Webhook-Triggered Conversational AI with Memory & Search

LLM Chain

Complex Trigger

This workflow creates a sophisticated conversational AI assistant, triggered by an incoming webhook request. Upon receiving a text input via the webhook, a LangChain Agent leverages an OpenAI Chat Model (specifically `gpt-4o-mini`) to process the query. The agent is equipped with a `Window Buffer Memory` to maintain conversational context across multiple interactions, enabling more coherent and personalized responses. Furthermore, it has access to a `Serp API Tool`, allowing it to perform real-time internet searches to answer questions requiring up-to-date information or external data. This setup empowers the AI to act as a helpful assistant, capable of understanding context, recalling past dialogue, and fetching information from the web to provide comprehensive and relevant answers. It's ideal for building interactive chatbots or smart assistants that need both memory and external knowledge.