diff --git a/stream_agent.py b/stream_agent.py
deleted file mode 100644
index b94737c..0000000
--- a/stream_agent.py
+++ /dev/null
@@ -1,80 +0,0 @@
-from __future__ import annotations
-
-import argparse
-import os
-from typing import Any
-
-from langchain_core.messages import AIMessage
-from langchain_core.tools import tool
-from langchain_openai import ChatOpenAI
-from langgraph.prebuilt import create_react_agent
-
-
-@tool
-def get_demo_price(product: str, city: str = "Казань") -> str:
-    """Return a deterministic demo price for the requested product."""
-    prices = {
-        "молоко": "89 рублей",
-        "хлеб": "54 рубля",
-        "сыр": "219 рублей",
-    }
-    return f"{product} в городе {city}: {prices.get(product.lower(), 'цена не найдена')}"
-
-
-def build_agent():
-    llm = ChatOpenAI(
-        model=os.getenv("OPENAI_MODEL", "openai/gpt-oss-20b:free"),
-        base_url=os.getenv("OPENAI_BASE_URL") or None,
-        api_key=os.getenv("OPENAI_API_KEY", "not-needed"),
-        temperature=0,
-        streaming=True,
-    )
-    return create_react_agent(llm, tools=[get_demo_price])
-
-
-def format_message(message: AIMessage) -> str:
-    if message.content:
-        return str(message.content)
-    if message.tool_calls:
-        call = message.tool_calls[0]
-        return f"{call['name']}({call['args']})"
-    return ""
-
-
-def format_chunk_message(chunk: tuple[Any, dict[str, Any]], state: dict[str, int]) -> None:
-    message, meta = chunk
-    current_step = int(meta.get("langgraph_step", state["step"]))
-    if current_step != state["step"]:
-        state["step"] = current_step
-        print("\n --- --- --- \n")
-    if getattr(message, "content", None):
-        print(message.content, end="", flush=True)
-
-
-def run_stream(question: str) -> None:
-    agent = build_agent()
-    stream = agent.stream(
-        {"messages": [{"role": "human", "content": question}]},
-        stream_mode=["messages", "updates"],
-    )
-    state = {"step": 1}
-    for chunk_type, chunk_data in stream:
-        if chunk_type == "messages":
-            format_chunk_message(chunk_data, state)
-        elif chunk_type == "updates" and chunk_data.get("model"):
-            last_message = chunk_data["model"]["messages"][-1]
-            formatted = format_message(last_message)
-            if formatted:
-                print(f"\n{formatted}")
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description="Streaming AI-agent demo")
-    parser.add_argument("question", nargs="*", help="User question")
-    args = parser.parse_args()
-
-    question = " ".join(args.question).strip() or "Сколько стоит молоко и хлеб в Казани?"
-    run_stream(question)
-
-
-if __name__ == "__main__":
-    main()