# Human‑in‑the‑Loop Middleware Demo  
A minimal LangChain project that demonstrates how to pause an agent before every tool call and let a user approve or reject the action via the terminal.

---

## Table of Contents
- [Project Overview](#project-overview)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Running the Demo](#running-the-demo)
  - [1️⃣ `main.py` – Interactive Agent](#1-mainpy---interactive-agent)
  - [2️⃣ `tool_example.py` – Custom Tool](#2-tool_exempley---custom-tool)
- [Example Interaction](#example-interaction)
- [Project Structure](#project-structure)

---

## Project Overview
The agent is created with **HumanInTheLoopMiddleware**.  
When the agent wants to use a tool (e.g., `get_weather`), it pauses, prints a prompt in the terminal, and waits for user input:

| Decision | Effect |
|----------|--------|
| `approve` | Tool call proceeds |
| `reject`  | Tool call is skipped; the agent continues reasoning |
| `edit`    | (Optional) User can modify the tool arguments before resuming |

The middleware automatically handles the pause/resume logic via `Command(resume={"decisions": [...]})`.

---

## Prerequisites
- Python 3.10+  
- A working OpenAI API key (or any LLM provider supported by LangChain)

Set your key in an environment variable:

```bash
export OPENAI_API_KEY="sk-..."
```

---

## Installation

1. **Clone the repo**

   ```bash
   git clone https://github.com/yourusername/human-in-loop-demo.git
   cd human-in-loop-demo
   ```

2. **Create a virtual environment (optional but recommended)**

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

3. **Install dependencies**

   ```bash
   pip install -r requirements.txt
   ```

> `requirements.txt` contains:
> ```
> langchain==0.2.*
> langgraph==0.1.*
> python-dotenv  # optional, for .env support
> ```

---

## Running the Demo

### 1️⃣ `main.py` – Interactive Agent

```bash
python main.py
```

You will see a prompt like:

```
Agent: What would you like to know?
User: Tell me the weather in London.
Agent: (pausing) Would you like to call tool 'get_weather' with arguments {'location': 'London'}? [approve/reject/edit]
> 
```

Type `approve`, `reject`, or `edit` and press **Enter**.  
If you choose `edit`, you’ll be prompted to modify the JSON arguments.

### 2️⃣ `tool_example.py` – Custom Tool

The demo includes a simple weather tool (`get_weather`).  
You can add more tools by editing `tools/__init__.py` or creating new modules.

---

## Example Interaction

```
$ python main.py
Agent: Hi! How can I help you today?
User: What's the weather in Paris?

Agent: (pausing) Would you like to call tool 'get_weather' with arguments {'location': 'Paris'}? [approve/reject/edit]
> approve
Tool get_weather called. Result: "Sunny, 22°C"

Agent: The current weather in Paris is Sunny, 22°C.
User: Thanks!
```

If you type `reject`, the agent will continue without calling the tool:

```
Agent: (pausing) Would you like to call tool 'get_weather' with arguments {'location': 'Paris'}? [approve/reject/edit]
> reject
Agent: I couldn't retrieve the weather. Could you provide more details?
```

---

## Project Structure

```
human-in-loop-demo/
├── main.py                # Entry point – runs the interactive agent
├── tool_example.py        # Example custom tool (get_weather)
├── requirements.txt       # Dependencies
└── README.md              # This file
```

Feel free to extend the project by adding new tools, customizing prompts, or integrating a different LLM provider. Happy hacking!