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 number_of_initial_queries and max_research_loops.

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.