📚 US Federal Register Rules Scraper

Scrape US Federal Register rules and proposed rules: agency, title, document type, dates and docket. Run it once and you get the current records as a dataset — that is the default. Turn on watch mode and a scheduled run returns only what changed since the last run (see Optional: run it on a schedule).

Rules, proposed rules and notices as they publish in the Federal Register.

Output is one document_record row per result; billing is pay-per-event, the value event being one rule event delta (a $0.02 start fee per run, then $0.005 per rule event delta plus one $0.10 source check per run that reads the source). Source: www.federalregister.gov.

No login, no API key and no CAPTCHA solving are involved: the source is read logged-out with an identified contact User-Agent.

Who buys this

Buyers usually search for Federal Register or federal rules.

• Analysts, researchers and journalists who need Federal Register as clean rows.
• Risk, insurance and planning teams joining federal rules to their own data.
• Data teams that want the official source without building a scraper.

📊 Sample Output

Real rows from run jmB6vcgYiVxHieocl on build 0.1.29 (2026-09-28), the same input as the Quick start below — every value is as the source published it (emails masked, long text shortened):

document_number	title	doc_type	publication_date	agency_names
2026-19843	Notice of Policy Change and Rescission of Single-Pilot Training Exempt	Rule	2026-09-28	["Transportation Department", "Federal Aviation Administration"]
2026-19813	Fisheries Off West Coast States; West Coast Salmon Fisheries; Rebuildi	Rule	2026-09-28	["Commerce Department", "National Oceanic and Atmospheric Administrati
2026-19801	Fisheries of the Caribbean, Gulf of America, and South Atlantic; Snapp	Rule	2026-09-28	["Commerce Department", "National Oceanic and Atmospheric Administrati
2026-19762	Airworthiness Directives; Safran Helicopter Engines, S.A. Engines	Rule	2026-09-28	["Transportation Department", "Federal Aviation Administration"]
2026-19761	Airworthiness Directives; Hélicoptères Guimbal Helicopters	Rule	2026-09-28	["Transportation Department", "Federal Aviation Administration"]
2026-19760	Airworthiness Directives; Costruzioni Aeronautiche Tecnam S.P.A. Airpl	Rule	2026-09-28	["Transportation Department", "Federal Aviation Administration"]
2026-19758	Airworthiness Directives; Airbus Helicopters	Rule	2026-09-28	["Transportation Department", "Federal Aviation Administration"]
2026-19757	Airworthiness Directives; Airbus Helicopters Deutschland GmbH (AHD) He	Rule	2026-09-28	["Transportation Department", "Federal Aviation Administration"]

The run finished with the status message: NORMAL: emitted 10 item(s)

✅ What you get

One row per record delivered by US Federal Register Rule Event Watch; in watch mode a delta row carries delta_type, before, after and changed_fields. Field names come from the actor's real output; fields the source does not publish are null.

Each row is flat JSON with these fields (from the dataset schema and the sample run; a field the source does not publish for a given row is null):

• record_key (string/null) — e.g. fr:2026-19843
• document_number (string/number/integer/boolean/null) — e.g. 2026-19843
• title (string/number/integer/boolean/null) — e.g. Notice of Policy Change and Rescission of Single-Pilot Training Exemptions for C
• doc_type (string/number/integer/boolean/null) — e.g. Rule
• publication_date (string/number/integer/boolean/null) — e.g. 2026-09-28
• agency_names (array/string/number/integer/boolean/null) — e.g. ["Transportation Department", "Federal Aviation Administration"]
• source_url (string/number/integer/boolean/null) — e.g. https://www.federalregister.gov/documents/2026/09/28/2026-19843/notice-of-policy
• record_type (string) — e.g. document_record
• delta_type (string/null) — null in every sample row
• before (object/array/string/number/integer/boolean/null) — null in every sample row
• after (object/array/string/number/integer/boolean/null) — null in every sample row
• changed_fields (array/object/string/null) — null in every sample row
• observed_at (string/null) — null in every sample row

✅ What you get

• record_key (string/null) — e.g. fr:2026-19493
• document_number (string/number/integer/boolean/null) — e.g. 2026-19493
• title (string/number/integer/boolean/null) — e.g. Patient Protection and Affordable Care Act; Temporary Moratoria on Certain Agent
• doc_type (string/number/integer/boolean/null) — e.g. Rule
• publication_date (string/number/integer/boolean/null) — e.g. 2026-09-23
• agency_names (array/string/number/integer/boolean/null) — e.g. ["Health and Human Services Department"]
• source_url (string/number/integer/boolean/null) — e.g. https://www.federalregister.gov/documents/2026/09/23/2026-19493/patient-protecti
• record_type (string) — e.g. document_record
• delta_type (string/null) — null in every sample row
• before (object/array/string/number/integer/boolean/null) — null in every sample row
• after (object/array/string/number/integer/boolean/null) — null in every sample row
• changed_fields (array/object/string/null) — null in every sample row
• observed_at (string/null) — null in every sample row

