gemini-fullstack-langgraph-quickstart
Get started with building Fullstack Agents using Gemini 2.5 and LangGraph
Build a fullstack application that conducts research and provides answers using AI.
From the README
docker build -t gemini-fullstack-langgraph -f Dockerfile .
- 💬 Fullstack application with a React frontend and LangGraph backend.
- 🧠 Powered by a LangGraph agent for advanced research and conversational AI.
- 🔍 Dynamic search query generation using Google Gemini models.
- 🌐 Integrated web research via Google Search API.
- 🤔 Reflective reasoning to identify knowledge gaps and refine searches.
- 📄 Generates answers with citations from gathered sources.
Excerpts from the project README on GitHub. Copyright and licensing remain with the respective authors.
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gemini-fullstack-langgraph-quickstart has 18.3k stars on GitHub. It has been forked 3.1k times. gemini-fullstack-langgraph-quickstart is written mainly in Jupyter Notebook. It has been in active development since 2025. gemini-fullstack-langgraph-quickstart is available under the Apache-2.0 license. Its main topics are gemini, gemini-api.
What is gemini-fullstack-langgraph-quickstart?
Get started with building Fullstack Agents using Gemini 2.5 and LangGraph
Is gemini-fullstack-langgraph-quickstart open source?
gemini-fullstack-langgraph-quickstart is an open-source project. It is released under the Apache-2.0 license.
Is gemini-fullstack-langgraph-quickstart free?
Yes. gemini-fullstack-langgraph-quickstart is free and open source — you can use, modify and self-host it.
What license does gemini-fullstack-langgraph-quickstart use?
gemini-fullstack-langgraph-quickstart is available under the Apache-2.0 license.
What language is gemini-fullstack-langgraph-quickstart written in?
gemini-fullstack-langgraph-quickstart is written mainly in Jupyter Notebook.
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