add: main.py

main.py

@@ -0,0 +1,75 @@
+import os
+import asyncio
+from langchain_openai import ChatOpenAI
+from langchain.tools import tool
+from langchain.agents import create_agent
+from langchain_core.messages import HumanMessage
+from deepagents import create_deep_agent
+from deepagents.backends import CompositeBackend, LocalShellBackend, FilesystemBackend
+
+# ---------- LLM ----------
+# Use OpenRouter – cloud API, no local GPU required
+llm = ChatOpenAI(
+    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(),
+])
+
+# ---------- Tool with sub‑agent ----------
+@tool
+def get_price(product: str, city: str) -> str:
+    """Return a realistic price table for a product in a city.
+    The tool internally creates a sub‑agent that generates the table.
+    """
+    # Sub‑agent that simply produces a price table
+    sub_agent = create_agent(
+        model=llm,
+        tools=[],
+        system_prompt=f"You are a price estimator for {city}. Provide a realistic price for {product} in a table format.",
+    )
+    prompt = (
+        f"Generate a table with columns Продукт, Цена (руб.), Магазин for product '{product}' in city '{city}'."
+    )
+    result = sub_agent.invoke({"messages": [HumanMessage(content=prompt)]})
+    # The last message contains the table
+    return result["messages"][-1].content
+
+# ---------- Main agent ----------
+agent = create_deep_agent(
+    model=llm,
+    tools=[get_price],
+    backend=backend,
+    system_prompt="Ты помощник по планированию покупок.",
+)
+
+# ---------- Helper to pretty‑print messages ----------
+
+def format_message(msg):
+    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 execution ----------
+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"}},
+    )
+    # Print all messages in order
+    for msg in result["messages"]:
+        print(format_message(msg))
+        print("---")
+
+if __name__ == "__main__":
+    asyncio.run(main())