# Human‑in‑the‑Loop Middleware Demo

This repository contains a minimal **LangChain** agent that demonstrates how to pause execution at every tool call and ask the user for approval or rejection before continuing.  
The core of this behaviour is provided by `HumanInTheLoopMiddleware`, which automatically injects a confirmation step into the agent’s workflow.

> ⚠️  The example uses the open‑source **Ollama** LLM backend.  
> If you don’t have Ollama installed, follow the instructions in the *Prerequisites* section.

---

## Table of Contents

- [Description](#description)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Running the Demo](#running-the-demo)
- [Example Interaction](#example-interaction)

---

## Description

`solution.py` implements a LangChain agent that:

1. **Creates** an LLM chain using `ChatOllama`.
2. **Wraps** it with a `MemorySaver` checkpoint for state persistence.
3. **Adds** the `HumanInTheLoopMiddleware`, which:
   - Intercepts every tool call,
   - Prints the intended action to the terminal (via `rich`),
   - Prompts the user for `"approve"` or `"reject"`,
   - Resumes execution based on the response.

This pattern is useful when you want a human operator to supervise or validate each step of an automated workflow, without having to manually interrupt the agent.

---

## Prerequisites

| Item | Version | Notes |
|------|---------|-------|
| Python | ≥ 3.10 | Tested with 3.11 |
| pip | – | Standard package installer |
| **Ollama** | Latest | Install from https://ollama.ai/ and run `ollama serve` before starting the demo. |
| **OpenAI‑compatible model** | e.g., `llama2:7b` | Pull via `ollama pull llama2:7b`. |

> **Tip:** If you prefer a different LLM, replace `ChatOllama` with any LangChain-compatible LLM (e.g., OpenAI, Anthropic).

---

## Installation

```bash
# 1. Clone the repo
git clone https://github.com/your-username/human-in-the-loop-demo.git
cd human-in-the-loop-demo

# 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 langchain-ollama rich
```

> **Note:**  
> `langgraph` is required for the checkpointing and middleware support.

---

## Running the Demo

```bash
# Make sure Ollama server is running:
#   ollama serve

# Run the agent script
python solution.py
```

You will be prompted to enter a question. The agent will then:

1. Generate a plan.
2. For each tool call, pause and display the intended action.
3. Await your `"approve"` or `"reject"` input before proceeding.

---

## Example Interaction

```text
$ python solution.py
Enter your question: What is the capital of France?

[Agent] Thinking...
[HumanInTheLoopMiddleware] Tool call planned: "search" with query "capital of France"
Approve? (yes/no): yes

[Agent] Executing tool...
Result: Paris

[Agent] Continuing...

Final answer: The capital of France is Paris.
```

If you type `no` instead of `yes`, the agent will skip that tool call and attempt to continue with its plan, potentially leading to a different outcome.

---

## Customization

- **Different tools** – Add more tools via `@tool` decorators or by passing a list to `create_agent`.
- **Custom prompt** – Modify the middleware’s confirmation message by subclassing `HumanInTheLoopMiddleware`.
- **Persist state** – The `MemorySaver` checkpoint can be swapped for a database-backed store if you need long‑term persistence.

---

Happy experimenting! 🚀