flarecrawl-extraction

name	flarecrawl-extraction
description	Flarecrawl AI-powered data extraction workflows. Triggers: extract data, structured extraction, AI scraping, schema extraction, Workers AI
version	1.0.0
category	domain
tool	flarecrawl
requires	{"bins":["flarecrawl"],"skills":["flarecrawl-ops"]}
allowed-tools	Read Bash Grep

Flarecrawl Extraction

AI-powered structured data extraction from web pages using Cloudflare Workers AI. Extract structured JSON from any page using natural language prompts and optional JSON schemas.

Key Commands

Basic extraction

flarecrawl extract "Get all product names and prices" --urls https://shop.example.com --json

With JSON schema

flarecrawl extract "Extract article metadata" --urls https://blog.example.com \
  --schema '{"type":"json_schema","schema":{"type":"object","properties":{"title":{"type":"string"},"date":{"type":"string"},"author":{"type":"string"}}}}'

Schema from file

flarecrawl extract "Extract data" --urls https://example.com --schema-file schema.json

Multiple URLs (sequential)

flarecrawl extract "Get page title" --urls https://a.com,https://b.com --json

Batch extraction (parallel)

flarecrawl extract "Get page title and description" --batch urls.txt --workers 5
# Output: NDJSON with {index, status, data/error}

Workflows

Schema-driven extraction

1. Define a JSON schema describing the expected output shape
2. Write a clear prompt describing what to extract
3. Run extraction with --schema or --schema-file

# schema.json
cat > schema.json << 'EOF'
{
  "type": "json_schema",
  "schema": {
    "type": "object",
    "properties": {
      "products": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "name": {"type": "string"},
            "price": {"type": "number"},
            "currency": {"type": "string"}
          }
        }
      }
    }
  }
}
EOF

flarecrawl extract "Extract all products with their prices" \
  --urls https://shop.example.com --schema-file schema.json --json

Batch extraction pipeline

# 1. Discover pages
flarecrawl map https://shop.example.com --json | jq -r '.data[]' | grep '/product/' > products.txt

# 2. Batch extract with schema
flarecrawl extract "Get product name, price, and description" \
  --batch products.txt --workers 5 --schema-file schema.json

# 3. Filter successful results
flarecrawl extract "Get product name and price" --batch products.txt \
  | jq 'select(.status == "ok") | .data'

Extract vs scrape --format json

Feature	extract	scrape --format json
Custom prompt	Yes	No (generic "extract main content")
JSON schema	Yes (--schema)	No
Batch mode	Yes (--batch)	Yes (--batch)
Use case	Structured data	Quick content extraction

Use extract when you need specific fields. Use scrape --format json for generic content.

MCP equivalent: the extract_data(urls, prompt, json_schema) T1 composite tool exposes this over the flarecrawl MCP server — see the flarecrawl-ops skill for MCP setup.

Domain-Specific Gotchas

• Workers AI models have context limits — very large pages may truncate
• Prompt quality matters — be specific about what fields you want
• Schema validation is best-effort — Workers AI may not perfectly match complex schemas
• No streaming — extraction waits for the full AI response
• Cost: $0 — Workers AI extraction is included in Browser Rendering pricing
• Batch output is NDJSON, not {data, meta} envelope — parse line by line
• Combine --urls and --batch — batch URLs supplement --urls comma list
• --body bypass — for advanced Workers AI options not exposed as flags:

  flarecrawl extract --body '{"url":"https://example.com","prompt":"Extract data","response_format":{"type":"json_schema","schema":{...}}}' --json