add main.py

main.py

@@ -0,0 +1,65 @@
+import os
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage
+from langchain.tools import tool
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.types import Command
+
+# LLM setup
+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,
+)
+
+# Dummy tool: get_price
+@tool
+def get_price(product: str, city: str) -> str:
+    """Return a markdown table with price for a product in a city."""
+    # In a real scenario, fetch from API. Here we return a static example.
+    return f"| Продукт | Цена (руб.) | Магазин |\n| {product} | 89 | Магнит |"
+
+# Agent definition
+from langchain.agents import create_agent
+
+agent = create_agent(
+    llm=llm,
+    tools=[get_price],
+    system_prompt="You are a helpful assistant that can call get_price to provide price information.",
+)
+
+# Stream execution
+stream = agent.stream(
+    {
+        "messages": [HumanMessage(content="Покажи цену молока и хлеба в Казани.")]
+    },
+    stream_mode=["messages", "updates"],
+)
+
+step = 1
+
+def format_chunk_message(chunk):
+    message, meta = chunk
+    global step
+    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
+    return f"{message.tool_calls[0]['name']}({message.tool_calls[0]['args']})"
+
+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_message = chunk_data["model"]["messages"][-1]
+            print(format_message(last_message))
+
+print("\n--- Завершено ---")