convert a natural language assignment description into a structured data object using **LangChain** and **Pydantic**.
+Главная
+Мои задания
+Экзамен: Самокорректирующийся агент
+5Д
+EN
+Экзамен: Самокорректирующийся агент
+Зачёт
+Версия 1
+Дедлайн сдачи: 31.08.2026
 
-## Features
+В работе
 
-- Parses assignment details such as title, subject, deadline, deliverable type, and grading hints.
-- Uses OpenAI's GPT model to interpret free‑form text.
-- Validates the output with a Pydantic model to ensure type safety.
+Редактирование ответа
 
-## Prerequisites
+Заполните ответ и отправьте работу на проверку преподавателю.
 
-- Python 3.10 or newer
-- An OpenAI API key
+Тип ответа
+Текст
+Ссылка
+Файлы
+Текст ответа
+Прикреплённые файлы
+Загрузить файл
+Отправить на проверку
+Отменить
 
-## Setup
+Задание
 
-1. **Clone the repository** (or copy the files into a directory):
+Практическое задание: Самокорректирующийся агент
+Цель
 
-   ```bash
-   git clone https://github.com/your-username/assignment-card-extractor.git
-   cd assignment-card-extractor
-   ```
-
-2. **Create a virtual environment** (recommended):
-
-   ```bash
-   python -m venv .venv
-   source .venv/bin/activate   # On Windows: .venv\Scripts\activate
-   ```
-
-3. **Install dependencies**:
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-4. **Configure the OpenAI API key**:
-
-   - Open the `.env` file.
-   - Replace `your_openai_api_key_here` with your actual key.
-
-   ```dotenv
-   OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXX
-   ```
-
-## Running the Example
-
-```bash
-python src/main.py
-```
-
-You should see output similar to:
-
-```
-=== Parsed Assignment Card ===
-{
-  "title": "Мини-отчёт по LangChain",
-  "subject": "LangChain",
-  "deadline_hint": "к пятнице",
-  "deliverable_type": "отчёт",
-  "grading_hints": [
-    "полнота",
-    "пример кода"
-  ]
-}
-
-=== Human-readable Summary ===
-Title: Мини-отчёт по LangChain
-Subject: LangChain
-Deadline: к пятнице
-Deliverable: отчёт
-Grading Hints: полнота, пример кода
-```
-
-## Customization
-
-- **Change the LLM**: Edit `src/main.py` to use a different model or adjust temperature.
-- **Add more fields**: Update the `AssignmentCard` model and the prompt template accordingly.
-- **Use in a pipeline**: Import the `chain` object from `src/main.py` into your own application.
-
-## License
-
-MIT License
\ No newline at end of file
+Реализовать LangGraph-агента, который после выполнения задачи проверяет результат (LLM-as-
\ No newline at end of file
diff --git a/src/state.py b/src/state.py
new file mode 100644
index 0000000..19f0b28
--- /dev/null
+++ b/src/state.py
@@ -0,0 +1,9 @@
+from typing import TypedDict, Optional
+
+class AgentState(TypedDict):
+    task: str
+    result: str
+    attempts: int
+    status: str  # pending | success | failed | max_attempts
+    error: Optional[str]
+    max_attempts: int
\ No newline at end of file
diff --git a/src/tools.py b/src/tools.py
new file mode 100644
index 0000000..e233377
--- /dev/null
+++ b/src/tools.py
@@ -0,0 +1,12 @@
+import random
+
+def unreliable_tool(task: str) -> str:
+    """
+    Simulate a tool that fails 30% of the time.
+    """
+    if random.random() < 0.3:
+        raise ValueError("Tool failed due to random error.")
+    # Simple implementation: if task contains arithmetic, compute it
+    if "2+2" in task:
+        return "4"
+    return "unknown"
\ No newline at end of file