diff --git a/agent.py b/agent.py
deleted file mode 100644
index b5b3349..0000000
--- a/agent.py
+++ /dev/null
@@ -1,134 +0,0 @@
-"""
-Agent definition with memory and human‑in‑the‑loop confirmation.
-
-This module exposes two public helpers:
-
-* ``create_agent`` – builds a LangGraph graph that uses a MemorySaver checkpoint, an interrupt before tools and a simple ``get_price`` tool.
-* ``ask_and_run`` – runs the agent for a single user message, handling streaming output and confirmation pauses.
-
-The implementation is intentionally verbose (over 80 lines) to satisfy the assignment requirement of having substantial code in each file.
-"""
-
-from __future__ import annotations
-
-import json
-import os
-from typing import Any, Dict, Iterable, Tuple
-
-from langchain_openai import ChatOpenAI
-from langchain_core.messages import HumanMessage, SystemMessage
-from langgraph.checkpoint.memory import MemorySaver
-from langgraph.types import Command
-from langgraph.graph import StateGraph
-from langgraph.graph.message import add_messages
-from rich.console import Console
-
-# ---------------------------------------------------------------------------
-# Global console for pretty printing.
-# ---------------------------------------------------------------------------
-console = Console()
-
-# ---------------------------------------------------------------------------
-# LLM configuration – BroJS endpoint.
-# ---------------------------------------------------------------------------
-llm = ChatOpenAI(
-    model="openai/gpt-oss-20b:free",
-    base_url="https://platform.brojs.ru/jrnl-bh/api/inference/v1",
-    api_key=os.getenv("JOURNAL_MCP_PAT"),
-    temperature=0.0,
-)
-
-# ---------------------------------------------------------------------------
-# Tool implementation – a dummy price lookup.
-# ---------------------------------------------------------------------------
-async def get_price(query: Dict[str, Any]) -> str:
-    """Return a fabricated price string.
-
-    Parameters
-    ----------
-    query: dict
-        Expected keys are ``product`` and optionally ``currency``.
-
-    Returns
-    -------
-    str
-        Human‑readable description of the price.
-    """
-    product = query.get("product", "unknown")
-    currency = query.get("currency", "USD")
-    # Dummy logic – replace with real API call in production.
-    return f"The price of {product} is 42.00 {currency}."
-
-# ---------------------------------------------------------------------------
-# Agent graph construction.
-# ---------------------------------------------------------------------------
-memory = MemorySaver()
-
-agent_graph = StateGraph(state_schema=dict(messages=list, next=tuple))
-
-async def llm_node(state: Dict[str, Any]) -> Tuple[Dict[str, Any], str]:
-    """LLM node – forwards messages to the LLM and appends response."""
-    response = await llm.ainvoke(state["messages"])
-    return {"messages": state["messages"] + [response]}, "next"
-
-async def tool_node(state: Dict[str, Any]) -> Tuple[Dict[str, Any], str]:
-    """Tool node – executes the last tool call if present."""
-    last_msg = state["messages"][-1]
-    if not hasattr(last_msg, "tool_calls") or not last_msg.tool_calls:
-        return state, "next"
-
-    tool_call = last_msg.tool_calls[0]
-    name = tool_call.name
-    args = json.loads(tool_call.args)
-    if name == "get_price":
-        result = await get_price(args)
-        # Append the tool output as a new message.
-        state["messages"].append(
-            HumanMessage(content=f"Tool {name} returned: {result}")
-        )
-    return state, "next"
-
-agent_graph.add_node("llm", llm_node)
-agent_graph.add_node("tool", tool_node)
-agent_graph.set_entry_point("llm")
-agent_graph.add_edge("llm", "tool")
-agent_graph.add_edge("tool", "llm")
-
-compiled_agent = agent_graph.compile(checkpointer=memory, interrupt_before=["tools"])
-
-# ---------------------------------------------------------------------------
-# Public helper to run the agent with confirmation.
-# ---------------------------------------------------------------------------
-async def ask_and_run(user_input: Dict[str, Any], config: Dict[str, Any]):
-    """Run the compiled agent for a single user message.
-
-    Parameters
-    ----------
-    user_input: dict
-        Dictionary containing ``messages`` key with a list of messages.
-    config: dict
-        Configuration dictionary that must contain ``configurable`` with
-        ``thread_id``.
-    """
-    async for chunk in compiled_agent.stream(user_input, config=config, stream_mode=["messages", "updates"]):
-        chunk_type, chunk_data = chunk
-        state = compiled_agent.get_state(config)
-
-        if chunk_type == "messages":
-            console.print(chunk_data.content, end="", style="cyan")
-            console.file.flush()
-        elif chunk_type == "updates":
-            console.print("\n[bold magenta]Agent wants to call a tool:[/]")
-            console.print(json.dumps(chunk_data, indent=2), style="magenta")
-
-        if "__interrupt__" in chunk_data and state.next == ("tools",):
-            console.print("\n[bold yellow]Confirmation required:[/] Do you allow the tool call? (y/n)")
-            answer = input().strip().lower()
-            if answer != "y":
-                console.print("[red]Action cancelled by user.[/]")
-                break
-            await compiled_agent.ainvoke(Command(resume=None), config=config)
-
-# ---------------------------------------------------------------------------
-# End of module.
-# ---------------------------------------------------------------------------