# US Public Library Leads Scraper (IMLS Directory) (`scrapesage/us-library-leads-scraper`) Actor Scrape every US public library from the official IMLS Public Libraries Survey: ~9,000 library systems + ~17,000 branch outlets with phone, full address, population served, budget, collections, programs, computers/Wi-Fi & a lead score. Filter by state, size & type. B2B leads + new-library monitoring. - **URL**: https://apify.com/scrapesage/us-library-leads-scraper.md - **Developed by:** [Scrape Sage](https://apify.com/scrapesage) (community) - **Categories:** Lead generation, Automation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $6.00 / 1,000 library system leads 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## US Public Library Leads Scraper (IMLS Directory) Extract **every public library in the United States** — city, county, district, multi-jurisdictional, tribal and non-profit library systems plus all of their physical branches — straight from the **official IMLS Public Libraries Survey (PLS)**. Each library comes with its **phone, full address, county and geo**, plus **population served, operating budget, staffing, collection size, visits, circulation, programs, public computers and Wi‑Fi**, and a derived **lead score** on every record. No login, no cookies, no browser, **no API key** — fast extraction of the official IMLS data files. ### Why this library scraper? Other "library" tools on the market scrape library *catalogs* (books) or a single library's website. This actor ships the **richest US public-library lead & intelligence dataset in the category** — built for sales and market research, not just a name and address: | Data | Typical scrapers | This actor | |---|---|---| | Library / branch **name** | ✅ | ✅ | | **Phone** | partial | ✅ (~99%) | | Full address + county + geo | partial | ✅ | | Population served + size tier | ❌ | ✅ | | Operating **budget** (revenue & expenditures) | ❌ | ✅ | | Staffing (FTE, MLS librarians) | ❌ | ✅ | | Collection (books, e‑books, audio, video) | ❌ | ✅ | | Visits, circulation, registered users | ❌ | ✅ | | Programs & attendance | ❌ | ✅ | | Public computers & Wi‑Fi sessions | ❌ | ✅ | | Governance / legal basis | ❌ | ✅ | | Branch-level outlets (address, sq ft, hours, geo) | ❌ | ✅ | | Lead score (0–100) + lead signals | ❌ | ✅ | | New-library monitoring | ❌ | ✅ | There are **~9,000 public library systems and ~17,000 individual outlets** in the US — every one a buyer of books and e‑content, library software, furniture, RFID/self‑checkout, computers and Wi‑Fi, makerspace equipment, facilities services and professional development. ### Use cases - **B2B lead generation** — public libraries are recurring, grant-funded buyers: book & e‑book publishers and platforms (OverDrive/Libby, Hoopla, cloudLibrary), integrated library systems (ILS/automation), RFID & self‑checkout, public-access computers & broadband, furniture & shelving, makerspace/STEM kits, security, HVAC/facilities, and library design firms. Score libraries by `populationServed`, `totalRevenue` and `visits` and reach the `phone` directly. - **Territory & account planning** — pull every library system in a state, rank by budget and population, and build a clean, prioritized call list for reps. - **Market & sizing analysis** — analyze budgets, collection mix (print vs e‑book), staffing, program volume and technology by state, county or locale (city/suburb/town/rural). - **Grant & program targeting** — find large, high-traffic systems (or under-resourced rural ones) for partnerships, sponsorships and grant programs. - **Site & location intelligence** — switch to **Outlets** to get every physical branch with address, latitude/longitude, square footage and open hours. - **Monitoring** — schedule recurring runs with `monitorMode` to capture **only new libraries** as the survey refreshes. ### How to use 1. [Sign up for Apify](https://console.apify.com/sign-up) — the free plan is enough to try this actor. 2. Open the **US Public Library Leads Scraper**, choose **states**, a **record type** (library systems / outlets / both) and filters (size, budget, governance, locale), then click **Start**. 3. Watch results stream into the dataset table. 