@DanKornas: Building a research agent is one problem. Connecting its search loop, backend, and usable frontend is another. Gemini F…
Summary
An open-source full-stack research agent example using Gemini, LangGraph, React, and Google Search, demonstrating an end-to-end workflow with dynamic query generation, reflection loops, and cited answers.
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Cached at: 07/22/26, 08:35 PM
Building a research agent is one problem. Connecting its search loop, backend, and usable frontend is another.
Gemini Fullstack LangGraph Quickstart is a full-stack research-agent example for builders learning how to connect a React UI to a LangGraph backend.
It helps you study an end-to-end research workflow by generating search queries with Gemini, using Google Search, reflecting on knowledge gaps, and iterating before producing a cited answer.
Key features: • Full-stack setup – React and Vite frontend paired with a LangGraph backend • Dynamic query generation – Gemini creates initial and follow-up searches • Web research – Google Search finds relevant pages for the user’s query • Reflection loop – the agent checks for knowledge gaps and refines its research • Cited answers – gathered information is synthesized with source citations
It’s open-source (Apache License 2.0 license).
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