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

Human‑in‑the‑Loop Agent via Middleware

This repository contains a minimal example of an LLM agent that pauses whenever it wants to call a tool and asks the user for approval before proceeding.
The core idea is to use HumanInTheLoopMiddleware from LangChain, which intercepts every tool invocation, prints a prompt with the action details, and waits for the user to respond (approve, reject, or optionally edit the request).

Why this matters – In many real‑world scenarios you want an LLM to ask for human confirmation before performing potentially sensitive actions (e.g., sending emails, accessing databases, calling external APIs).

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Table of Contents

• Project Structure
• Installation
• Running the Agent
  • Interactive Mode
  • Scripted Example
• Example Usage
• Extending the Agent

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Project Structure

├── solution.py          # Main script with the agent implementation
└── README.md            # This file

solution.py contains:

1. LLM configuration – uses ChatOpenAI.
2. A simple tool (get_weather) that returns a fake weather string.
3. Memory checkpoint via MemorySaver.
4. Agent creation with create_react_agent and the middleware.
5. Execution loop that keeps asking for user input until the conversation ends.

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Installation

1. Clone the repo

   git clone https://github.com/your-username/human-in-the-loop-agent.git
   cd human-in-the-loop-agent
   

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 --upgrade pip
   pip install langchain langgraph openai
   

4. Set your OpenAI API key

   export OPENAI_API_KEY="sk-..."
   # Windows: setx OPENAI_API_KEY "sk-..."
   

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Running the Agent

Interactive Mode

Simply run the script:

python solution.py

You will see a prompt like:

Agent wants to call tool `get_weather` with arguments:
  city = "Moscow"
  date = "2025-10-01"

Please type one of: approve / reject (or edit <new_args>)
> 

Type approve to let the agent proceed, or reject to stop it.
If you want to modify the arguments before approval, use edit city=London date=2025-12-25.

The conversation continues until the user types stop or the agent finishes its plan.

Scripted Example

You can also run a quick demo that automatically approves all calls:

python - <<'PY'
from solution import agent, llm, memory
# Override middleware to auto‑approve for demonstration
agent.middleware[0].interrupt_on = {"get_weather": False}
print(agent.run("What's the weather in New York tomorrow?"))
PY

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Example Usage

$ python solution.py
User: What's the weather in Paris next Friday?
Agent wants to call tool `get_weather` with arguments:
  city = "Paris"
  date = "2025-10-06"

Please type one of: approve / reject (or edit <new_args>)
> approve

Assistant: Погода в Париже на 2025‑10‑06: солнечно 25°C.
User: Thank you!

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Extending the Agent

1. Add more tools – decorate any function with @tool and add it to the tools list in create_react_agent.
2. Change the interrupt policy – modify interrupt_on dict (e.g., { "get_weather": True, "send_email": False }).
3. Persist conversation state – replace MemorySaver() with a database checkpoint if you need long‑term memory.
4. Custom prompts – tweak system_prompt or add a custom description_prefix.

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Happy hacking! 🚀