From fd545e1aba9c902956e6dde7391255d195915008 Mon Sep 17 00:00:00 2001
From: Danil Parunin 5f1b81b8-4f5d-11e8-9c2d-fa7ae01bbebc <dapa46@mail.ru>
Date: Tue, 16 Jun 2026 08:19:14 +0000
Subject: [PATCH] =?UTF-8?q?fix(needs=5Ffixes):=201=20=D0=B8=D1=81=D0=BF?=
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---
 main.py | 84 +++++++++++++++++++--------------------------------------
 1 file changed, 27 insertions(+), 57 deletions(-)

diff --git a/main.py b/main.py
index 6765fc2..eeaaed5 100644
--- a/main.py
+++ b/main.py
@@ -1,83 +1,53 @@
-import asyncio
 import os
+import asyncio
 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 FilesystemBackend, LocalShellBackend, CompositeBackend
+from langchain.agents import create_agent
+from langchain_core.messages import HumanMessage
 
-# --- 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.
+# Настройка LLM через OpenRouter
 llm = ChatOpenAI(
-    model='<model_name>',
-    base_url='http://localhost:1234/v1',
-    api_key=os.getenv('OPENAI_API_KEY', 'fake'),
+    model="openai/gpt-oss-20b:free",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENAI_API_KEY"),
     temperature=0.7,
 )
 
-# --- Backend ---------------------------------------------------------------
-backend = CompositeBackend([
-    LocalShellBackend(workspace_dir="./workspace"),
-    FilesystemBackend(),
-])
-
-# --- Sub‑agent tool --------------------------------------------------------
+# Инструмент, который вызывает субагент для получения цены
 @tool
 def get_price(product: str, city: str) -> str:
-    """Return a realistic price for a product in a given city.
-
-    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.
+    """Получить примерную цену продукта в указанном городе.
+    Возвращает таблицу в формате Markdown.
     """
-    # Create a sub‑agent that only has the task of generating a price table.
-    sub_agent = create_deep_agent(
+    # Создаём субагент, который генерирует таблицу
+    sub_agent = create_agent(
         model=llm,
         tools=[],
-        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.",
+        system_prompt=f"Ты эксперт по ценам в {city}.\n\nДай таблицу: | Продукт | Цена (руб.) | Магазин |",  # простая подсказка
     )
-    # 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).
+    # Запускаем субагент с запросом
+    sub_prompt = f"Какова примерная цена на {product} в {city}?"
+    result = sub_agent.invoke({"messages": [HumanMessage(content=sub_prompt)]})
+    # Извлекаем последний текстовый ответ
     return result["messages"][-1].content
 
-# --- Main agent ------------------------------------------------------------
-main_agent = create_deep_agent(
+# Главный агент
+agent = create_agent(
     model=llm,
     tools=[get_price],
-    backend=backend,
     system_prompt="Ты помощник по планированию покупок.",
 )
 
-# --- 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_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
+    user_query = "Помоги составить список покупок: молоко, хлеб, яблоки. Я нахожусь в Казани."
+    result = await agent.ainvoke({"messages": [HumanMessage(content=user_query)]})
+    # Печатаем все сообщения, включая вызовы инструментов
     for msg in result["messages"]:
-        print(format_message(msg))
-        print("---")
+        if msg.content:
+            print(msg.content)
+        elif msg.tool_calls:
+            for call in msg.tool_calls:
+                print(f"{call['name']}({call['args']})")
 
 if __name__ == "__main__":
     asyncio.run(main())