4. **Export** as JSON, CSV, Excel, XML, or HTML — or pull results programmatically via the [Apify API](https://docs.apify.com/api/v2). ### Input ```json { "recordType": "libraries", "states": ["TX"], "minPopulationServed": 10000, "withPhoneOnly": true, "sortBy": "leadScore", "maxResults": 500 } ```` - **recordType** *(default `libraries`)* — `libraries` (rich system-level leads), `outlets` (branch-level location records), or `both`. - **states** — two-letter USPS codes (`TX`, `CA`, `NY`). Leave empty to sweep all states & territories (bounded by `maxResults`). - **counties / cities / zipCodes / nameQuery** — filter by county, city, ZIP prefix, or library name text. - **localeCategories / beaRegions / legalBasis** — filter by urbanicity (City/Suburb/Town/Rural), BEA region, or governance (municipal, county, district, tribal, non‑profit…). - **minPopulationServed / maxPopulationServed / minTotalRevenue / minVisits / minTotalStaff / minBranches** — target by size, budget and activity. - **outletTypes / minSquareFeet** — filter outlets by type (central/branch/bookmobile) and floor area. - **multiOutletOnly / withPhoneOnly / excludeTemporarilyClosed / minLeadScore** — high-value segment toggles. - **sortBy** — `leadScore` (recommended), `populationHigh`, `revenueHigh`, `visitsHigh`, `squareFeetHigh`, `name`, `state`, or `source`. - **maxResults / maxResultsPerState / deduplicate / includeRawFields** — output controls. - **monitorMode / monitorKey** — only emit new records across scheduled runs. ### Output One record per library system (`recordType: "library"`): ```json { "recordType": "library", "fscsKey": "TX0001", "libraryName": "Austin Public Library", "address": "710 W Cesar Chavez St", "city": "Austin", "state": "TX", "zip": "78701", "county": "Travis", "fullAddress": "710 W Cesar Chavez St, Austin, TX, 78701", "latitude": 30.2705, "longitude": -97.7501, "locale": "City — Large", "localeCategory": "City", "phone": "(512) 974-7400", "hasPhone": true, "legalBasis": "Municipal government", "interlibraryRelation": "Member of a federation or cooperative", "beaRegion": "Southwest", "populationServed": 961855, "populationCategory": "Very large (100k+)", "centralLibraries": 1, "branchLibraries": 20, "totalOutlets": 21, "isMultiOutlet": true, "totalStaff": 470.5, "librariansMLS": 120, "totalRevenue": 62000000, "localRevenue": 60000000, "totalOperatingExpenditures": 58000000, "collectionExpenditures": 6000000, "bookVolumes": 1300000, "ebooks": 450000, "totalCollections": 2100000, "visits": 2500000, "registeredUsers": 600000, "totalCirculation": 6000000, "totalPrograms": 5000, "totalProgramAttendance": 200000, "publicComputers": 400, "wifiSessions": 900000, "fiscalYear": "FY2023", "leadScore": 100, "leadSignals": ["Very large service area (500k+)", "Very high budget ($10M+)", "Very high foot traffic", "Large multi-branch system", "High program volume", "Heavy Wi-Fi usage", "Strong e-book collection", "Has phone"], "dataSource": "IMLS Public Libraries Survey", "scrapedAt": "2026-06-21T12:00:00.000Z" } ``` Outlet records (`recordType: "outlet"`) carry the branch name & type, full address, phone, latitude/longitude, square footage, annual open hours and weeks open. Every record also carries `sourceFields` — the complete raw IMLS survey row — so no data is lost (turn off with `includeRawFields: false`). ### Automate & schedule Run this actor on autopilot and pull results into your own stack: - **[Apify API](https://docs.apify.com/api/v2)** — start runs, fetch datasets, and manage schedules over REST. - **[apify-client for JavaScript](https://docs.apify.com/api/client/js/)** and **[apify-client for Python](https://docs.apify.com/api/client/python/)** — official SDKs. - **[Schedules](https://docs.apify.com/platform/schedules)** — run it on a schedule with `monitorMode` to capture newly-added libraries as fresh leads. - **[Webhooks](https://docs.apify.com/platform/integrations/webhooks)** — trigger downstream actions (CRM import, Slack alert, email sequence) the moment a run finishes. ```js import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'MY_APIFY_TOKEN' }); const run = await