feat: solution for 'Human-in-the-Loop через middleware'

.gitignore

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

README.md

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+# Human-in-the-Loop через middleware
+
+Главная
+Мои задания
+Human-in-the-Loop через middleware
+5Д
+EN
+Human-in-the-Loop через middleware
+Зачёт
+Версия 1
+Дедлайн сдачи: 31.08.2026
+
+В работе
+
+Редактирование ответа
+
+Заполните ответ и отправьте работу на проверку преподавателю.
+
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+
+Задание
+
+Задание: Human-in-the-Loop через middleware
+Цель
+
+Доработать агента с HumanInTheLoopMiddleware: при каждом вызове инструмента агент останавливается

requirements.txt

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+langchain==0.2.0
+langgraph==0.0.1
+openai==1.3.0

src/main.py

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+#!/usr/bin/env python3
+"""
+Human-in-the-Loop Agent Demo
+
+This script demonstrates how to use LangChain's HumanInTheLoopMiddleware
+to pause an agent when it wants to call a tool, let the user approve or
+reject the call, and then resume execution.
+
+Requirements:
+- langchain
+- langgraph
+- openai (for ChatOpenAI)
+"""
+
+import os
+import sys
+from typing import List, Dict, Any
+
+# LangChain imports
+from langchain.chat_models import ChatOpenAI
+from langchain.tools import tool
+from langchain.agents import create_agent
+from langchain.agents.middleware import HumanInTheLoopMiddleware
+
+# LangGraph imports
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.schema import Command
+
+# --------------------------------------------------------------------------- #
+# Tool definition
+# --------------------------------------------------------------------------- #
+
+@tool
+def get_weather(city: str) -> str:
+    """
+    Return a simple weather description for the given city.
+    """
+    # In a real scenario you might call an external API here.
+    return f"Sunny in {city}."
+
+# --------------------------------------------------------------------------- #
+# Agent setup
+# --------------------------------------------------------------------------- #
+
+def build_agent() -> Any:
+    """
+    Build and return a LangChain agent configured with HumanInTheLoopMiddleware.
+    """
+    # Ensure OpenAI API key is available
+    api_key = os.getenv("OPENAI_API_KEY")
+    if not api_key:
+        print("Error: OPENAI_API_KEY environment variable not set.")
+        sys.exit(1)
+
+    llm = ChatOpenAI(
+        model_name="gpt-3.5-turbo",
+        temperature=0,
+        openai_api_key=api_key,
+    )
+
+    agent = create_agent(
+        model=llm,
+        tools=[get_weather],
+        system_prompt="Ты полезный ассистент",
+        middleware=[
+            HumanInTheLoopMiddleware(
+                interrupt_on={"get_weather": True},
+                description_prefix="Подтвердите вызов инструмента",
+            ),
+        ],
+        checkpointer=MemorySaver(),
+    )
+    return agent
+
+# --------------------------------------------------------------------------- #
+# Human-in-the-loop loop
+# --------------------------------------------------------------------------- #
+
+def prompt_decision(request: Dict[str, Any]) -> str:
+    """
+    Prompt the user for a decision on a tool call.
+    Returns 'approve' or 'reject'.
+    """
+    name = request.get("name", "unknown")
+    args = request.get("args", {})
+    description = request.get("description", "")
+
+    print("\n=== Tool Call ===")
+    print(f"Name: {name}")
+    print(f"Args: {args}")
+    if description:
+        print(f"Description: {description}")
+
+    while True:
+        choice = input("Approve (a) / Reject (r) [a/r]: ").strip().lower()
+        if choice == "a":
+            return "approve"
+        elif choice == "r":
+            return "reject"
+        else:
+            print("Invalid input. Please enter 'a' to approve or 'r' to reject.")
+
+def run_agent(agent: Any, user_message: str, thread_id: str = "session-1") -> None:
+    """
+    Run the agent with Human-in-the-Loop, handling pauses and resumes.
+    """
+    config = {"configurable": {"thread_id": thread_id}}
+
+    # Initial invocation
+    result = agent.invoke(
+        {"messages": [{"role": "human", "content": user_message}]},
+        config=config,
+    )
+
+    # Loop until the agent finishes (no '__interrupt__' key)
+    while "__interrupt__" in result:
+        interrupt = result["__interrupt__"][0]
+        # The interrupt value is a dict with action_requests and review_configs
+        interrupt_value = interrupt.get("value", {})
+        action_requests = interrupt_value.get("action_requests", [])
+        # review_configs = interrupt_value.get("review_configs", [])
+
+        decisions = []
+        for req in action_requests:
+            decision = prompt_decision(req)
+            decisions.append(
+                {
+                    "name": req["name"],
+                    "args": req["args"],
+                    "decision": decision,
+                }
+            )
+
+        # Resume the agent with the collected decisions
+        result = agent.invoke(
+            Command(resume={"decisions": decisions}),
+            config=config,
+        )
+
+    # Agent finished; display the final response
+    messages = result.get("messages", [])
+    if messages:
+        # Find the last assistant message
+        for msg in reversed(messages):
+            if msg.get("role") == "assistant":
+                print("\n=== Agent Response ===")
+                print(msg.get("content", "").strip())
+                break
+    else:
+        print("\nNo messages returned by the agent.")
+
+# --------------------------------------------------------------------------- #
+# Main entry point
+# --------------------------------------------------------------------------- #
+
+if __name__ == "__main__":
+    agent = build_agent()
+    print("Human-in-the-Loop Agent Demo")
+    print("----------------------------")
+    user_input = input("Введите ваш запрос: ").strip()
+    if not user_input:
+        print("Empty input. Exiting.")
+        sys.exit(0)
+    run_agent(agent, user_input)