How to Scrape Federal Register Rules and Notices

the official Federal Register documents API already exposes structured records, so browser scraping would add fragility without adding data. The Federal Register Scraper handles pagination, retries, normalization, deduplication, and Apify dataset output.

Pick a bounded query

The Actor accepts search terms, document types, agency slugs, publication dates, and newest or oldest ordering. Start with one narrow query and a small result cap. Inspect the returned rows before increasing volume. A broad default is convenient for a demo, but it is a poor production specification.

{
  "queries": [
    "artificial intelligence"
  ],
  "documentTypes": [
    "RULE",
    "PRORULE"
  ],
  "maxResultsPerQuery": 100
}

Each row contains document type, title, abstract, agencies, publication and effective dates, comment deadline, citation, docket IDs, regulation identifiers, PDF, and HTML links. The natural key is document_number. Use it when merging reruns; dataset row order is not an identity.

Export and verify

Run the Actor from the Apify Console, API, or a saved Task. JSON preserves arrays and nulls best. CSV is convenient for a quick spreadsheet review, but inspect how arrays were serialized before importing it into a warehouse.

Open several source links and compare identifiers, dates, and one multi-value field with the published record. A search result is not a legal conclusion. Proposed rules, final rules, and notices have different effects. Preserve document type and official links so counsel or policy owners can review the actual publication.

A good first run answers two questions: does the query find the intended population, and can every important row be traced back to the publisher? Scale only after both answers are yes.