client.actor('scrapesage/us-library-leads-scraper').call({ states: ['CA'], recordType: 'libraries', minPopulationServed: 10000, withPhoneOnly: true, sortBy: 'leadScore', maxResults: 500, }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(`Got ${items.length} library leads`); ``` ### Integrate with any app Connect the dataset to 5,000+ apps — no code required: - **[Make](https://docs.apify.com/platform/integrations/make)** — multi-step automation scenarios. - **[Zapier](https://docs.apify.com/platform/integrations/zapier)** — push new library leads straight into your CRM. - **[Slack](https://docs.apify.com/platform/integrations/slack)** — get notified when a monitored search finds new libraries. - **[Google Drive / Sheets](https://docs.apify.com/platform/integrations/drive)** — auto-export every run to a spreadsheet. - **[Airbyte](https://docs.apify.com/platform/integrations/airbyte)** — pipe results into your data warehouse. - **[GitHub](https://docs.apify.com/platform/integrations/github)** — trigger runs from commits or releases. ### Use with AI assistants (MCP) The output is clean, LLM-ready JSON. You can call this actor from Claude, ChatGPT, or any agent framework through the **[Apify MCP server](https://docs.apify.com/platform/integrations/mcp)** — ask your assistant to "find every large public library system in California with a phone number and a budget over $1M" and let it run this scraper for you. ### Agent-ready: autonomous payments (x402 & Skyfire) This actor is **agent-ready** — AI agents can discover it, run it, and **pay for it autonomously**, with no Apify account and no human in the loop. It uses [pay-per-event](https://docs.apify.com/platform/actors/publishing/monetize/pay-per-event) pricing and [limited permissions](https://docs.apify.com/platform/actors/development/permissions), so it qualifies for Apify's agentic-payment standards: - **[x402](https://docs.apify.com/platform/integrations/x402)** — an open, HTTP-native payment protocol. Agents pay per run in USDC on the Base network directly through the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp) — no account, no API key. - **[Skyfire](https://docs.apify.com/platform/integrations/skyfire)** — agent-to-service payments for fully autonomous AI-agent workflows. Building an AI agent, MCP tool, or autonomous data pipeline? This scraper is ready to plug in and pay as it goes. ### More scrapers from scrapesage Build a complete **US institutional & B2B lead-gen stack** from official open-data sources: - **[US School Leads Scraper](https://apify.com/scrapesage/us-schools-scraper)** — public & private K‑12 schools, districts and colleges with enrollment and contacts. - **[US Healthcare Facility Leads Scraper](https://apify.com/scrapesage/us-healthcare-facility-leads-scraper)** — hospitals, clinics and care facilities with contacts. - **[US Law Enforcement Leads Scraper](https://apify.com/scrapesage/us-law-enforcement-leads)** — police, sheriff and public-safety agencies. - **[US Fire Department Leads Scraper](https://apify.com/scrapesage/us-fire-department-leads-scraper)** — fire & EMS departments. - **[Nonprofit 990 Scraper](https://apify.com/scrapesage/nonprofit-990-scraper)** — US nonprofits with financials and contacts (incl. Friends-of-the-Library groups). - **[US Water System & Utility Leads (EPA SDWIS)](https://apify.com/scrapesage/us-water-system-scraper)** — public water utilities with admin contacts. - **[US Property Records Scraper](https://apify.com/scrapesage/us-property-records-scraper)** — property & parcel records with owner mailing addresses. - **[Website Contact Scraper](https://apify.com/scrapesage/website-contact-scraper)** — enrich any library website for emails, phones and socials. ### Tips - **Best leads first**: keep `recordType` on `libraries` and `sortBy` on `leadScore` — large, well-funded, high-traffic systems with a phone rank highest. - **Account sizing**: pull the full system record (budget, staffing, collection, programs, technology) to qualify and segment accounts before you call. - **Site lists**: switch to `outlets` to get every physical branch address with geo, square footage and hours — ideal for field sales, mailers or mapping. - **National sweeps**: leave `states` empty, set a `maxResults` cap, and use `maxResultsPerState` for an even spread across states. - **Proxies**: not needed — the official IMLS data file is downloaded directly. Leave the proxy off for the fastest run. ### FAQ **Where does the data come from?