Perplexity-style iterative research with Gemini and Google Search

AI Comprehensive Research on User's Query with Gemini and Web Search What is this? Perform comprehensive research on a user's query by dynamically generating search terms, querying the web using Google Search (by Gemini) , reflecting on the results to identify knowledge gaps, and iteratively refining its search until it can provide a well-supported answer with citations. (like Perplexity) This workflow is a reproduction of gemini-fullstack-langgraph-quickstart in N8N. The gemini‑fullstack‑langgraph‑quickstart is a demo by the Google‑Gemini team that showcases how to build a powerful full‑stack AI agent using Gemini and LangGraph How It Works Generate Query 💬 - generates one or more search queries tasks based on the User's question. - uses Gemini 2.0 Flash Web Research 🌐 - execute web search tasks using the native Google Search API tool in combination with Gemini 2.0 Flash. Reflection 📚 - Identifies knowledge gaps and generates potential follow-up queries. Setup 1. Configure API Credentials: - Create Google Gemini(PaLM) Api Credential using you own Gemini key - Connect the credential with three nodes: Google Gemini Chat Model and GeminiSearch and reflection 2. Configure Redis Source: - prepare a Redis service that can be accessed by n8n - Create Redis Crediential and connect it with all Redis node Customize - Try using different Gemini models. - Try modifying the parameters numberofinitialqueries and maxresearchloops. Why use Redis? Use Redis as an external storage to maintain global variables (counter, search results, etc.) This workflow contains a loop process, which need global variables (as State in LangGraph). It is difficult to achieve global variables management without external storage in n8n.