alihan/gemini-fullstack-langgraph-quickstart
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This project demonstrates a fullstack application using a React frontend and a LangGraph-powered backend agent. The agent is designed to perform comprehensive research on a user's query by dynamically generating search terms, querying the web using Google Search, reflecting on the results to identify knowledge gaps, and iteratively refining its search until it can provide a well-supported answer with citations. This application serves as an example of building research-augmented conversational AI using LangGraph and Google's Gemini models.
![Gemini Fullstack LangGraph](./app.png)
<img src="./app.png" title="Gemini Fullstack LangGraph" alt="Gemini Fullstack LangGraph" width="90%">
## Features
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The core of the backend is a LangGraph agent defined in `backend/src/agent/graph.py`. It follows these steps:
![Agent Flow](./agent.png)
<img src="./agent.png" title="Agent Flow" alt="Agent Flow" width="50%">
1. **Generate Initial Queries:** Based on your input, it generates a set of initial search queries using a Gemini model.
2. **Web Research:** For each query, it uses the Gemini model with the Google Search API to find relevant web pages.
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## License
This project is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for details.
This project is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for details.