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Get started with building Fullstack Agents using Gemini 2.5 and LangGraph
| Date | Stars |
|---|---|
| 2026-07-31 | 18288 |
| 2026-08-01 | 18288 |
| 2026-08-06 | 18297 |
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# Gemini Fullstack LangGraph Quickstart
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.
<img src="./app.png" title="Gemini Fullstack LangGraph" alt="Gemini Fullstack LangGraph" width="90%">
## Features
- 💬 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.
- 🔄 Hot-reloading for both frontend and backend during development.
## Project Structure
The project is divided into two main directories:
- `frontend/`: Contains the React application built with Vite.
- `backend/`: Contains the LangGraph/FastAPI application, including the research agent logic.
## Getting Started: Development and Local Testing
Follow these steps to get the application running locally for development and testing.
**1. Prerequisites:**
- Node.js and npm (or yarn/pnpm)
- Python 3.11+
- **`GEMINI_API_KEY`**: The backend agent requires a Google Gemini API key.
1. Navigate to the `backend/` directory.
2. Create a file named `.env` by copying the `backend/.env.example` file.
3. Open the `.env` file and add your Gemini API key: `GEMINI_API_KEY="YOUR_ACTUAL_API_KEY"`
**2. Install Dependencies:**
**Backend:**
```bash
cd backend
pip install .
```
**Frontend:**
```bash
cd frontend
npm install
```
**3. Run Development Servers:**
**Backend & Frontend:**
```bash
make dev
```
This will run the backend and frontend development servers. Open your browser and navigate to the frontend development server URL (e.g., `http://localhost:5173/app`).
_Alternatively, you can run the backend and frontend development servers separately. For the backend, open a terminal in the `backend/` directory and run `langgraph dev`. The backend API will be available at `http://127.0.0.1:2024`. It will also open a browser window to the LangGraph UI. For the frontend, open a terminal in the `frontend/` directory and run `npm run dev`. The frontend will be available at `http://localhost:5173`._
## How the Backend Agent Works (High-Level)
The core of the backend is a LangGraph agent defined in `backend/src/agent/graph.py`. It follows these steps:
<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.
3. **Reflection & Knowledge Gap Analysis:** The agent analyzes the search results to determine if the information is sufficient or if there are knowledge gaps. It uses a Gemini model for this reflection process.
4. **Iterative Refinement:** If gaps are found or the information is insufficient, it generates follow-up queries and repeats the web research and reflection steps (up to a configured maximum number of loops).
5. **Finalize Answer:** Once the research is deemed sufficient, the agent synthesizes the gathered information into a coherent answer, including citations from the web sources, using a Gemini model.
## CLI Example
For quick one-off questions you can execute the agent from the command line. The
script `backend/examples/cli_research.py` runs the LangGraph aExcerpt of 6,399 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f2f9c7691cafc721, llm:Repository description: 'Get started with building Fullstack Agents using Gemini 2.5 and LangGraph'; topics: gemini, gemini-api; language: Jupyter Notebook
matched fp:f2f9c7691cafc721, llm:Repository description: 'Get started with building Fullstack Agents using Gemini 2.5 and LangGraph'; topics: gemini, gemini-api; language: Jupyter Notebook
matched fp:f2f9c7691cafc721, llm:Repository description: 'Get started with building Fullstack Agents using Gemini 2.5 and LangGraph'; topics: gemini, gemini-api; language: Jupyter Notebook