Gemini Fullstack LangGraph
Builds fullstack AI agents leveraging Google Gemini models and LangGraph for advanced web research and conversational capabilities.
About
This project offers a comprehensive fullstack application template, featuring a React frontend and a LangGraph-powered backend agent. It empowers developers to create intelligent agents capable of performing in-depth research by dynamically generating search queries, utilizing Google Search, reflecting on results to identify knowledge gaps, and iteratively refining searches. The agent synthesizes well-supported answers with citations, showcasing a practical example of building sophisticated, research-augmented conversational AI using LangGraph and Google's Gemini models.
Key Features
- Fullstack application with React frontend and LangGraph backend
- Reflective reasoning to identify knowledge gaps and refine searches
- LangGraph agent for advanced research and conversational AI
- 11,962 GitHub stars
- Dynamic search query generation using Google Gemini models
- Integrated web research via Google Search API
Use Cases
- Building research-augmented conversational AI applications
- Learning and demonstrating integration of Google Gemini models with LangGraph for complex workflows
- Developing fullstack agents with dynamic web search and iterative refinement
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