Human‑in‑the‑loop (interrupt / resume) – LangGraph example

What this project does

This repository contains a minimal, self‑contained example that demonstrates how to use LangGraph’s custom interrupt mechanism for Human‑in‑the‑loop (HITL). The graph pauses at a node, asks the user for confirmation via an interactive console prompt, and then resumes execution with the chosen answer.

The key concepts shown are:

1. State definition – a TypedDict that holds data produced by the user.
2. Interrupt node – uses interrupt(payload) to pause the graph.
3. Resume handling – the main loop detects __interrupt__, shows a question, collects an answer and sends it back with Command(resume=payload).
4. Checkpointing – InMemorySaver keeps the graph state so that resumption works correctly.

File structure

File	Purpose
requirements.txt	Python dependencies (langgraph, questionary, python‑dotenv)
main.py	Entry point – builds and runs the graph, handles interrupts
README.md	Project description and usage instructions

Installation

# Create a virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate   # On Windows use `.venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt

Running the example

python main.py

You will see a prompt asking whether you want to continue. Choose an option and the graph will resume, printing the final state.

How it works (high‑level)

1. Graph construction – two nodes: ask_user (interrupt) and process_answer (continues after resume).
2. Interrupt payload – a dictionary with type, question, and options.
3. Main loop – streams the graph, intercepts __interrupt__, uses questionary.select to get user input, then resumes the graph with the answer attached.
4. Final state – after completion, the final state is printed showing the chosen value.

Extending the example

• Replace the console prompt with a web UI or chatbot interface.
• Add more nodes before/after the interrupt to build richer workflows.
• Persist checkpoints to disk using FileSaver if you need long‑term state.

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Author: Kirill Kutlakhmetov