diff --git a/main.py b/main.py
index a60cc37..6765fc2 100644
--- a/main.py
+++ b/main.py
@@ -1,75 +1,83 @@
-import os
 import asyncio
+import os
 from langchain_openai import ChatOpenAI
 from langchain_core.messages import HumanMessage
 from langchain.tools import tool
 from deepagents import create_deep_agent
-from deepagents.backends import CompositeBackend, LocalShellBackend, FilesystemBackend
+from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
 
-# ---------- LLM ----------
+# --- LLM configuration -----------------------------------------------------
+# Connect to the local LM Studio server. Replace '<model_name>' with the exact
+# name of the model you have loaded in LM Studio.
 llm = ChatOpenAI(
-    model="openai/gpt-oss-20b:free",
-    base_url="https://openrouter.ai/api/v1",
-    api_key=os.getenv("OPENAI_API_KEY"),
+    model='<model_name>',
+    base_url='http://localhost:1234/v1',
+    api_key=os.getenv('OPENAI_API_KEY', 'fake'),
     temperature=0.7,
 )
 
-# ---------- Backend ----------
+# --- Backend ---------------------------------------------------------------
 backend = CompositeBackend([
     LocalShellBackend(workspace_dir="./workspace"),
     FilesystemBackend(),
 ])
 
-# ---------- Sub‑agent for price generation ----------
-# The sub‑agent simply asks the LLM to produce a realistic price table.
-# It is wrapped in a tool so that the main agent can call it.
-
+# --- Sub‑agent tool --------------------------------------------------------
 @tool
 def get_price(product: str, city: str) -> str:
     """Return a realistic price for a product in a given city.
-    The response must be a Markdown table with columns: Продукт, Цена (руб.), Магазин.
-    """
-    # Create a tiny agent that only generates the table.
-    from langchain.agents import create_agent
-    from langchain_core.messages import HumanMessage
 
-    system_prompt = (
-        "You are a market price generator. "
-        "Given a product and a city, produce a realistic price table in Markdown. "
-        "Use plausible Russian store names and prices."
-    )
-    sub_agent = create_agent(
+    The function internally creates a sub‑agent that asks the LLM to generate
+    a price table. The sub‑agent is a lightweight wrapper around the same
+    LLM instance to keep the example simple.
+    """
+    # Create a sub‑agent that only has the task of generating a price table.
+    sub_agent = create_deep_agent(
         model=llm,
         tools=[],
-        system_prompt=system_prompt,
+        backend=backend,
+        system_prompt=f"You are a market analyst. Provide a realistic price for {product} in {city}. Output a markdown table with columns: Продукт, Цена (руб.), Магазин.",
     )
-    prompt = f"Product: {product}\nCity: {city}"
-    result = sub_agent.invoke({"messages": [HumanMessage(content=prompt)]})
-    # The sub‑agent returns a dict with 'messages'; take the last content.
+    # Invoke the sub‑agent with a simple prompt.
+    result = asyncio.run(
+        sub_agent.ainvoke(
+            {"messages": [HumanMessage(content=f"Generate price for {product} in {city}")]},
+            {"configurable": {"thread_id": f"price-{product}-{city}"}},
+        )
+    )
+    # Return the content of the last message (the table).
     return result["messages"][-1].content
 
-# ---------- Main agent ----------
-agent = create_deep_agent(
+# --- Main agent ------------------------------------------------------------
+main_agent = create_deep_agent(
     model=llm,
     tools=[get_price],
     backend=backend,
     system_prompt="Ты помощник по планированию покупок.",
 )
 
-# ---------- Run ----------
+# --- Helper to pretty‑print the conversation ------------------------------
+from langchain_core.messages import BaseMessage
+
+def format_message(msg: BaseMessage) -> str:
+    if hasattr(msg, "content") and msg.content:
+        return msg.content
+    if hasattr(msg, "tool_calls") and msg.tool_calls:
+        call = msg.tool_calls[0]
+        return f"{call['name']}({call['args']})"
+    return ""
+
+# --- Main entry point ------------------------------------------------------
 async def main():
-    user_query = "Помоги составить список покупок: молоко, хлеб, яблоки. Я нахожусь в Казани."
-    result = await agent.ainvoke(
-        {"messages": [HumanMessage(content=user_query)]},
-        {"configurable": {"thread_id": "session-1"}},
+    user_prompt = "Помоги составить список покупок: молоко, хлеб, яблоки. Я нахожусь в Казани."
+    result = await main_agent.ainvoke(
+        {"messages": [HumanMessage(content=user_prompt)]},
+        {"configurable": {"thread_id": "shopping-session"}},
     )
     # Print all messages in order
     for msg in result["messages"]:
-        if msg.content:
-            print(msg.content)
-        elif msg.tool_calls:
-            for call in msg.tool_calls:
-                print(f"{call['name']}({call['args']})")
+        print(format_message(msg))
+        print("---")
 
 if __name__ == "__main__":
     asyncio.run(main())