Listen: Query Fan-Out Prompt Implementation in Google’s Open-Source Agentic Framework

Imagine building your own deep-research assistant that doesn't just search the web, but actually thinks, reflects, and cites its sources. Google's open-source Gemini Fullstack LangGraph Quickstart shows you exactly how to do this.

By pairing the Gemini 2.5 large language model with LangGraph, this project creates a transparent, multi-step research agent. It operates through a clever four-part loop.

First, the Query Writer analyzes your request and generates targeted search terms. Next, the Web Searcher uses those terms to run live Google searches, gathering the most up-to-date information and summarizing the findings.

Then comes the clever part: Reflection. The agent inspects its own summary, looks for any missing details or knowledge gaps, and drafts follow-up queries to fill those holes. This loop continues until the research is complete. Finally, the agent synthesizes everything into a polished, citation-backed answer.

While this isn't Google's official consumer tool, it provides a perfect, practical blueprint for anyone wanting to build a sophisticated, self-correcting research pipeline.