dz/solutions/69a86305c46fd26feae6bcaa_Human-in-the-Loop_через_middleware

Human‑in‑the‑Loop Agent via Middleware (LangGraph)

This repository contains a minimal example of a Human‑in‑the‑Loop agent built on top of LangGraph.
The agent pauses whenever it needs to call an external tool, prints the tool request and waits for a user decision (approve or reject). After the decision is supplied, execution resumes automatically.

⚠️ The example uses OpenAI’s API (or any compatible LLM). Make sure you have an API key set in the environment variable OPENAI_API_KEY.

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Table of Contents

• Features
• Prerequisites
• Installation
• Running the Agent
  • Interactive Demo (solution.py)
  • Unit Tests (tests/test_solution.py)
• Example Usage
• License

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Features

Feature	Description
Human‑in‑the‑Loop	Agent stops before calling any tool, prints the request and waits for user input.
Interrupts via interrupt_before=["tools"]	Configurable interruption point in LangGraph.
Tool Example	Simple get_weather(city, date) function that returns a mock weather string.
Checkpointing	Uses an in‑memory checkpoint (MemorySaver) to preserve state across interruptions.

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Prerequisites

• Python 3.10+
• An OpenAI API key (or any compatible LLM endpoint)

export OPENAI_API_KEY="sk-..."

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Installation

1. Clone the repository

   git clone https://github.com/yourusername/human-in-loop-langgraph.git
   cd human-in-loop-langgraph
   

2. Create a virtual environment (optional but recommended)

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

3. Install dependencies

   pip install -r requirements.txt
   

   If you don’t have a requirements.txt, create one with the following content:

   langchain-openai>=0.2.0
   langgraph>=0.1.0
   

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Running the Agent

Interactive Demo (solution.py)

The main script demonstrates how to start the agent and handle interruptions.

python solution.py

What happens:

1. The user enters a prompt (e.g., “What’s the weather in Paris on 2024‑12‑01?”).

2. The agent processes the request, decides it needs to call get_weather, and pauses.

3. The tool request is printed:

   Tool requested: get_weather
   Arguments: {'city': 'Paris', 'date': '2024-12-01'}
   

4. You are prompted to type approve or reject.

5. After your decision, the agent resumes and prints the final answer.

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Unit Tests (tests/test_solution.py)

Run the test suite to verify that the interruption logic works as expected:

pytest tests/test_solution.py

The tests simulate a user approving the tool call automatically and check that the final output contains the weather string.

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Example Usage

Below is a quick snippet you can paste into a Python REPL or another script to see the agent in action:

from solution import agent, memory  # assuming solution.py defines them

# Start a new thread/session
config = {"configurable": {"thread_id": "demo-session"}}

# Invoke with a user message
response = agent.invoke(
    {"messages": [{"role": "user", "content": "What's the weather in Tokyo on 2025-01-15?"}]},
    config=config,
)

print("\nFinal response:")
print(response["messages"][-1]["content"])

When you run this, you’ll see the same interruption prompt as described above.

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License

MIT © 2026. Feel free to fork and adapt for your own projects.