Scraping Bee is a web scraping API that simplifies the process of extracting data from websites by handling proxies, browsers and CAPTCHAs.
Enhance your data collection with AI agents that can automatically gather, process and take action on web data.
Scraping Bee handles the complexities of web data extraction with automated proxy rotation and CAPTCHA solving. Relevance AI transforms this raw data into intelligent insights through AI agents that can analyze, process and act on web data at scale.
Dynamic Data Acquisition
Agents can continuously gather real-time data from various online sources.
Enhanced Decision-Making
Empowers agents to provide timely and accurate information for informed choices.
Competitive Intelligence Insights
Enables agents to monitor market trends and competitor strategies in real-time.
Relevance AI gives you access to Scraping Bee's web scraping capabilities within your AI agent workflows.
What you’ll need
You don't need to be a developer to set up this integration. Follow this simple guide to get started:
- A Relevance AI account
- An Airtable account with access to the base and table you'd like to use
- Authorization (you'll connect securely using OAuth—no sensitive info stored manually)
Security & Reliability
The integration leverages secure OAuth authentication with ScrapingBee's API, enabling authorized web scraping and data extraction through Relevance AI's workflow engine. The system automatically handles request methods, headers, and response parsing—converting raw HTML and JSON into structured, analysis-ready data.
Built-in error handling and rate limiting ensure reliable scraping operations, while maintaining compliance with target site policies.
No training on your data
Your data remains private and is never utilized for model training purposes.
Security first
We never store anything we don’t need to. The inputs or outputs of your tools are never stored.
Best Practices for Non-Technical Users
To get the most out of the ScrapingBee + Relevance AI integration without writing code:
- Configure scraping parameters: Set appropriate rendering options and timeout settings for reliable data extraction.
- Optimize request headers: Use custom headers and proxy settings to avoid blocking and improve success rates.
- Handle rate limiting: Space out requests appropriately and implement retry logic for failed attempts.
- Validate data structure: Ensure scraped data matches expected formats before processing with Relevance AI.
- Monitor usage: Keep track of API credits and implement error handling for failed requests.
