Powered by Bright Data
An end-to-end solution for building AI-enriched spreadsheets with real-time web access. The application combines Bright Data's advanced web scraping and search capabilities with AI foundation models to transform your business spreadsheets with intelligent web-sourced information.
With this application, you can:
- 📊 Enrich spreadsheet cells with AI-generated content backed by live web data
- 🧠 Entity extraction and data processing with advanced LLMs
- 🔄 Process entire columns in batch for efficient data enhancement
- 📑 Access source citations for all web-sourced information
- 📂 Export your enriched data as CSV files for further use
- 🔍 Deep research mode with comprehensive multi-source verification
Designed for ease of customization, you can extend this core implementation to:
- Integrate proprietary data sources
- Modify the agent architecture
- Configure different AI foundation models
- Perform advanced web scraping and data extraction using Bright Data's comprehensive toolkit
This application requires API keys from the following services:
- Bright Data API
- Google Gemini API (for LLM processing)
- OpenRouter API (optional, for deep research mode)
a. Create a .env file in the project's root directory with your API keys:
BRIGHT_DATA_API_TOKEN=<your Bright Data API token>
WEB_UNLOCKER_ZONE=<your web unlocker zone (default: unblocker)>
BROWSER_ZONE=<your scraping browser zone (default: scraping_browser)>
GOOGLE_API_KEY=<your Google Gemini API key>
OPENROUTER_API_KEY=<your OpenRouter API key (optional, for deep research)>b. Create a .env.development file in the ui directory with:
VITE_API_URL=http://localhost:8000
VITE_WS_URL=ws://localhost:8000- Create a virtual environment and activate it:
python3.11 -m venv venv
source venv/bin/activate # On Windows: .\venv\Scripts\activate- Install dependencies:
python3.11 -m pip install -r requirements.txt- From the root of the project, run the backend server:
python app.py- Alternatively, build and run the backend using Docker from the root of the project:
# Build the Docker image
docker build -t brightdata-spreadsheet .
# Run the container
docker run -p 8000:8000 --env-file .env brightdata-spreadsheet- Navigate to the frontend directory:
cd ui- Install dependencies:
npm install- Start the development server:
npm run dev- Launch the app in your browser with http://localhost:5174/
This repository includes everything required to create a functional spreadsheet enrichment tool with web access:
📡 Backend (backend/)
The core backend logic, powered by LangGraph and Bright Data MCP:
graph.py– Defines the agent architecture, state management, and processing nodes using Bright Data's web scraping capabilities.
🌐 Frontend (ui/)
Interactive React frontend for dynamic user interactions and spreadsheet responses.
app.py– FastAPI server that handles API endpoints and orchestrates the enrichment pipeline.
- Bright Data MCP: Advanced web scraping and data collection through Model Context Protocol
- LangGraph: Agent workflow orchestration and state management
- FastAPI: High-performance API backend
- React: Interactive frontend interface
- AI Models: Intelligent data processing and extraction
- Fast, efficient web search and data extraction
- Single-source verification
- Optimized for speed and basic enrichment tasks
- Multi-source verification across 3+ independent sources
- Authority prioritization: LinkedIn profiles, company pages, press releases
- Iterative refinement with 5-7 search iterations
- Advanced browser automation for complex sites
- Comprehensive fact-checking and validation
POST /api/enrich/batch: Endpoint that handles batched agent execution and spreadsheet population.GET /api/health: Health check endpoint.
Feel free to submit issues and enhancement requests!
Have questions, feedback, or looking to build a custom solution? We'd love to hear from you!
- Email our team directly for support and customization requests