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 Credential and connect it with all Redis nodes

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.