# LangGraph Basic Chatbot

A basic chatbot built with LangGraph following the official tutorial. This chatbot uses OpenAI's GPT-4 model to provide conversational AI capabilities.

## Features

- 🤖 Basic conversational AI using LangGraph
- 🔄 State management with message history
- 🚀 Built with uv for fast dependency management
- 🔐 Secure API key handling

## Prerequisites

- Python 3.8 or higher
- [uv](https://docs.astral.sh/uv/) package manager
- OpenAI API key

## Setup

### 1. Clone and Navigate to Project

```bash
cd /Users/ghsioux/tmp/langgraph-pard0x
```

### 2. Install Dependencies

Dependencies are already installed with uv. The virtual environment is automatically managed.

### 3. Set up OpenAI API Key

You have several options:

#### Option A: Use the setup script
```bash
uv run setup_env.py
```

#### Option B: Set environment variable manually
```bash
export OPENAI_API_KEY='your-api-key-here'
```

#### Option C: Create a .env file
Create a `.env` file in the project root:
```
OPENAI_API_KEY=your-api-key-here
```

### 4. Run the Chatbot

```bash
uv run main.py
```

## Usage

1. Start the chatbot with `uv run main.py`
2. Type your messages and press Enter
3. The bot will respond using GPT-4
4. Type `quit`, `exit`, or `q` to end the conversation

### Example Conversation

```
🤖 LangGraph Basic Chatbot
Type 'quit', 'exit', or 'q' to exit the chat.
----------------------------------------
✅ Chatbot initialized successfully!

User: Hello! What is LangGraph?
Assistant: LangGraph is a library designed to help build stateful multi-agent applications using language models...

User: quit
👋 Goodbye!
```

## Project Structure

```
langgraph-pard0x/
├── main.py           # Main chatbot implementation
├── setup_env.py      # Environment setup helper
├── pyproject.toml    # Project configuration and dependencies
├── README.md         # This file
└── .venv/            # Virtual environment (auto-managed by uv)
```

## Dependencies

- **langgraph**: Graph-based workflow library for LLM applications
- **langchain**: Framework for developing applications with LLMs
- **langchain-openai**: OpenAI integration for LangChain
- **langsmith**: Observability and debugging for LangChain applications

## How It Works

The chatbot uses LangGraph's `StateGraph` to manage conversation state:

1. **State Definition**: Uses a `TypedDict` with `messages` field that appends new messages
2. **Graph Creation**: Creates a state graph with a single "chatbot" node
3. **LLM Integration**: Uses `init_chat_model` to initialize OpenAI's GPT-4
4. **Message Processing**: The chatbot node processes incoming messages and returns responses
5. **Streaming**: Responses are streamed back to the user in real-time

## Next Steps

This is the foundation for more advanced LangGraph features:

- Add web search tools
- Implement memory persistence
- Add human-in-the-loop capabilities
- Create multi-agent workflows

Check out the [LangGraph tutorials](https://langchain-ai.github.io/langgraph/tutorials/) for more advanced features!

## Troubleshooting

### "Please set your OPENAI_API_KEY environment variable"
- Make sure you've set your OpenAI API key using one of the methods above
- Verify your API key is valid and active

### "Error initializing chatbot"
- Check your internet connection
- Verify your OpenAI API key has sufficient credits
- Make sure all dependencies are installed correctly

### Import Errors
- Run `uv sync` to ensure all dependencies are properly installed
- Check that you're using the virtual environment created by uv