diff --git a/src/main.py b/src/main.py
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+"""
+Simple hierarchical LangChain agent for shopping list.
+
+The script demonstrates:
+* Connection to a local LLM via OpenAI‑compatible API.
+* A tool `get_price` that internally creates a sub‑agent to generate a price table.
+* A main agent that uses the tool and prints all intermediate calls and final answer.
+"""
+
+from langchain_openai import ChatOpenAI
+from langchain.tools import tool, BaseTool
+from langchain.agents import create_agent
+from pydantic import SecretStr
+import json
+
+# --- LLM setup -----------------------------------------------------------
+llm = ChatOpenAI(
+    model="gpt-4o-mini",  # replace with your LM Studio model name
+    base_url="http://localhost:1234/v1",
+    api_key=SecretStr("fake"),
+    temperature=0.7,
+)
+
+# --- Sub‑agent that generates a price table ------------------------------
+def create_price_agent(product: str, city: str):
+    """Return a simple string with a fake price table for the product."""
+    # In a real scenario you would query a database or API.
+    prices = {
+        ("молоко", "Казань"): ("89", "Магнит"),
+        ("хлеб", "Казань"): ("45", "Пятёрочка"),
+        ("яблоки", "Казань"): ("120/кг", "Перекрёсток"),
+    }
+    price, store = prices.get((product.lower(), city), ("?", "?"))
+    table = f"| Продукт | Цена (руб.) | Магазин |\n|---------|-------------|---------|\n| {product}  | {price}          | {store} |
+"
+    return table
+
+# --- Tool that calls the sub‑agent ---------------------------------------
+@tool(name="get_price", description="Get price for a product in a city")
+def get_price(product: str, city: str) -> str:
+    return create_price_agent(product, city)
+
+# --- Main agent ----------------------------------------------------------
+main_agent = create_agent(
+    model=llm,
+    tools=[get_price],
+    system_prompt="Ты помощник по планированию покупок. Используй инструмент get_price для получения цен.",
+)
+
+# --- Run the agent with a sample query -----------------------------------
+query = "Помоги составить список покупок: молоко, хлеб, яблоки. Я нахожусь в Казани."
+result = main_agent.invoke({"messages": [{"role": "human", "content": query}]})
+
+# Print all messages (intermediate tool calls and final answer)
+for msg in result["messages"]:
+    if msg.get("tool_calls"):
+        for call in msg["tool_calls"]:
+            print(f"{call['name']}({json.dumps(call['args'])})")
+    else:
+        print(msg["content"])
+
+# End of script