Federal Register Regulatory Change Monitor

Monitor Federal Register topics through the official API, detect new or materially updated documents, rank materiality and keep state between runs for compliance, legal and policy watchlists. Returns structured, machine-readable results for repeatable research, monitoring, automation.

Quick start

Start with this working example and replace the target values with your own:

{
  "terms": [
    "artificial intelligence"
  ],
  "lookbackDays": 30,
  "persistState": true
}

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

Input

• terms — Watch terms: Topics, products, rules, technologies, or regulated activities to monitor.
• agencyFilters — Agency filters: Optional agency-name fragments. Leave empty for all agencies.
• lookbackDays — Lookback days: How far back each monitoring check searches.
• maxDocumentsPerWatch — Max documents per watch: Maximum documents inspected per term.
• persistState — Persist monitor state: Compare later runs with the previous state to detect new or updated documents.
• includeUnchanged — Include unchanged documents: Also include a compact current-document sample in each report.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

Output

The Dataset exposes predictable machine-readable output. Representative fields include dataset.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.020000 per result event. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

Use cases

• Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
• Use the Actor from API or schedules for recurring collection, comparison or monitoring.
• Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
• Compare repeat runs when the product supports snapshots or previous-run inputs.

Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

Limitations

• Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
• Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
• Analytical outputs depend on the quality and coverage of the supplied or collected source data.
• Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

Detailed documentation

Detailed documentation

Monitor Federal Register topics as a recurring regulatory intelligence workflow. The Actor queries the official FederalRegister.gov API, keeps bounded state between runs when enabled, detects newly observed or materially updated documents, and returns one structured monitoring report per watch term. It is designed for compliance, legal operations, government affairs, procurement, policy research, and strategy teams that need to know what changed rather than repeatedly export raw search results.

Why use this Actor

A conventional Federal Register scraper answers “what documents exist?” This Actor is built around a different operational question: “what changed since the last check, and which changes deserve attention first?” Persistent state converts repeated runs into a monitoring workflow. Each report includes counts, change status, newly detected documents, updated documents, source links, agencies, publication dates, abstracts, and a deterministic materiality score. The materiality heuristic uses document type and explicit language such as effective dates, deadlines, compliance requirements, penalties, final rules, and requests for comment. It is a triage tool, not legal advice.

Key features

• Persistent run-to-run state for new and updated document detection.
• Official FederalRegister.gov API source with no browser automation.
• One decision-ready report per monitored term rather than one paid row per source document.
• Optional agency-name filtering after retrieval.
• Materiality ranking for current and changed documents.
• Bounded lookback, document limits, and state size for predictable execution.
• Structured errors instead of silent partial failures.
• No external LLM or paid third-party data dependency.

Input

Provide terms as the watchlist. Optional agencyFilters narrow results by agency-name fragments. lookbackDays controls the historical search window, maxDocumentsPerWatch bounds inspected documents, persistState enables change detection across runs, and includeUnchanged optionally returns a compact sample of current unchanged documents. A useful production pattern is one Task per regulatory territory with scheduled execution.

Output

Each dataset item is a monitoring report for one term. Core fields include status, matchedDocuments, sourceReportedCount, changeCount, newCount, updatedCount, changes, and highestMateriality. INITIALIZED means the watch has no previous state, CHANGED means new or updated documents were detected, and NO_CHANGE means the current fingerprints match the prior run for the retrieved set.

Example

Input: {"terms":["artificial intelligence"],"lookbackDays":30,"persistState":true}. The first run establishes state and returns INITIALIZED. Later runs compare document fingerprints and surface new or updated Federal Register records with agency, publication date, links, abstract, and materiality score.

Use cases

Use it for AI regulation, cybersecurity rules, privacy policy, banking regulation, healthcare regulation, environmental policy, labor rules, federal procurement, energy regulation, digital assets, or any other recurring Federal Register topic. Teams can schedule Tasks, feed structured reports into databases, send alerts from downstream automation, or use the dataset as a machine-readable regulatory watch surface.

Pricing

Pay per event. One primary event is charged for each decision-ready watch report written to the default dataset. There is no separate start fee in the QuanMatrix pricing design. The product is intentionally report-priced rather than raw-document-priced so repeated monitoring remains understandable and aligned with the output users actually consume.

Limitations

The materiality score is a deterministic triage heuristic and does not replace legal review. Federal Register API availability, search behavior, and metadata determine what can be detected. Agency filters are applied to the retrieved result set, so very broad terms combined with narrow agencies can require a larger document limit. A document disappearing outside the configured lookback window is not treated as a regulatory withdrawal. Persistent state is bounded and intended for recurring operational watchlists, not permanent archival storage.

Gen2 decision intelligence

This Actor preserves its original analysis and adds a decision layer with baseline awareness, regression detection, confidence, GO/WARN/BLOCK executive output, and an optional economic-impact estimate. Economic estimates are produced only when the user supplies valuePerImpactUnitUsd; the result states the calculation basis instead of inventing monetary value.