From 13bc8755dee704dc68b7b64e0460ea8f6fd997a5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=9A=D0=B8=D1=80=D0=B8=D0=BB=D0=BB=20=D0=9A=D1=83=D1=82?=
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 <kirillkutlahmetov10@gmail.com>
Date: Tue, 26 May 2026 13:43:26 +0000
Subject: [PATCH] add README.md

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 README.md | 56 ++++++++++++++++++++++++++++++++++++++++++++++++++++++-
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-# task-69b19fbf67bbf488a1177d94
+# 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
+
+```bash
+# 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
+
+```bash
+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.
+
+---
+
+**Author:** Kirill Kutlakhmetov