AI Practitioner

  In this tutorial, we upgrade the   stateless chatbot server   by adding stateful memory support using   LangGraph . This enables more human-like, multi-turn conversations where the model remembers previous messages. Key Features of This Upgrade Powered by  Gemini 2.5 Pro  via LangChain's integration Uses  LangGraph's MemorySaver  for session memory Built with  Flask  and CORS enabled Maintains per-user conversation history using  thread_id Difference from the Stateless Version The main differences from the stateless version are: State Management:  Introduces a  State  class using  TypedDict  to track conversation history via  messages . LangGraph Integration:  Defines a stateful workflow using  StateGraph  and persists memory using  MemorySaver . Session Memory:  Associates chat sessions with a unique  thread_id  (e.g.,  user_124 ) using LangGraph's  config...

When designing AI chatbots, a key architectural choice is whether to make your chatbot   stateless   or   stateful . Here's what that means and why it matters. Stateless Chatbots Stateless chatbots treat every user input as an isolated message. They do not remember previous interactions. This can be simple to implement but lacks conversational memory, making complex or context-driven dialogue harder to handle. Stateful Chatbots Stateful chatbots maintain memory across interactions, which allows them to provide personalized and coherent responses. They are ideal for tasks like long-form conversations, remembering user preferences, or task-driven agents. Building a Stateful Chatbot with Gemini + LangGraph Below is a complete example of how to build a stateful chatbot using  Gemini 2.5 Pro ,  LangChain , and  LangGraph . This chatbot can remember prior messages using a memory saver, and supports graph-based workflows for flexibility. # Import required librari...