# Federal Register Regulations Monitor (`agentictools/federal-register-monitor`) Actor Search and monitor US Federal Register documents: rules, proposed rules, and notices by term, agency, or type. For compliance and policy tracking. - **URL**: https://apify.com/agentictools/federal-register-monitor.md - **Developed by:** [Ken Agland](https://apify.com/agentictools) (community) - **Categories:** Business, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $1.00 / 1,000 document returneds 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 Regulations Monitor Search and monitor US Federal Register documents: rules, proposed rules, and notices by term, agency, or type. For compliance and policy tracking. ### What it does - Full-text search across the Federal Register using the public API (no API key needed). - Filter by document type: final rule, proposed rule, or notice. - Filter by a publication date floor to catch only recent activity. - Export clean, flat records to a dataset plus an aggregate summary in OUTPUT. ### Example input Recent AI-related rules and notices: ```json { "term": "artificial intelligence", "types": ["RULE", "NOTICE"], "sinceDate": "2026-01-01", "maxItems": 40 } ```` All proposed rules mentioning "tariffs": ```json { "term": "tariffs", "types": ["PRORULE"], "maxItems": 100 } ``` ### Input | Field | Type | Description | |---|---|---| | `term` | string | Full-text search term matched against title and text. Leave empty to list by type and date only. | | `types` | array | Document types to include: `RULE`, `PRORULE`, `NOTICE`. Defaults to all three. | | `sinceDate` | string | Only include documents published on or after this date (YYYY-MM-DD). | | `maxItems` | integer | How many documents to return (auto-paginated, max 1000). Default 40. | ### Output Each dataset item is one document: ```json { "documentNumber": "2025-00636", "title": "Framework for Artificial Intelligence Diffusion", "type": "Rule", "agencies": ["Commerce Department", "Industry and Security Bureau"], "publicationDate": "2025-01-15", "effectiveDate": "2025-01-13", "abstract": "With this interim final rule, the Commerce Department's Bureau of Industry and Security (BIS) revises the Export Administration Regulations' controls on advanced computing integrated circuits and adds a new control on artificial intelligence model weights for certain advanced closed-weight dual-use AI models.", "htmlUrl": "https://www.federalregister.gov/documents/2025/01/15/2025-00636/framework-for-artificial-intelligence-diffusion", "pdfUrl": "https://www.govinfo.gov/content/pkg/FR-2025-01-15/pdf/2025-00636.pdf" } ``` #### Run summary (OUTPUT) The run's default key-value store record `OUTPUT` holds an aggregate for the whole result set: ```json { "query": { "term": "artificial intelligence", "types": ["RULE", "NOTICE"], "sinceDate": "2026-01-01" }, "totalMatched": 304, "returned": 40, "requestedMaxItems": 40, "typeBreakdown": { "Rule": 12, "Notice": 28 }, "generatedFrom": "https://www.federalregister.gov/api/v1/documents.json" } ``` ### How it works The Actor calls the public Federal Register API with your term, type, and date filters, paginating at 100 documents per page until it has `maxItems` results or runs out of matches. Every request sends a descriptive User-Agent. Network errors and rate limit or server errors are retried with backoff; other client errors fail fast. ### Use cases - Compliance tracking: watch for new rules and notices in a regulated area. - Policy research: pull every document mentioning a topic across agencies. - Competitive intelligence: monitor rulemaking that affects an industry. - Feed a scheduled run into your own alerting or dashboard. MIT licensed. # Actor input Schema ## `term` (type: `string`): Full-text search term matched against document title and text (for example "artificial intelligence", "data privacy", "tariffs"). Leave empty to list documents by type and date only. ## `types` (type: `array`): Which Federal Register document types to include. Leave empty to include all three. ## `sinceDate` (type: `string`): Only include documents published on or after this date (YYYY-MM-DD). Leave empty for no date floor. ## `maxItems` (type: `integer`): How many documents to return. Results are paginated automatically until this count is reached or there are no more matches. ## Actor input object example ```json { "term": "data privacy", "types": [ "RULE", "PRORULE", "NOTICE" ], "sinceDate": "2026-01-01", "maxItems": 40 } ``` # 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("agentictools/federal-register-monitor").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("agentictools/federal-register-monitor").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 agentictools/federal-register-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=agentictools/federal-register-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Federal Register Regulations Monitor", "description": "Search and monitor US Federal Register documents: rules, proposed rules, and notices by term, agency, or type. For compliance and policy tracking.", "version": "0.1", "x-build-id": "miLjRBLn1HzkBQgmn" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/agentictools~federal-register-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-agentictools-federal-register-monitor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/agentictools~federal-register-monitor/runs": { "post": { "operationId": "runs-sync-agentictools-federal-register-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/agentictools~federal-register-monitor/run-sync": { "post": { "operationId": "run-sync-agentictools-federal-register-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 search term matched against document title and text (for example \"artificial intelligence\", \"data privacy\", \"tariffs\"). Leave empty to list documents by type and date only." }, "types": { "title": "Document types", "type": "array", "description": "Which Federal Register document types to include. Leave empty to include all three.", "items": { "type": "string", "enum": [ "RULE", "PRORULE", "NOTICE" ], "enumTitles": [ "Final rule", "Proposed rule", "Notice" ] }, "default": [ "RULE", "PRORULE", "NOTICE" ] }, "sinceDate": { "title": "Published since", "type": "string", "description": "Only include documents published on or after this date (YYYY-MM-DD). Leave empty for no date floor." }, "maxItems": { "title": "Maximum items", "minimum": 1, "maximum": 1000, "type": "integer", "description": "How many documents to return. Results are paginated automatically until this count is reached or there are no more matches.", "default": 40 } } }, "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 } } } } } } } } } } ```