diff --git a/main.py b/main.py
index bfb1ff1..220333f 100644
--- a/main.py
+++ b/main.py
@@ -1,27 +1,26 @@
 import os
 import asyncio
-from typing import List
+from typing import Any, Dict, List
 
 from pydantic import SecretStr
 from langchain_openai import ChatOpenAI
 from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
 from langchain.tools import tool
-
 from deepagents import create_deep_agent
 from deepagents.backends import CompositeBackend, LocalShellBackend, FilesystemBackend
 
 # ----------------------------------------------------------------------
-# Configuration of the LLM (OpenRouter, as required by the course)
+# LLM configuration (OpenRouter)
 # ----------------------------------------------------------------------
 llm = ChatOpenAI(
     model="openai/gpt-oss-20b:free",
     base_url="https://openrouter.ai/api/v1",
-    api_key=SecretStr(os.getenv("OPENAI_API_KEY")),
+    api_key=os.getenv("OPENAI_API_KEY"),
     temperature=0.7,
 )
 
 # ----------------------------------------------------------------------
-# Backend for the agents - allows file operations and shell commands
+# Backend for sub-agents (allows file operations and shell commands)
 # ----------------------------------------------------------------------
 backend = CompositeBackend(
     [
@@ -31,84 +30,91 @@ backend = CompositeBackend(
 )
 
 # ----------------------------------------------------------------------
-# Sub-agent tool: get_price
+# Sub-agent that generates a realistic price table for a product
+# ----------------------------------------------------------------------
+def create_price_subagent() -> Any:
+    """
+    Returns a deep agent that, given a product and a city, produces a markdown
+    table with product, price and store. The prompt forces the model to fabricate
+    plausible data based on typical market prices.
+    """
+    system_prompt = (
+        "You are a price-generation sub-agent. Given a product name and a city, "
+        "return a markdown table with columns: Продукт, Цена (руб.), Магазин. "
+        "Fabricate realistic prices based on typical Russian market data. "
+        "Do not add any extra commentary, only the table."
+    )
+    subagent = create_deep_agent(
+        model=llm,
+        tools=[],  # no external tools needed for this simple sub-agent
+        backend=backend,
+        system_prompt=system_prompt,
+    )
+    return subagent
+
+price_subagent = create_price_subagent()
+
+# ----------------------------------------------------------------------
+# Tool that calls the sub-agent
 # ----------------------------------------------------------------------
 @tool
 def get_price(product: str, city: str) -> str:
     """
-    Получить примерную цену продукта в указанном городе.
-    Возвращает markdown-таблицу с колонками: Продукт, Цена (руб.), Магазин.
+    Generate a realistic price for the given product in the specified city.
+    Returns a markdown table with columns: Продукт, Цена (руб.), Магазин.
     """
-    # Создаём суб-агента, который генерирует цену.
-    sub_agent = create_deep_agent(
-        model=llm,
-        tools=[],
-        backend=backend,
-        system_prompt=(
-            "Ты суб-агент, который генерирует реалистичную цену продукта "
-            "в заданном городе. Выдай результат в виде markdown-таблицы "
-            "с колонками: Продукт, Цена (руб.), Магазин."
-        ),
-    )
-
-    # Формируем запрос к суб-агенту
-    query = f"Сгенерируй цену для продукта '{product}' в городе {city}."
-    # Асинхронный вызов суб-агента
-    async def _invoke():
-        result = await sub_agent.ainvoke(
-            {"messages": [HumanMessage(content=query)]},
+    # Build the prompt for the sub-agent
+    prompt = f"Продукт: {product}\nГород: {city}"
+    # Invoke the sub-agent synchronously (deepagents also supports async,
+    # but a simple sync call keeps the example straightforward)
+    result = asyncio.run(
+        price_subagent.ainvoke(
+            {"messages": [HumanMessage(content=prompt)]},
             {"configurable": {"thread_id": f"price-{product}-{city}"}},
         )
-        # Последнее сообщение содержит таблицу
-        return result["messages"][-1].content
-
-    # Запускаем цикл событий, если уже внутри async контекста
-    try:
-        loop = asyncio.get_running_loop()
-        table = loop.create_task(_invoke())
-        # Если мы уже в async функции, вернём задачу, иначе дождёмся результата
-        if isinstance(table, asyncio.Task):
-            return asyncio.run(table)
-    except RuntimeError:
-        # Нет запущенного цикла - создаём новый
-        return asyncio.run(_invoke())
+    )
+    # The sub-agent returns a list of messages; the last one contains the table
+    final_message = result["messages"][-1]
+    if isinstance(final_message, AIMessage):
+        return final_message.content
+    elif isinstance(final_message, ToolMessage):
+        return final_message.content
+    else:
+        return str(final_message)
 
 # ----------------------------------------------------------------------
-# Main shopping-assistant agent
+# Main shopping-list agent
 # ----------------------------------------------------------------------
-assistant_agent = create_deep_agent(
+shopping_agent = create_deep_agent(
     model=llm,
     tools=[get_price],
     backend=backend,
     system_prompt="Ты помощник по планированию покупок.",
 )
 
-# ----------------------------------------------------------------------
-# Helper to format the chain of messages for display
-# ----------------------------------------------------------------------
-def format_message(msg) -> str:
-    if isinstance(msg, HumanMessage):
-        return f"Human: {msg.content}"
-    if isinstance(msg, AIMessage):
-        return f"AI: {msg.content}"
+def format_message(msg: Any) -> str:
+    """Human-readable representation of a message or tool call."""
+    if isinstance(msg, (HumanMessage, AIMessage)):
+        return msg.content
     if isinstance(msg, ToolMessage):
-        # tool call result
-        return f"ToolResult: {msg.content}"
-    # Fallback for generic messages
+        return f"{msg.name}({msg.args}) -> {msg.content}"
+    # Fallback for generic dict-like messages
+    if hasattr(msg, "tool_calls") and msg.tool_calls:
+        call = msg.tool_calls[0]
+        return f"{call['name']}({call['args']})"
     return str(msg)
 
-async def main():
+async def main() -> None:
     user_query = "Помоги составить список покупок: молоко, хлеб, яблоки. Я нахожусь в Казани."
-    result = await assistant_agent.ainvoke(
+    result = await shopping_agent.ainvoke(
         {"messages": [HumanMessage(content=user_query)]},
         {"configurable": {"thread_id": "shopping-session-1"}},
     )
-
-    # Выводим всю цепочку сообщений
-    print("\n--- Диалог с агентом ---\n")
-    for m in result["messages"]:
-        print(format_message(m))
-        print("---")
+    # Print the whole chain of messages
+    for i, message in enumerate(result["messages"]):
+        print(f"--- Message {i + 1} ---")
+        print(format_message(message))
+        print()
 
 if __name__ == "__main__":
     asyncio.run(main())
\ No newline at end of file