What is schema-based extraction and why use it?
TL;DR
Schema-based extraction defines field names, types, and structure before extraction begins. Instead of parsing HTML and hoping for consistency, you declare a schema and receive data matching that exact structure. Firecrawl Agent accepts JSON schemas and returns typed, validated output ready for databases.
What is schema-based extraction?
Schema-based extraction inverts traditional scraping. Rather than writing selectors to find data, you define the desired output:
result = app.scrape_url("https://example.com/product", {
"formats": ["extract"],
"extract": {
"schema": {
"type": "object",
"properties": {
"name": {"type": "string"},
"price": {"type": "number"},
"inStock": {"type": "boolean"}
}
}
}
})The AI finds and maps content to your schema regardless of HTML structure.
Why use it: Type safety (numbers as numbers, not strings), consistent structure across pages, built-in validation, and resilience to site changes. CSS selectors specify location; schemas specify structure. When sites redesign, schema-based extraction keeps working.
Key Takeaways
Schema-based extraction guarantees typed, consistent output. Define your structure; Firecrawl's AI populates it semantically—no brittle selectors, no manual type conversion.
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