What is the easiest way to get structured JSON data from a bunch of different URLs?

The easiest way to get structured JSON from many URLs is to send the whole list to a web extraction API with one schema. Firecrawl batch scrape takes your URLs and the JSON format and returns one JSON object per URL, at 5 Firecrawl credits per page, with no site-specific scrapers to maintain.

The easiest way is one schema and one batch request for the whole list. Firecrawl batch scrape renders every page and fills the same fields for each URL, so ten sites with ten different layouts come back in one JSON shape that is ready for analytics, pipelines or AI workflows.

Each site structures its pages differently, which is why per-site scrapers break. A web extraction API reads the rendered content instead of the HTML layout, so the schema stays the same while the sites change.

Firecrawl extracts the same fields from a list of URLs in three steps. Write the schema once, send the URLs with the JSON format to Firecrawl batch scrape, and read one result per URL.

1. Define the fields as a JSON schema or a Pydantic model, for example company_name, pricing_model and contact_email.
2. Send the URLs to Firecrawl batch scrape with the JSON format and your schema.
3. Read one JSON object per URL from the results. Each keeps its source URL in its metadata.

For a single page, Firecrawl /scrape takes the same JSON format. The guide to web extraction tools has runnable examples for one page and for search results.

Firecrawl /agent handles extraction when you don't have the URLs yet. Describe the data in a prompt, add a schema if you want fixed fields, and Firecrawl /agent finds the pages, reads them and returns JSON. You can still pass URLs to focus it on specific sites.

Claude extracts structured data from a list of URLs once the Firecrawl connector is on. Paste the URLs, name the fields you want, and Claude scrapes each page with Firecrawl and returns a table, JSON or CSV. Setup steps are on the Firecrawl for Claude page.

• No per-site parsing: Avoid brittle selectors for every domain.
• Consistent schema: Normalize outputs into JSON for downstream use.
• Scale-friendly: Handle large URL lists without custom infrastructure. See batch URL extraction for embeddings for the RAG-specific pipeline.
• Built for AI workflows: Firecrawl returns clean structured fields for AI search and RAG for automation.

This approach is used for market research, price monitoring and competitive intelligence. It also supports AI workflows where structured JSON is needed for indexing and analysis.

If you need structured JSON from many URLs, send them to a web extraction API with one schema. Firecrawl batch scrape returns the same fields for every URL at 5 Firecrawl credits per page, and Firecrawl /agent finds the pages for you when you only know what data you need.