From 0ec131cff673e0b0844850b51dc7356d592e2022 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=A0=D0=B8=D0=BD=D0=B0=D1=80=20=D0=9C=D0=B8=D1=80=D0=B7?=
 =?UTF-8?q?=D0=B0=D0=B3=D0=B8=D1=82=D0=BE=D0=B2?= <mirzagitov03@mail.ru>
Date: Tue, 26 May 2026 10:26:59 +0000
Subject: [PATCH] Add agent.py for stream agent task

---
 agent.py | 97 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 97 insertions(+)
 create mode 100644 agent.py

diff --git a/agent.py b/agent.py
new file mode 100644
index 0000000..984affa
--- /dev/null
+++ b/agent.py
@@ -0,0 +1,97 @@
+from langchain.agents import create_agent
+from langchain.tools import tool
+from langchain_openai import ChatOpenAI
+from langgraph.checkpoint.memory import MemorySaver
+from pydantic import SecretStr
+
+
+llm = ChatOpenAI(
+    model="local-model",
+    base_url="http://localhost:1234/v1",
+    api_key=SecretStr("fake"),
+    temperature=0.7,
+)
+
+
+@tool
+def get_price(product: str, city: str) -> str:
+    """Возвращает примерную цену продукта в указанном городе."""
+
+    price_agent = create_agent(
+        model=llm,
+        tools=[],
+        system_prompt=(
+            "Ты субагент оценки цен. Верни одну markdown-таблицу "
+            "с колонками: Продукт, Цена (руб.), Магазин."
+        ),
+    )
+    answer = price_agent.invoke(
+        {
+            "messages": [
+                {
+                    "role": "human",
+                    "content": (
+                        f"Оцени реалистичную цену продукта '{product}' "
+                        f"в городе {city}, опираясь на исторические данные о ценах."
+                    ),
+                }
+            ]
+        }
+    )
+    return answer["messages"][-1].content
+
+
+def format_message(message) -> str:
+    if message.content:
+        return message.content
+    if message.tool_calls:
+        call = message.tool_calls[0]
+        return f"{call['name']}({call['args']})"
+    return ""
+
+
+def stream_shopping_agent() -> None:
+    memory = MemorySaver()
+    agent = create_agent(
+        model=llm,
+        tools=[get_price],
+        system_prompt="Ты помощник по планированию покупок",
+        checkpointer=memory,
+    )
+
+    stream = agent.stream(
+        {
+            "messages": [
+                {
+                    "role": "human",
+                    "content": (
+                        "Помоги составить список покупок: молоко, хлеб, яблоки. "
+                        "Я нахожусь в Казани."
+                    ),
+                }
+            ]
+        },
+        stream_mode=["messages", "updates"],
+        config={"configurable": {"thread_id": "shopping-stream-demo"}},
+    )
+
+    step = None
+    for chunk_type, chunk_data in stream:
+        if chunk_type == "messages":
+            message, meta = chunk_data
+            current_step = meta.get("langgraph_step")
+            if current_step != step:
+                step = current_step
+                print("\n --- --- --- \n")
+            if message.content:
+                print(message.content, end="", flush=True)
+
+        if chunk_type == "updates" and chunk_data.get("model"):
+            last_message = chunk_data["model"]["messages"][-1]
+            formatted = format_message(last_message)
+            if formatted:
+                print(formatted)
+
+
+if __name__ == "__main__":
+    stream_shopping_agent()