** The official US **IMLS Public Libraries Survey (PLS)** public-use data files (Administrative Entity + Outlet), published annually by the Institute of Museum and Library Services. No API key or login is required. **How fresh is the data?** The actor auto-resolves the latest fiscal year published on the IMLS website (currently FY2023). IMLS releases a new survey each year. **Do records include phone numbers?** Yes — ~99% of library systems and outlets carry a phone number. Fields are `null` only when IMLS doesn't publish them, never because the scraper skipped them. **Do they include emails?** The IMLS survey publishes phone and full mailing address, not email. Pair this with the [Website Contact Scraper](https://apify.com/scrapesage/website-contact-scraper) to enrich a library's website for emails and socials. **Can I get only large libraries?** Yes — use `minPopulationServed`, `minTotalRevenue`, `minVisits` or `minBranches`, and sort by `leadScore` or `revenueHigh`. **Can I export to Google Sheets, CSV, or Excel?** Yes — one click in the dataset view, or automatically on every run via the [Google Drive integration](https://docs.apify.com/platform/integrations/drive). **How do I monitor new libraries automatically?** Turn on `monitorMode`, give each watch a `monitorKey`, and create a [Schedule](https://docs.apify.com/platform/schedules). Each run emits only records that are new since the last run. **Is this legal?** This actor collects publicly available US government open data only. You are responsible for using the data in compliance with applicable laws and IMLS's terms. ### Need help? Open an issue on the actor's **Issues** tab, or visit the [Apify help center](https://help.apify.com/). Feature requests are welcome — this actor is actively maintained. # Actor input Schema ## `recordType` (type: `string`): Library systems = one rich record per public library (Administrative Entity) with full operational, financial and contact data (recommended). Outlets = one record per physical branch/central/bookmobile with address, phone, square footage & hours. Both = systems and outlets together. ## `states` (type: `array`): Filter by the library's state — two-letter USPS codes, e.g. TX, CA, NY. Leave empty for all 50 states + territories. ## `counties` (type: `array`): Filter by county name (case-insensitive, exact), e.g. Travis, Cook, Los Angeles. ## `cities` (type: `array`): Filter by city (case-insensitive, exact city name), e.g. Austin, Chicago. ## `zipCodes` (type: `array`): Filter by ZIP — full ZIP (78701) or a prefix (787 matches all 787xx). ## `localeCategories` (type: `array`): Filter by NCES locale category of the library's location. ## `beaRegions` (type: `array`): Filter by Bureau of Economic Analysis region code (library systems only). ## `legalBasis` (type: `array`): Filter library systems by their legal/governance basis. Useful for targeting government-run vs district vs non-profit libraries. ## `outletTypes` (type: `array`): Filter physical outlets by type. Only applies to outlet records. ## `minPopulationServed` (type: `integer`): Only return library systems whose legal-service-area population is at least this large (e.g. 10000). Targets bigger, higher-budget accounts. ## `maxPopulationServed` (type: `integer`): Only return library systems serving at most this many people. ## `minTotalRevenue` (type: `integer`): Only return library systems with at least this much total annual revenue (operating budget signal). ## `minVisits` (type: `integer`): Only return library systems with at least this many annual in-person visits (foot-traffic signal). ## `minTotalStaff` (type: `integer`): Only return library systems with at least this many total FTE staff. ## `minBranches` (type: `integer`): Only return library systems with at least this many branch libraries. ## `minSquareFeet` (type: `integer`): Only return outlets with