# 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`.

---

## Table of Contents

- [Features](#features)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Running the Agent](#running-the-agent)
  - [Interactive Demo (`solution.py`)](#interactive-demo-solutionpy)
  - [Unit Tests (`tests/test_solution.py`)](#unit-tests-test_solutionpy)
- [Example Usage](#example-usage)
- [License](#license)

---

## 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. |

---

## Prerequisites

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

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

---

## Installation

1. **Clone the repository**

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

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 -r requirements.txt
   ```

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

   ```text
   langchain-openai>=0.2.0
   langgraph>=0.1.0
   ```

---

## Running the Agent

### Interactive Demo (`solution.py`)

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

```bash
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.

---

### Unit Tests (`tests/test_solution.py`)

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

```bash
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.

---

## Example Usage

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

```python
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.

---

## License

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