Handling "Machine Gun" Messages in LangGraph: Debounce vs. Other Solutions?

Hi everyone,

I’m currently building an AI agent using LangGraph that features real-time messaging. The system allows both humans and the AI to participate in the chat.

I've run into a common UX issue: users often send multiple short messages in quick succession (e.g., "Hi," then "How are you?" then "I need help with..."). Currently, my API treats each message as a separate trigger. This leads to:

1. High token consumption.
2. Fragmented context for the model, as it starts processing before the user finishes their thought.

I’m considering implementing a Debounce mechanism to "collect" messages sent within a 2-3 second window, concatenate them, and send them to the model as a single prompt.

My questions for the community:

• Is Debounce the industry standard for this, or is there a more elegant way to handle "fragmented" input in LangGraph?
• How do you handle the UI/UX side? Do you show a "typing" indicator while waiting for the debounce timer?
• Are there any specific LangGraph patterns (nodes/state updates) you’d recommend for buffering input?

I’d like to hear how you’ve tackled this or if there are any pitfalls I should watch out for. Thanks in advance!