LangGraph ChatBot

A web-based chatbot application powered by LangGraph with memory functionality. This chatbot maintains conversation history within threads (short-term memory) and can store user information across conversations (long-term memory).

Features

• Interactive web interface with real-time responses
• Thread-based conversation management with unique thread IDs
• Short-term memory using InMemorySaver to maintain conversation history
• Long-term memory using InMemoryStore for user information
• System message context injection based on stored user information
• Markdown support in chat responses

Requirements

• Python 3.8+
• Flask
• LangGraph
• LangChain
• OpenAI API key (or other supported LLM provider)

Installation

1. Clone the repository
2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up your API key in the environment or update chatbot.py

Running Locally

python app.py

The application will be available at http://localhost:5001

Deployment

This application can be deployed to various platforms. Here are some options:

Option 1: Render

1. Create a new account on Render if you don't have one
2. Click "New" and select "Blueprint" (or "Web Service" if you prefer manual setup)
3. Connect your GitHub repository
4. Render will automatically detect the render.yaml configuration
5. Add your OPENAI_API_KEY as an environment variable in the Render dashboard
6. Deploy the application

Option 2: Heroku

1. Create a new app on Heroku
2. Connect your GitHub repository or use the Heroku CLI
3. Add your OPENAI_API_KEY as a config var in the Heroku dashboard
4. Deploy the application

Option 3: Python Anywhere

1. Create an account on PythonAnywhere
2. Upload your code or clone from GitHub
3. Set up a web app with Flask
4. Add your OPENAI_API_KEY as an environment variable
5. Configure the WSGI file to point to your app.py

Option 4: Netlify, Vercel, or other platforms

This application can also be deployed to other platforms that support Python web applications. Please refer to the respective platform's documentation for deployment instructions.

Project Structure

• app.py: Flask web server
• chatbot.py: LangGraph chatbot implementation with memory
• templates/: HTML templates
• static/: CSS and JavaScript files
• visualize_graph.py: Utility for visualizing the LangGraph