# 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).

---

## Table of Contents

- [Project Structure](#project-structure)
- [Installation](#installation)
- [Running the Agent](#running-the-agent)
  - [Interactive Mode](#interactive-mode)
  - [Scripted Example](#scripted-example)
- [Example Usage](#example-usage)
- [Extending the Agent](#extending-the-agent)

---

## 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.

---

## Installation

1. **Clone the repo**

   ```bash
   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)**

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

3. **Install dependencies**

   ```bash
   pip install --upgrade pip
   pip install langchain langgraph openai
   ```

4. **Set your OpenAI API key**

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

---

## Running the Agent

### Interactive Mode

Simply run the script:

```bash
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:

```bash
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
```

---

## Example Usage

```bash
$ 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!
```

---

## 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`.

---

Happy hacking! 🚀