fix: main.py — Создайть просто AI агент на Python с применением langchain

main.py

@@ -1,22 +1,28 @@
 import os
 import asyncio
-from typing import Any
+from typing import List
 
+from pydantic import SecretStr
 from langchain_openai import ChatOpenAI
-from langchain_core.messages import HumanMessage
+from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
 from langchain.tools import tool
-from deepagents import create_deep_agent
-from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
 
-# LLM configuration according to the assignment specification
+from deepagents import create_deep_agent
+from deepagents.backends import CompositeBackend, LocalShellBackend, FilesystemBackend
+
+# ----------------------------------------------------------------------
+# Configuration of the LLM (OpenRouter, as required by the course)
+# ----------------------------------------------------------------------
 llm = ChatOpenAI(
-    model="your-model-name",  # replace with the actual model name in LM Studio
-    base_url="http://localhost:1234/v1",
-    api_key="fake",  # OpenAI SDK requires a non-empty key
+    model="openai/gpt-oss-20b:free",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=SecretStr(os.getenv("OPENAI_API_KEY")),
     temperature=0.7,
 )
 
-# Backend for file operations and shell commands (required by deepagents)
+# ----------------------------------------------------------------------
+# Backend for the agents - allows file operations and shell commands
+# ----------------------------------------------------------------------
 backend = CompositeBackend(
     [
         LocalShellBackend(workspace_dir="./workspace"),
@@ -24,78 +30,85 @@ backend = CompositeBackend(
     ]
 )
 
+# ----------------------------------------------------------------------
+# Sub-agent tool: get_price
+# ----------------------------------------------------------------------
 @tool
 def get_price(product: str, city: str) -> str:
     """
-    Retrieve a realistic price for the given product in the specified city.
-    The function creates a sub-agent that returns a markdown table row.
+    Получить примерную цену продукта в указанном городе.
+    Возвращает markdown-таблицу с колонками: Продукт, Цена (руб.), Магазин.
     """
-    # System prompt for the sub-agent - it must output a table with columns
-    # Product, Price (руб.), Store.
-    sub_system_prompt = (
-        "You are a price generator. Provide a markdown table with columns "
-        "'Продукт', 'Цена (руб.)', 'Магазин' for the given product and city. "
-        "Give a realistic price and a plausible store name."
-    )
-
-    # Create the sub-agent (no additional tools needed)
+    # Создаём суб-агента, который генерирует цену.
     sub_agent = create_deep_agent(
         model=llm,
         tools=[],
         backend=backend,
-        system_prompt=sub_system_prompt,
+        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."
+        ),
     )
 
-    # Prepare the query for the sub-agent
-    query = f"Provide price information for {product} in {city}."
-
-    # Invoke the sub-agent synchronously
-    # DESIGN DECISION: Use asyncio.run to execute the sub-agent inside a
-    # synchronous tool. deepagents operates asynchronously, but the tool
-    # interface required by the main agent is synchronous.
-    # NECESSITY: The assignment defines the tool as a regular function.
-    # OPTIMALITY: This approach keeps the code simple and avoids mixing
-    # async/sync contexts incorrectly.
-    # ALTERNATIVES CONSIDERED: Making the tool async (deepagents supports
-    # async tools) would require changes to the main agent invocation pattern,
-    # which is unnecessary for this educational example.
-    result = asyncio.run(
-        sub_agent.ainvoke(
+    # 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)]},
             {"configurable": {"thread_id": f"price-{product}-{city}"}},
         )
-    )
-    # Extract the final content from the sub-agent's response
+        # Последнее сообщение содержит таблицу
         return result["messages"][-1].content
 
-# Main shopping-list agent
-agent = create_deep_agent(
+    # 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())
+
+# ----------------------------------------------------------------------
+# Main shopping-assistant agent
+# ----------------------------------------------------------------------
+assistant_agent = create_deep_agent(
     model=llm,
     tools=[get_price],
     backend=backend,
     system_prompt="Ты помощник по планированию покупок.",
 )
 
-def format_message(message: Any) -> str:
-    """Convert a LangChain message to a readable string."""
-    if hasattr(message, "content") and message.content:
-        return message.content
-    if hasattr(message, "tool_calls") and message.tool_calls:
-        tc = message.tool_calls[0]
-        return f"{tc['name']}({tc['args']})"
-    return str(message)
+# ----------------------------------------------------------------------
+# 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}"
+    if isinstance(msg, ToolMessage):
+        # tool call result
+        return f"ToolResult: {msg.content}"
+    # Fallback for generic messages
+    return str(msg)
 
-async def main() -> None:
+async def main():
     user_query = "Помоги составить список покупок: молоко, хлеб, яблоки. Я нахожусь в Казани."
-    result = await agent.ainvoke(
+    result = await assistant_agent.ainvoke(
         {"messages": [HumanMessage(content=user_query)]},
         {"configurable": {"thread_id": "shopping-session-1"}},
     )
-    # Output the whole chain of messages
-    for idx, msg in enumerate(result["messages"], start=1):
-        print(f"--- Message {idx} ---")
-        print(format_message(msg))
-        print()
+
+    # Выводим всю цепочку сообщений
+    print("\n--- Диалог с агентом ---\n")
+    for m in result["messages"]:
+        print(format_message(m))
+        print("---")
 
 if __name__ == "__main__":
     asyncio.run(main())