feat: solution for 'Практическое задание №3: Память и подтверждение действий'

.gitignore

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+node_modules/
+.env
+dist/
+build/
+*.log

README.md

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+# Практическое задание №3: Память и подтверждение действий
+
+Главная
+Мои задания
+Практическое задание №3: Память и подтверждение действий
+5Д
+EN
+Практическое задание №3: Память и подтверждение действий
+Зачёт
+Версия 1
+Дедлайн сдачи: 31.08.2026
+
+В работе
+
+Редактирование ответа
+
+Заполните ответ и отправьте работу на проверку преподавателю.
+
+Тип ответа
+Текст
+Ссылка
+Файлы
+Текст ответа
+Прикреплённые файлы
+Загрузить файл
+Отправить на проверку
+Отменить
+
+Задание
+
+Цель
+
+Доработать агента из предыдущих заданий: добавить память разговора и механизм подтверждения каждо

requirements.txt

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+rich
+langgraph
+langchain
+openai

src/main.py

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+#!/usr/bin/env python3
+"""
+A simple LangGraph agent with memory and user confirmation before tool calls.
+"""
+
+import os
+import sys
+from typing import Any, Dict, Optional
+
+from langgraph import create_agent
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.tools import Tool
+from langchain.chat_models import ChatOpenAI
+from rich.console import Console
+
+# --------------------------------------------------------------------------- #
+# Configuration
+# --------------------------------------------------------------------------- #
+
+# Ensure the OpenAI API key is set
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+if not OPENAI_API_KEY:
+    print("Error: OPENAI_API_KEY environment variable not set.")
+    sys.exit(1)
+
+# Rich console for pretty output
+console = Console()
+
+# LLM model
+llm = ChatOpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)
+
+# Memory saver for conversation persistence
+memory = MemorySaver()
+
+# --------------------------------------------------------------------------- #
+# Tool definition
+# --------------------------------------------------------------------------- #
+
+def echo_tool(message: str) -> str:
+    """
+    A simple echo tool that returns the message back to the user.
+    """
+    return f"Echo: {message}"
+
+# Wrap the function as a LangGraph Tool
+echo = Tool.from_function(
+    fn=echo_tool,
+    name="echo",
+    description="Echoes back the provided message."
+)
+
+# --------------------------------------------------------------------------- #
+# Agent creation
+# --------------------------------------------------------------------------- #
+
+system_prompt = """
+You are a helpful assistant. When you need to use a tool, you will call it.
+"""
+
+agent = create_agent(
+    model=llm,
+    tools=[echo],
+    system_prompt=system_prompt,
+    checkpointer=memory,          # Enable memory
+    interrupt_before=["tools"],   # Pause before any tool call
+)
+
+# --------------------------------------------------------------------------- #
+# Conversation loop
+# --------------------------------------------------------------------------- #
+
+def ask_and_run(user_input: Optional[str], config: Dict[str, Any]) -> None:
+    """
+    Handles streaming from the agent, pauses before tool calls, and asks the user
+    for confirmation before executing the tool.
+    """
+    # If user_input is provided, send it as a new message
+    if user_input is not None:
+        # The agent expects a dict with a "messages" key
+        input_payload = {"messages": [{"role": "user", "content": user_input}]}
+    else:
+        # None means resume from the paused state
+        input_payload = {}
+
+    # Stream the agent's response
+    for chunk_type, chunk_data in agent.stream(
+        input_payload,
+        config=config,
+        stream_mode=["messages", "updates"],
+    ):
+        # Handle message chunks
+        if chunk_type == "messages":
+            # chunk_data is a list of messages; print the last one
+            if isinstance(chunk_data, list) and chunk_data:
+                last_msg = chunk_data[-1]
+                if last_msg.get("role") == "assistant":
+                    console.print(f"[bold cyan]Assistant:[/bold cyan] {last_msg.get('content', '')}")
+                elif last_msg.get("role") == "tool":
+                    console.print(f"[bold magenta]Tool Output:[/bold magenta] {last_msg.get('content', '')}")
+                else:
+                    console.print(last_msg.get("content", ""))
+            else:
+                console.print(chunk_data)
+
+        # Detect interrupt before tool call
+        if "__interrupt__" in chunk_data and agent.get_state(config).next == ("tools",):
+            # Retrieve the pending tool call
+            state = agent.get_state(config)
+            try:
+                last_message = state.values["messages"][-1]
+                tool_call = last_message.tool_calls[0]
+                tool_name = tool_call["name"]
+                tool_args = tool_call["args"]
+                console.print(f"[yellow]Agent wants to call tool:[/yellow] {tool_name}({tool_args})")
+            except Exception as e:
+                console.print(f"[red]Error retrieving tool call: {e}[/red]")
+                break
+
+            # Ask user for confirmation
+            console.print("[bold]Allow tool execution? (Y/n):[/bold] ", end="")
+            answer = input().strip().lower()
+            if answer in ("", "y", "yes"):
+                console.print("[green]Executing tool...[/green]")
+                # Recursively resume the agent
+                ask_and_run(None, config)
+            else:
+                console.print("[red]Tool execution cancelled by user.[/red]")
+                break
+
+        # Handle updates (optional)
+        if chunk_type == "updates":
+            # For this simple example, we ignore updates
+            pass
+
+
+def main() -> None:
+    """
+    Main conversation loop.
+    """
+    # Use a fixed thread ID for this session
+    config = {"configurable": {"thread_id": "thread-1"}}
+
+    console.print("[bold green]Welcome to the LangGraph Agent![/bold green]")
+    console.print("Type your messages below. Press Ctrl+C to exit.\n")
+
+    while True:
+        try:
+            user_input = input("[bold]You:[/bold] ")
+            if not user_input:
+                continue
+            ask_and_run(user_input, config)
+        except KeyboardInterrupt:
+            console.print("\n[bold red]Exiting...[/bold red]")
+            break
+        except Exception as e:
+            console.print(f"[red]Unexpected error: {e}[/red]")
+
+
+if __name__ == "__main__":
+    main()