This n8n workflow automates intelligent handling of incoming WhatsApp or SMS messages via Twilio, buffering messages with Redis, and using AI to generate contextual responses. The system determines whether to reply immediately or wait for further user input, thus creating a seamless, human-like chat experience.
The workflow begins with the Twilio Trigger capturing new inbound messages. Incoming messages are stored in a Redis list to maintain chat history. A buffer check is performed after a brief delay (5 seconds), comparing the latest message to previous ones to detect if the user is still sending follow-up messages or has paused, indicating readiness for a response.
If the messages are still part of a rapid sequence, the workflow buffers and processes all messages together, passing them to an AI agent powered by OpenAI through the Langchain integration. The AI generates a consolidated reply based on the recent interaction history, which is then sent back to the user via Twilio.
This setup is especially useful for customer support, live chat automation, or any scenario where maintaining conversational context improves user experience. It reduces the chances of fragmented responses while managing multiple quick messages efficiently.
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