This workflow is designed to automate real-time research and intelligent responses based on incoming chat messages. It begins with a trigger that activates whenever a message is received, such as a user query or prompt. The message is then processed through an AI agent that leverages multiple services: it first queries SerpAPI to gather the latest web data, consults a window buffer memory for contextual history, and uses a powerful language model (like Google’s Gemini or OpenAI’s GPT) to analyze the combined information. This setup enables instant, up-to-date responses that are highly relevant and context-aware, making it ideal for researchers, support teams, content creators, and analysts who need timely, accurate data analysis and fact-checking. The workflow is fully customizable, allowing users to swap AI models, modify memory settings, and integrate different chat platforms, providing a flexible tool for live information gathering and response generation.
Real-Time AI Research Agent for Instant Data Analysis
Node Count | 6 – 10 Nodes |
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Nodes Used | @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmChatGoogleGemini, @n8n/n8n-nodes-langchain.lmChatOpenAi, @n8n/n8n-nodes-langchain.manualChatTrigger, @n8n/n8n-nodes-langchain.memoryBufferWindow, @n8n/n8n-nodes-langchain.toolSerpApi, stickyNote |
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