at least this much floor area. Only applies to outlet records. ## `multiOutletOnly` (type: `boolean`): Only return library systems that operate more than one outlet (central + branches). A signal of larger, multi-location accounts. ## `withPhoneOnly` (type: `boolean`): Only return records that have a phone number on file (~99% of libraries do). ## `excludeTemporarilyClosed` (type: `boolean`): Skip libraries/outlets flagged as temporarily closed in the survey year. On by default for a clean, contactable list. ## `minLeadScore` (type: `integer`): Only return records with a derived lead score (0–100) at or above this value. ## `sortBy` (type: `string`): Lead score = highest-value accounts first (recommended). Population / Revenue / Visits = biggest libraries first. Square footage = biggest outlets first. Name / State = alphabetical. Source = file order (fastest). ## `maxResults` (type: `integer`): Maximum number of records to return in this run. The full survey holds ~9,000 library systems and ~17,000 outlets. ## `maxResultsPerState` (type: `integer`): Optional — cap the number of records per state for an even national spread. Leave empty for no per-state cap. ## `deduplicate` (type: `boolean`): Remove duplicate records by unique IMLS identifier (FSCS key, plus outlet sequence for branches). ## `includeRawFields` (type: `boolean`): Attach the complete raw IMLS survey row as sourceFields on every record so no data is lost. Turn off for a smaller, tidier output. ## `monitorMode` (type: `boolean`): Remember which libraries/outlets were already returned (in a named key-value store) and emit ONLY records that are new since the last run — each tagged monitorEvent: "new". IMLS refreshes the survey annually; run this on a Schedule to capture newly-added libraries as fresh leads. Works alongside Apify Schedules (the schedule starts the run; monitoring decides what's new). ## `monitorKey` (type: `string`): Names the memory used by monitoring mode. Use a distinct key per saved watch (e.g. per state) so different monitors don't share state. ## `dataFileUrl` (type: `string`): Advanced: override the IMLS PLS public-use CSV ZIP URL. Leave empty to auto-resolve the latest fiscal year from the IMLS website. ## `proxyConfiguration` (type: `object`): The actor downloads the official IMLS data file directly and needs no proxy in almost all cases — leave this off for the fastest, free download. If a direct download ever fails, the actor automatically retries through Apify Residential (US) proxy as a fallback. ## Actor input object example ```json { "recordType": "libraries", "states": [ "TX" ], "multiOutletOnly": false, "withPhoneOnly": false, "excludeTemporarilyClosed": true, "sortBy": "leadScore", "maxResults": 1000, "deduplicate": true, "includeRawFields": true, "monitorMode": false, "monitorKey": "default", "proxyConfiguration": { "useApifyProxy": false } } ``` # Actor output Schema ## `results` (type: `string`): All scraped library systems and outlets in the default dataset, each with name, address, phone, rich operational/financial data and a lead score. # 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 = { "states": [ "TX" ], "proxyConfiguration": { "useApifyProxy": false } }; // Run the Actor and wait for it to finish const run = await client.actor("scrapesage/us-library-leads-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 = { "states": ["TX"], "proxyConfiguration": { "useApifyProxy": False }, } # Run the Actor and wait for it to finish run = client.actor("scrapesage/us-library-leads-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 '{ "states": [ "TX" ], "proxyConfiguration": { "useApifyProxy": false } }' | apify call scrapesage/us-library-leads-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapesage/us-library-leads-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "US Public Library Leads Scraper (IMLS Directory)", "description": "Scrape every US public library from the official IMLS Public Libraries Survey: ~9,000 library systems + ~17,000 branch outlets with phone, full address, population served, budget, collections, programs, computers/Wi-Fi & a lead score. Filter by state, size & type. B2B