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Gemini Fullstack LangGraph Quickstart

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GitHub Jupyter Notebook Apache-2.0

Description

Official Google Gemini fullstack quickstart using LangGraph. Complete React + Python implementation for building production AI agent applications.

Key Features

  • Fullstack application with React frontend and LangGraph-powered backend agent
  • Dynamic search query generation using Google Gemini models
  • Integrated web research via Google Search API
  • Reflective reasoning to identify knowledge gaps and iteratively refine searches
  • Generates well-supported answers with source citations
  • Hot-reloading for both frontend and backend during development

Use Cases

💡 Research assistant that performs deep web research on complex topics
💡 Knowledge synthesis platform combining multiple web sources with citations
💡 Conversational AI with real-time web search capabilities
💡 Educational tool for students to explore topics with verified sources
💡 Template for building production-grade LangGraph + Gemini applications

Strengths & Limitations

✅ Strengths

  • • High community interest (18.3k stars)
  • • Permissive open-source license (Apache-2.0)
  • • Responsive to issues, low backlog

Tags

gemini langgraph fullstack react python

Categories

🤖 Agent Framework 📚 RAG Tools

Quick Start

cd frontend && npm install && cd ../backend && pip install . && cp .env.example .env && echo 'GEMINI_API_KEY=your_key' > .env && cd .. && make dev
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