✅ What you get

• record_key (string/null) — e.g. fr:2026-19084
• document_number — e.g. 2026-19084
• title — e.g. Airworthiness Directives; Airbus SAS Airplanes
• doc_type — e.g. Rule
• publication_date — e.g. 2026-09-17
• agency_names — e.g. ["Transportation Department", "Federal Aviation Administration"]
• source_url — e.g. https://www.federalregister.gov/documents/2026/09/17/2026-19084/airworthiness-di
• record_type (string) — e.g. document_record
• delta_type (string/null) — null in every sample row

Every run also writes a RUN_RECEIPT record to its key-value store with the source checks it made and the counts it charged — diagnostics never land in the paid dataset.

⚙️ Sample inputs

1. Quick start — the Store example (this is what the sample above came from)

{
  "doc_types": [
    "RULE"
  ],
  "agencies": [],
  "watch_mode": false,
  "baseline_id": "default",
  "max_items": 10
}

The sample run charged exactly: 1 × $0.02 apify-actor-start + 1 × $0.10 source-check + 0 × $0.005 rule-event-delta = $0.12 on the Free tier — snapshot preview rows are delivered unbilled; only the source check and the start fee were charged.

2. A smaller, narrowed run

{
  "doc_types": [
    "RULE"
  ],
  "agencies": [],
  "watch_mode": false,
  "baseline_id": "default",
  "max_items": 5
}

Caps the run at 5 rows; same billing shape as the sample run above.

3. Optional watch mode (changes only, for a schedule)

{
  "doc_types": [
    "RULE"
  ],
  "agencies": [],
  "watch_mode": true,
  "baseline_id": "default",
  "max_items": 10
}

The first run seeds a private baseline and emits zero deltas; every later run emits one row per change, billed at $0.005 each plus the $0.10 source check; billed deltas are capped at 500 per run.

🧾 JSON sample record

One real record from run jmB6vcgYiVxHieocl, exactly as it lands in the dataset (emails masked, long text shortened):

{
  "record_key": "fr:2026-19843",
  "document_number": "2026-19843",
  "title": "Notice of Policy Change and Rescission of Single-Pilot Training Exemptions for Certain Cessna Aircraft",
  "doc_type": "Rule",
  "publication_date": "2026-09-28",
  "agency_names": [
    "Transportation Department",
    "Federal Aviation Administration"
  ],
  "source_url": "https://www.federalregister.gov/documents/2026/09/28/2026-19843/notice-of-policy-change-and-rescission-of-single-pilot-training-exemptions-for-certain-cessna",
  "record_type": "document_record",
  "schema_version": "1.0",
  "output_mode": "preview",
  "preview_cap": 25,
  "preview_truncated": 0
}

🔧 How it works

Source. The actor reads www.federalregister.gov — endpoints: https://www.federalregister.gov/api/v1/documents.json. Public pages and feeds only; nothing behind a login.

Transport. Plain HTTPS from the Apify platform, no proxy. Every request carries an identified contact User-Agent.

Watch semantics. Snapshot mode (watch_mode: false) returns a preview capped at 25 records. Watch mode keys every record on a source-native id, stores a private baseline in a named key-value store, and on each later run emits one delta row per change (new / changed / removed). A blocked fetch is never diffed, so a source outage cannot show up as a mass removal.

Terminal states. A run ends NORMAL, CAPPED (your cap was reached), PARTIAL (something was withheld and the message says what), GENUINE_EMPTY (the source was read and truly had nothing in scope) or BLOCKED (the source refused or changed shape — the run FAILS loud and bills nothing). A zero-row run is never reported as a silent success.

Charging. Each rule event delta is charged at the moment it is pushed (rule-event-delta); a row that fails to charge is not delivered, so the dataset count always equals the charged count. One source-check is charged per run that actually read the source.

🔧 How it works

🔧 How it works

Source

Federal Register API v1 documents (public, logged-out)

• https://www.federalregister.gov/api/v1/documents.json

Public, logged-out only — no API key, login, cookie or CAPTCHA.

Snapshot mode is a PREVIEW

watch_mode: false returns a preview only: at most 25 records, hard-capped in code regardless of max_items. Preview records carry output_mode: "preview" with preview_cap and preview_truncated. It charges no value event. This actor sells change detection, not bulk record export — use watch mode for the feed.

