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 # Interactive Story Game with LLM and Interrupts  
 A lightweight “choose‑your‑own‑adventure” text game that uses a large language model (LLM) to generate story snippets, pauses for user input, then continues the narrative based on the chosen option. The project demonstrates how to build a LangGraph pipeline with human‑in‑the‑loop interruptions and checkpoints.
 ---
 ## 📖 Overview
 1. **LLM generates an opening scene** – a short paragraph describing the setting and presenting several possible actions.
 2. **Interrupt** – the graph pauses, displaying the options in the console via `questionary`.
 3. **User selects an option** – the choice is stored in the graph state.
 4. **LLM writes a brief ending** that follows the chosen path.
 The result is a chain of prompts:  
 `LLM → pause & user input → LLM (ending)`.
 ---
 ## 🚀 Getting Started
 ### Prerequisites
 - Python 3.10+
 - An OpenAI API key (or any other model supported by LangChain)
 > **Tip:** If you prefer not to use OpenAI, replace the `OpenAI` wrapper with another LangChain LLM provider in `main.py`.
 ### Installation
 ```bash
 # Clone the repo
 git clone https://github.com/your-username/interactive-story-game.git
 cd interactive-story-game
 # Create a virtual environment (optional but recommended)
 python -m venv .venv
 source .venv/bin/activate   # On Windows: .\.venv\Scripts\activate
 # Install dependencies
 pip install langgraph==0.1.2 \
           langchain==1.2.10 \
           langchain-openai==1.1.9 \
           questionary
 ```
 > **Environment variable**  
 > Set your OpenAI key before running the game:
 > ```bash
 > export OPENAI_API_KEY="sk-..."
 > # Windows PowerShell
 > $env:OPENAI_API_KEY = "sk-..."
 > ```
 ---
 ## 📁 Project Structure
 | File | Purpose |
 |------|---------|
 | `main.py` | Entry point – builds the graph, runs it, and handles user interaction. |
 | `graph.py` | Defines the LangGraph nodes, state schema, and interrupt logic. |
 | `utils.py` | Helper functions (e.g., prompt templates). |
 | `requirements.txt` | List of dependencies (for reproducibility). |
 ---
 ## 🛠️ Running the Game
 ```bash
 python main.py
 ```
 You will see something like:
 ```
 === Interactive Story ===
 Topic: A mysterious forest
 Scene:
  You find yourself at the edge of a dark, misty forest. The trees loom overhead, their branches tangled like fingers.
 Options:
 1) Step into the forest.
 2) Walk along the clearing's edge.
 3) Call out for help.
 Enter your choice (1‑3): 
 ```
 After you type `1`, the game will ask the LLM to finish the story based on that choice and print a short ending.
 ---
 ## 🔧 Customization
 ### Changing the Prompt
 Edit `utils.py`:
 ```python
 def generate_scene_prompt(topic: str) -> str:
    return f"Topic: {topic}\nWrite a short opening scene with 3 possible actions."
 ```
 ### Adding More Nodes
 You can split the graph into separate nodes for “generate scene” and “write ending”, or add additional decision points.
 ---
 ## 🎮 Example Session
 ```text
 $ python main.py
 === Interactive Story ===
 Topic: A forgotten laboratory
 Scene:
  The lab is dimly lit, with humming machines and a faint smell of ozone. In the center lies an ancient console flickering with static.
 Options:
 1) Approach the console.
 2) Inspect the surrounding equipment.
 3) Leave the lab immediately.
 Enter your choice (1‑3): 2
 Ending:
 You carefully examine the equipment, discovering a hidden panel that opens to reveal a secret passage. The adventure continues...
 ```
 ---
 ## 📜 License
 This project is released under the MIT License – feel free to adapt and expand it for your own experiments with LLMs and human‑in‑the‑loop systems.
 ---