# Federal Register Scraper (`foo121/federal-register-scraper`) Actor Export US Federal Register documents (rules, proposed rules, notices, presidential documents) by keyword, agency, type and date from the official Federal Register API. No API key required. - **URL**: https://apify.com/foo121/federal-register-scraper.md - **Developed by:** [ziv shay](https://apify.com/foo121) (community) - **Categories:** News, Business, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $4.00 / 1,000 result items This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Federal Register Scraper **The federal register scraper that turns the official Federal Register API into clean, structured US regulations data — rules, proposed rules, notices, and presidential documents — exported ready for your stack.** Built for regulatory-affairs, compliance, legal, and lobbying / government-affairs teams who need to track US regulations data without manually refreshing federalregister.gov every morning. Search by term, document type, agency, and publication window, and get back structured rows with comment deadlines, effective dates, and direct document links. Data comes from the **official Federal Register API** — **no API key required**. This federal register scraper gives you a clean export of exactly the fields you need, instead of copy-pasting from the website. ### What you get | Field | Description | |-------|-------------| | `documentNumber` | Official Federal Register document number | | `title` | Document title | | `type` | Document type (Rule, Proposed Rule, Notice, Presidential Document) | | `agencies` | Publishing agencies, `"; "`-joined into one string | | `publicationDate` | Date the document was published in the Federal Register | | `effectiveOn` | Effective date, where applicable | | `commentsCloseOn` | Comment period close date, where applicable | | `abstract` | Document abstract / summary text | | `docketIds` | Associated docket IDs (string) | | `htmlUrl` | Link to the HTML version on federalregister.gov | | `pdfUrl` | Link to the official PDF | ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `term` | string | `"artificial intelligence"` | Free-text search term | | `documentTypes` | array | `[]` | Filter by type: rules, proposed rules, notices, presidential docs (empty = all) | | `agencySlug` | string | _(optional)_ | Filter to a single agency by its slug | | `publishedFrom` | string (YYYY-MM-DD) | _(optional)_ | Earliest publication date | | `publishedTo` | string (YYYY-MM-DD) | _(optional)_ | Latest publication date | | `maxItems` | integer | `500` | Maximum number of documents to return | #### Example input ```json { "term": "artificial intelligence", "documentTypes": ["Rule", "Proposed Rule"], "agencySlug": "federal-trade-commission", "publishedFrom": "2025-01-01", "publishedTo": "2025-06-01", "maxItems": 500 } ```` ### Example run output ```json { "documentNumber": "2025-08431", "title": "Commercial Surveillance and Data Security Rulemaking on Artificial Intelligence", "type": "Proposed Rule", "agencies": "Federal Trade Commission; Department of Commerce", "publicationDate": "2025-05-12", "effectiveOn": null, "commentsCloseOn": "2025-07-11", "abstract": "The Commission proposes rules to address unfair or deceptive practices related to the use of automated decision-making and artificial intelligence systems.", "docketIds": "FTC-2025-0034", "htmlUrl": "https://www.federalregister.gov/documents/2025/05/12/2025-08431/commercial-surveillance-and-data-security-rulemaking", "pdfUrl": "https://www.govinfo.gov/content/pkg/FR-2025-05-12/pdf/2025-08431.pdf" } ``` ### Use cases - **Regulatory-affairs teams** — monitor new rules and proposed rules in your sector and never miss an effective date. - **Compliance & legal teams** — pull structured US regulations data with comment deadlines straight into your tracking systems. - **Lobbying / government-affairs teams** — watch agency activity by term and agency, and catch comment windows while they're open. - **Analysts & researchers** — build a Federal Register dataset for trend analysis across agencies and document types. ### Why this beats checking federalregister.gov by hand This federal register scraper hits the **official Federal Register API** with **no API key**, then hands you a clean, structured export — `commentsCloseOn`, `effectiveOn`, `docketIds`, and both HTML and PDF links in every row. Instead of scanning the site daily and copying fields, you get a schedulable, queryable feed of US regulations data ready for a spreadsheet, database, or alert pipeline. ### Pricing Pay per result — you are billed only for the Federal Register documents returned to your dataset. ### Keywords federal register scraper, federal register API data, US regulations data, federal register API, proposed rules scraper, regulatory tracking, comment deadline tracker, agency rulemaking data, compliance monitoring, government regulations scraper # Actor input Schema ## `term` (type: `string`): Full-text keyword to search across Federal Register documents (e.g. tariff, artificial intelligence, PFAS, student loan). Leave empty to list everything in the date range. ## `documentTypes` (type: `array`): Limit to document types: RULE (final rule), PRORULE (proposed rule), NOTICE, PRESDOCU (presidential). Leave empty for all. ## `agencySlug` (type: `string`): Optional agency slug filter (e.g. environmental-protection-agency, securities-and-exchange-commission). See federalregister.gov/agencies. ## `publishedFrom` (type: `string`): Optional earliest publication date, e.g. 2025-01-01. ## `publishedTo` (type: `string`): Optional latest publication date, e.g. 2026-06-21. ## `maxItems` (type: `integer`): Maximum number of documents to output. ## Actor input object example ```json { "term": "artificial intelligence", "documentTypes": [], "maxItems": 500 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "term": "artificial intelligence" }; // Run the Actor and wait for it to finish const run = await client.actor("foo121/federal-register-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "term": "artificial intelligence" } # Run the Actor and wait for it to finish run = client.actor("foo121/federal-register-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "term": "artificial intelligence" }' | apify call foo121/federal-register-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=foo121/federal-register-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Federal Register Scraper", "description": "Export US Federal Register documents (rules, proposed rules, notices, presidential documents) by keyword, agency, type and date from the official Federal Register API. No API key required.", "version": "1.0", "x-build-id": "JJlk5OTapAbxxtEFd" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/foo121~federal-register-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-foo121-federal-register-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/foo121~federal-register-scraper/runs": { "post": { "operationId": "runs-sync-foo121-federal-register-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/foo121~federal-register-scraper/run-sync": { "post": { "operationId": "run-sync-foo121-federal-register-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "term": { "title": "Search term", "type": "string", "description": "Full-text keyword to search across Federal Register documents (e.g. tariff, artificial intelligence, PFAS, student loan). Leave empty to list everything in the date range." }, "documentTypes": { "title": "Document types", "type": "array", "description": "Limit to document types: RULE (final rule), PRORULE (proposed rule), NOTICE, PRESDOCU (presidential). Leave empty for all.", "items": { "type": "string", "enum": [ "RULE", "PRORULE", "NOTICE", "PRESDOCU" ], "enumTitles": [ "Final rule", "Proposed rule", "Notice", "Presidential document" ] }, "default": [] }, "agencySlug": { "title": "Agency slug", "type": "string", "description": "Optional agency slug filter (e.g. environmental-protection-agency, securities-and-exchange-commission). See federalregister.gov/agencies." }, "publishedFrom": { "title": "Published from (YYYY-MM-DD)", "type": "string", "description": "Optional earliest publication date, e.g. 2025-01-01." }, "publishedTo": { "title": "Published to (YYYY-MM-DD)", "type": "string", "description": "Optional latest publication date, e.g. 2026-06-21." }, "maxItems": { "title": "Max items", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Maximum number of documents to output.", "default": 500 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```