Watch semantics

The first watch run stores a private baseline and emits zero deltas. Later runs emit NEW / CHANGED / REMOVED keyed on the source-native id, with before / after and changed_fields.

Terminal states

NORMAL · SUCCEEDED-0: GENUINE_EMPTY · PARTIAL · BLOCKED. A zero with no evidence FAILS loud rather than exiting 0 quietly.

What is not done. No login, no cookie or CAPTCHA bypass, no private or personal-account data, no browser automation.

⏰ Optional: run it on a schedule

A single run returns the current records. If you also want to be told what changed over time, open the actor in Apify Console → Schedules → Create new, add this actor with the watch-mode input above (watch_mode: true) so each run returns only the changes, and pick a cadence:

• Daily — 0 7 * * * (07:00 UTC): catch changes within a day.
• Weekly — 0 7 * * 1: a Monday digest.

The baseline persists in a named key-value store between runs, so every scheduled run compares against the last one; use a different baseline_id for independent watches. Schedules can also POST results to a webhook (Console → Integrations).

💰 Pricing example

Event	Free	Bronze	Silver	Gold
Actor Start (apify-actor-start)	$0.02	$0.02	$0.02	$0.02
Source check (source-check)	$0.10	$0.09	$0.08	$0.067
Rule event delta (rule-event-delta)	$0.005	$0.0045	$0.004	$0.0034

Worked at the live Free-tier price (watch mode, one delta per change):

• 8 rule event deltas: $0.02 start + $0.10 source check + 8 × $0.005 = $0.16
• 25 rule event deltas: $0.02 start + $0.10 source check + 25 × $0.005 = $0.245
• 200 rule event deltas: $0.02 start + $0.10 source check + 200 × $0.005 = $1.12

A run that delivers zero rows charges the $0.02 start fee plus the $0.10 source check (the source was read and reported empty). A BLOCKED run (source refused) fails loud and charges no value event. The start fee is charged once per GB of run memory; the default run memory is 1024 MB.

Yield on the sample run: NORMAL: emitted 10 item(s). max_items is a hard ceiling on what is delivered and billed, never a target. Snapshot output is capped at 25 records regardless of the cap you set; watch mode is the full feed.

⚖️ Legal & ToS

This actor reads public, logged-out pages and feeds published by www.federalregister.gov. It collects only what the source publishes to any visitor, identifies itself with a contact User-Agent, and does not access accounts, private data or anything behind authentication. Use the output in line with the source's terms and your local law; the intended use is B2B research and monitoring.

❓ FAQ

Q: Do I need an API key or a login?
A: No. The source (www.federalregister.gov) is read logged-out; the input schema has no key field and the actor carries no secrets.

Q: Why did my run return 0 rows?
A: Read the run's status message. GENUINE_EMPTY means the source was read and had nothing in scope for your input (in watch mode the first run always seeds the baseline and returns zero deltas); BLOCKED means the source refused and the run failed without billing a value event — retry later or narrow the input. A zero-row run bills the start fee plus one source check.

Q: How many rows can one run return?
A: Up to max_items (default 200); snapshot previews stop at 25. Raise the cap for a bigger run; you pay per delivered row.

Q: What does watch mode remember between runs?
A: A private baseline of source-native record ids in a named key-value store (baseline_id names it, so several independent watches can coexist). Each later run diffs the live source against it and emits only changes.

Q: How fresh is the data?
A: Every run reads the source live at run time; nothing is cached between runs except the watch baseline. Put it on a schedule for a continuous feed.

Q: What formats can I export?
A: The dataset downloads as JSON, CSV, Excel, XML or RSS from the run's Dataset tab or the Apify API, and any run can push to a webhook or integration.

Q: How is this different from the other public records lookups actors?
A: Same output shape and billing model; this one covers www.federalregister.gov. The siblings under Related Actors cover the other sources or slices — run several on one schedule for a combined feed.

🆘 Troubleshooting

• Run FAILED with BLOCKED → the source refused the request or changed its page shape → nothing was billed beyond the start fee; retry after a while, and if it persists open an Issue with the run id.
• Status says CAPPED → your cap (max_items) was reached → raise it for a bigger run.
• Input validation error on start → a field is outside the schema's allowed values → start from the Quick start block and change one field at a time.
• Run TIMED-OUT → a very wide request on a slow day → raise the run timeout in Run options or narrow the input; what was delivered before the timeout is still in the dataset.
• First watch run shows no deltas → expected: it seeds the baseline; changes appear from the second run.

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