leads + new-library monitoring.", "version": "0.1", "x-build-id": "K5r5aqzsAbmCLL4bm" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapesage~us-library-leads-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapesage-us-library-leads-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapesage~us-library-leads-scraper/runs": { "post": { "operationId": "runs-sync-scrapesage-us-library-leads-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapesage~us-library-leads-scraper/run-sync": { "post": { "operationId": "run-sync-scrapesage-us-library-leads-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "recordType": { "title": "Record type", "enum": [ "libraries", "outlets", "both" ], "type": "string", "description": "Library systems = one rich record per public library (Administrative Entity) with full operational, financial and contact data (recommended). Outlets = one record per physical branch/central/bookmobile with address, phone, square footage & hours. Both = systems and outlets together.", "default": "libraries" }, "states": { "title": "States", "type": "array", "description": "Filter by the library's state — two-letter USPS codes, e.g. TX, CA, NY. Leave empty for all 50 states + territories.", "items": { "type": "string" } }, "counties": { "title": "Counties", "type": "array", "description": "Filter by county name (case-insensitive, exact), e.g. Travis, Cook, Los Angeles.", "items": { "type": "string" } }, "cities": { "title": "Cities", "type": "array", "description": "Filter by city (case-insensitive, exact city name), e.g. Austin, Chicago.", "items": { "type": "string" } }, "zipCodes": { "title": "ZIP codes (prefix)", "type": "array", "description": "Filter by ZIP — full ZIP (78701) or a prefix (787 matches all 787xx).", "items": { "type": "string" } }, "localeCategories": { "title": "Locale (urbanicity)", "type": "array", "description": "Filter by NCES locale category of the library's location.", "items": { "type": "string", "enum": [ "City", "Suburb", "Town", "Rural" ], "enumTitles": [ "City", "Suburb", "Town", "Rural" ] } }, "beaRegions": { "title": "BEA regions", "type": "array", "description": "Filter by Bureau of Economic Analysis region code (library systems only).", "items": { "type": "string", "enum": [ "01", "02", "03", "04", "05", "06", "07", "08", "09" ], "enumTitles": [ "01 — New England", "02 — Mid East", "03 — Great Lakes", "04 — Plains", "05 — Southeast", "06 — Southwest", "07 — Rocky Mountains", "08 — Far West", "09 — Outlying Areas" ] } }, "legalBasis": { "title": "Governance / legal basis", "type": "array", "description": "Filter library systems by their legal/governance basis. Useful for targeting government-run vs district vs non-profit libraries.", "items": { "type": "string", "enum": [ "CI", "CO", "CC", "LD", "MJ", "SD", "NL", "NP", "OT" ], "enumTitles": [ "CI — Municipal government", "CO — County/parish", "CC — City/county", "LD — Library district", "MJ — Multi-jurisdictional", "SD — School district", "NL — Native American tribal", "NP — Non-profit association", "OT — Other" ] } }, "outletTypes": { "title": "Outlet types (for Outlets / Both)", "type": "array", "description": "Filter physical outlets by type. Only applies to outlet records.", "items": { "type": "string", "enum": [ "CE", "BR", "BS", "BM" ], "enumTitles": [ "CE — Central library", "BR — Branch library", "BS — Bookmobile", "BM — Books-by-mail only" ] } }, "minPopulationServed": { "title": "Min population served", "minimum": 0, "type": "integer", "description": "Only return library systems whose legal-service-area population is at least this large (e.g. 10000). Targets bigger, higher-budget accounts." }, "maxPopulationServed": { "title": "Max population served", "minimum": 0, "type": "integer", "description": "Only return library systems serving at most this many people." }, "minTotalRevenue": { "title": "Min total revenue ($)", "minimum": 0, "type": "integer", "description": "Only return library systems with at least this much total annual revenue (operating budget signal)." }, "minVisits": { "title": "Min annual visits", "minimum": 0, "type": "integer", "description": "Only return library systems with at least