commit c5a78e5783acbc91ccd48cf3ce3634ac11c4ff0e
Author: Danil Parunin 5f1b81b8-4f5d-11e8-9c2d-fa7ae01bbebc <dapa46@mail.ru>
Date:   Mon Jun 15 12:30:31 2026 +0000

    add: main.py

diff --git a/main.py b/main.py
new file mode 100644
index 0000000..a8676a4
--- /dev/null
+++ b/main.py
@@ -0,0 +1,85 @@
+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
+
+# --- LLM ------------------------------------------------------------
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b:free",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENAI_API_KEY"),
+    temperature=0.0,
+)
+
+# --- Backend --------------------------------------------------------
+backend = CompositeBackend([
+    LocalShellBackend(workspace_dir="./workspace"),
+    FilesystemBackend(),
+])
+
+# --- Tool -----------------------------------------------------------
+@tool
+def get_price(query: str) -> str:
+    """Return a fake price table for a product in a city."""
+    # In a real task this would call an API or database
+    if "молоко" in query:
+        return "| Продукт | Цена (руб.) | Магазин |\n| Молоко | 89 | Магнит |"
+    if "хлеб" in query:
+        return "| Продукт | Цена (руб.) | Магазин |\n| Хлеб | 35 | Перекресток |"
+    return "No data found."
+
+# --- Agent ----------------------------------------------------------
+agent = create_deep_agent(
+    model=llm,
+    tools=[get_price],
+    backend=backend,
+    system_prompt="You are a helpful agent that can fetch product prices.",
+)
+
+# --- Stream handling -----------------------------------------------
+async def main():
+    # Prepare the initial message
+    human_msg = HumanMessage(content="Какую цену у молока в Казани?")
+
+    # Start streaming
+    stream = agent.stream(
+        {"messages": [human_msg]},
+        stream_mode=["messages", "updates"],
+    )
+
+    step = 1
+
+    def format_chunk_message(chunk):
+        nonlocal step
+        message, meta = chunk
+        if meta.get("langgraph_step") != step:
+            step = meta.get("langgraph_step")
+            print("\n --- --- --- \n")
+        if message.content:
+            print(message.content, end="", flush=True)
+
+    def format_message(message):
+        if message.content:
+            return message.content
+        # If the message is a tool call, format it nicely
+        if message.tool_calls:
+            call = message.tool_calls[0]
+            return f"{call['name']}({call['args']})"
+        return ""
+
+    for chunk in stream:
+        chunk_type, chunk_data = chunk
+        if chunk_type == "messages":
+            format_chunk_message(chunk_data)
+        elif chunk_type == "updates":
+            if chunk_data.get("model"):
+                last_msg = chunk_data["model"]["messages"][-1]
+                print(format_message(last_msg))
+
+    print("\n--- Stream finished ---")
+
+if __name__ == "__main__":
+    asyncio.run(main())