AI / ML · case study
LangGraph Chatbot Workflow
Most chatbot demos are a single prompt in a loop. This one is a stateful conversation engine: LangGraph manages the dialogue as an explicit graph, and every state change streams to the browser in real time.
State as a graph
LangGraph models the conversation as nodes and transitions instead of one long prompt. User input is routed through the graph, and structured state updates at every step, which makes the flow debuggable and extensible in a way ad-hoc prompt chains are not.
Real-time synchronization
Client and server stay in lockstep over WebSockets. As the graph advances, the frontend receives the updated state instantly and renders both the conversation history and the raw structured JSON state, so you can watch the machine think.
Running it
The model layer is a local LLM rather than a hosted API, and the whole system is deployed on Render behind a lightweight HTML, CSS, and JavaScript frontend.