this many annual in-person visits (foot-traffic signal)." }, "minTotalStaff": { "title": "Min total staff (FTE)", "minimum": 0, "type": "integer", "description": "Only return library systems with at least this many total FTE staff." }, "minBranches": { "title": "Min branch libraries", "minimum": 0, "type": "integer", "description": "Only return library systems with at least this many branch libraries." }, "minSquareFeet": { "title": "Min square footage (outlets)", "minimum": 0, "type": "integer", "description": "Only return outlets with at least this much floor area. Only applies to outlet records." }, "multiOutletOnly": { "title": "Multi-outlet systems only", "type": "boolean", "description": "Only return library systems that operate more than one outlet (central + branches). A signal of larger, multi-location accounts.", "default": false }, "withPhoneOnly": { "title": "With phone number only", "type": "boolean", "description": "Only return records that have a phone number on file (~99% of libraries do).", "default": false }, "excludeTemporarilyClosed": { "title": "Exclude temporarily-closed", "type": "boolean", "description": "Skip libraries/outlets flagged as temporarily closed in the survey year. On by default for a clean, contactable list.", "default": true }, "minLeadScore": { "title": "Min lead score", "minimum": 0, "maximum": 100, "type": "integer", "description": "Only return records with a derived lead score (0–100) at or above this value." }, "sortBy": { "title": "Sort order", "enum": [ "leadScore", "populationHigh", "revenueHigh", "visitsHigh", "squareFeetHigh", "name", "state", "source" ], "type": "string", "description": "Lead score = highest-value accounts first (recommended). Population / Revenue / Visits = biggest libraries first. Square footage = biggest outlets first. Name / State = alphabetical. Source = file order (fastest).", "default": "leadScore" }, "maxResults": { "title": "Max results", "minimum": 1, "type": "integer", "description": "Maximum number of records to return in this run. The full survey holds ~9,000 library systems and ~17,000 outlets.", "default": 1000 }, "maxResultsPerState": { "title": "Max results per state", "minimum": 1, "type": "integer", "description": "Optional — cap the number of records per state for an even national spread. Leave empty for no per-state cap." }, "deduplicate": { "title": "Deduplicate", "type": "boolean", "description": "Remove duplicate records by unique IMLS identifier (FSCS key, plus outlet sequence for branches).", "default": true }, "includeRawFields": { "title": "Include raw source fields", "type": "boolean", "description": "Attach the complete raw IMLS survey row as sourceFields on every record so no data is lost. Turn off for a smaller, tidier output.", "default": true }, "monitorMode": { "title": "Monitoring mode — only new records", "type": "boolean", "description": "Remember which libraries/outlets were already returned (in a named key-value store) and emit ONLY records that are new since the last run — each tagged monitorEvent: \"new\". IMLS refreshes the survey annually; run this on a Schedule to capture newly-added libraries as fresh leads. Works alongside Apify Schedules (the schedule starts the run; monitoring decides what's new).", "default": false }, "monitorKey": { "title": "Monitor key", "type": "string", "description": "Names the memory used by monitoring mode. Use a distinct key per saved watch (e.g. per state) so different monitors don't share state.", "default": "default" }, "dataFileUrl": { "title": "Data file URL (advanced)", "type": "string", "description": "Advanced: override the IMLS PLS public-use CSV ZIP URL. Leave empty to auto-resolve the latest fiscal year from the IMLS website." }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "The actor downloads the official IMLS data file directly and needs no proxy in almost all cases — leave this off for the fastest, free download. If a direct download ever fails, the actor automatically retries through Apify Residential (US) proxy as a fallback.", "default": { "useApifyProxy": false } } } }, "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 } } } } } } } } } } ```