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gemini fullstack langgraph quickstart

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Basic Information

This repository is a quickstart fullstack example that demonstrates how to build a research-augmented conversational agent using a React frontend and a LangGraph-powered backend with Google Gemini models. It shows a complete developer workflow including a frontend built with Vite and Tailwind, a LangGraph/FastAPI backend that implements an automated research agent, and examples for running the agent from the command line. The agent workflow implemented in backend/src/agent/graph.py generates search queries, performs web research via the Google Search API, reflects on results to identify knowledge gaps, iteratively refines searches, and synthesizes final answers with citations. The project includes development conveniences such as hot-reloading and a CLI example and documents production deployment requirements like Redis and Postgres for LangGraph and environment variables such as GEMINI_API_KEY.

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App URL
https://github.com/google-gemini/gemini-fullstack-langgraph-quickstart
Github Repository
https://github.com/google-gemini/gemini-fullstack-langgraph-quickstart/blob/main/README.md

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App Details

Features
The repository highlights a fullstack React frontend and a LangGraph backend agent designed for iterative web research and answer synthesis. Core agent features include initial query generation by a Gemini model, integrated web research using the Google Search API, reflection and knowledge-gap analysis by a Gemini model, iterative refinement loops to improve results, and final answer synthesis with citations. Development features include hot-reloading for frontend and backend, a CLI example script (backend/examples/cli_research.py) for one-off queries, and guidance for running langgraph dev locally. Deployment guidance includes building a Docker image, an example docker-compose workflow, and notes about required services such as Redis and Postgres and optional LangSmith credentials for some deployment examples.
Use Cases
This project is useful for developers who want a practical, hands-on example of building a research-oriented conversational AI agent with LangGraph and Google Gemini. It provides concrete code for the agent flow, demonstrating how to generate search queries, call the Google Search API, perform reflective analysis to find knowledge gaps, and iterate until a supported answer with citations can be produced. The repo includes a ready React UI, CLI usage for quick testing, local development instructions, and deployment notes including Docker and required backend services, making it easier to prototype, test, and extend research-capable assistants. It also documents required environment variables and integration points so teams can reproduce and adapt the architecture for their own data and models.

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