Info Lithuania Scraper: 20 Data Fields, Up to 1,000 Free Results/Month

Each company record you scrape from the info.lt directory carries 20 output fields, including the internal company ID, full street address, telephone, website, detailed description, and branch locations. This scraper handles keyword searches, taxonomy browsing across more than 300 business categories, and direct URL lookups. You can try it using Apify's free plan, which includes enough monthly usage to gather up to 1,000 results. This tool is built for engineering teams building targeted prospect lists by category and city, mapping business density, or feeding local directory search products. It is not suitable for users who require email addresses, as these are intentionally excluded due to complex client-side obfuscation on the target site.

Try it: open Info Lithuania Scraper on Apify, sign in on the free plan and run the prefilled example.

Can you try Info Lithuania Scraper before paying?

Yes. Apify's free plan includes $5.00 of prepaid usage every month and asks for no credit card. At $0.005 per result, that covers up to 1,000 results of Info Lithuania Scraper a month, before run-start charges and platform usage.

Info Lithuania Scraper was last updated on 2026-07-21. It is one of 1,725 Actors CrawlerBros publishes on Apify, which together have 692,561 lifetime public runs and an average rating of 4.63 out of 5 across 416 reviews.

What does it cost to run Info Lithuania Scraper?

Each result costs $0.005 on Apify's free plan, which is $5.00 per 1,000 results. Starting a run is charged separately at $0.005 per GB of Actor memory. Apify also bills the platform usage each run consumes, at the rates of your Apify plan, on top of these charges.

The primary driver of your run cost is the maxItems setting, which limits the number of scraped profiles written to the dataset. To minimize expenses, always test your queries with a low item cap before scaling. Toggling the fetchDetails option off also speeds up your runs, reducing the overall platform usage consumption per execution.

How do you run Info Lithuania Scraper from the API?

The schema marks 1 of its 12 controls as required: mode. The payload below uses the schema's own prefilled values, so it runs as written once you substitute your API token.

Call the synchronous endpoint to start a run and receive dataset items in one request:

curl -X POST "https://api.apify.com/v2/acts/crawlerbros~info-lt-scraper/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"mode":"search","searchQuery":"restoranas","companyUrls":[{"url":"https://www.info.lt/imones/Rejben-UAB/2383263"}]}'

The same run from Python, using the official client:

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")

run_input = {
  "mode": "search",
  "searchQuery": "restoranas",
  "companyUrls": [
    {
      "url": "https://www.info.lt/imones/Rejben-UAB/2383263"
    }
  ]
}

run = client.actor("crawlerbros~info-lt-scraper").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

And from Node.js:

import { ApifyClient } from 'apify-client'

const client = new ApifyClient({ token: '<YOUR_APIFY_TOKEN>' })

const input = {
  "mode": "search",
  "searchQuery": "restoranas",
  "companyUrls": [
    {
      "url": "https://www.info.lt/imones/Rejben-UAB/2383263"
    }
  ]
}

const run = await client.actor('crawlerbros~info-lt-scraper').call(input)
const { items } = await client.dataset(run.defaultDatasetId).listItems()
console.log(items)

The synchronous endpoint holds the connection open until the run finishes, which is convenient for small batches and wrong for large ones. For anything long running, start the run asynchronously and poll, or attach a webhook, so a dropped connection does not cost you the results.

Which Info Lithuania Scraper inputs matter, and which can you skip?

The essential setting is the mode selector, which lets you choose between search, byCategory, and byUrls. Most users should start with search mode and a searchQuery, leaving advanced overrides like customCategory alone unless a specific niche category is missing from the dropdown menu.

• mode (string): What to fetch: keyword search, category browse, or direct company URL fetch. Default: "search".
• searchQuery (string): Free-text keyword to search for, e.g. company name, service, or product (mode=search). Example: restoranas, automobilių remontas, baldai.
• category (string): Business category / sub-category to browse (mode=byCategory). Covers the full info.lt category taxonomy.
• customCategory (string): Advanced override for category (mode=byCategory) - paste a category URL or slug/id pair directly (e.g. Baldai-prekyba/100209503) if you know the exact info.lt category not covered by the dropdown.
• city (string): Restrict results to a specific Lithuanian city or municipality (applies to both search and byCategory modes). Leave empty for nationwide results. Default: "".
• companyUrls (array): Direct info.lt company page URLs to fetch (mode=byUrls), e.g. https://www.info.lt/imones/Rejben-UAB/2383263.
• containsKeyword (string): Only keep companies whose name, description, or categories contain this text (case-insensitive). Applies to all modes.
• requireWebsite (boolean): Only keep companies that have a website URL. Default: false.
• requirePhone (boolean): Only keep companies that have a phone number. Default: false.
• requireEmailListed (boolean): Only keep companies for which info.lt has an email address on file (uses info.lt's own "Turi el. paštą" server-side filter). Applies to search and byCategory modes. Note: the email address itself is not extracted/output - see the FAQ. Default: false.
• fetchDetails (boolean): Visit each company's detail page to collect description, opening hours, gallery, branches, and full category list. Disable for faster, listing-only runs (name, address, phone, website, primary categories only). Default: true.
• maxItems (integer): Maximum number of companies to return. Default: 20.

Fixed-choice controls: mode accepts search (Search by keyword), byCategory (Browse by category), byUrls (Fetch specific companies by URL); category accepts 341 values, including Advokatai/100209472 (Advokatai), Akmuo-akmens-gaminiai/100209473 (Akmuo, akmens gaminiai), Akvariumai/100222536 (Akvariumai), Alkoholiniai-g%C4%97rimai/100209474 (Alkoholiniai gėrimai); city accepts 55 values (default ""), including "" (All of Lithuania), Vilnius, Kaunas, Klaipėda.

What does Info Lithuania Scraper return?

The output files provide structured contact and location details suitable for database enrichment, sales pipelines, and localized search applications. However, the records conspicuously omit email addresses and official enterprise registration codes, as these are not retrievable from the public listing pages.

• companyId - info.lt internal company ID
• name - company / business name
• primaryCategory - main business category
• categories[] - all listed business categories
• fullAddress - complete street address as shown on info.lt
• city - city or municipality parsed from the address
• phone - contact phone number
• fax - fax number (when different from phone)
• website - company website URL
• description - business description text
• keywords[] - SEO search terms associated with the listing (capped at 20)
• openingHours[] - { day, hours } per weekday, when published
• latitude, longitude - map coordinates
• logoUrl - company logo image URL
• photos[] - photo gallery image URLs
• branches[] - other locations of the same business, { name, sourceUrl }
• sourceUrl - canonical info.lt company page URL
• recordType: "company", scrapedAt

These are the documented fields. Optional ones can be empty on a given record, so measure how often each field your deliverable depends on is populated across a real sample before automating the handoff.

How do you build the workflow end to end?

Open Info Lithuania Scraper and work through these in order. Each step ends with something to check, so a bad configuration surfaces on a small run rather than a scheduled one.

1. Run a quick trial with mode set to search, searchQuery set to a common term like restoranas, and maxItems restricted to 10 to inspect the returned structure.
2. Verify that the returned objects contain the expected contact fields such as phone and website before initiating a broader collection.
3. Switch mode to byCategory if you want to systematically extract an entire industry sector, selecting one of the 300+ taxonomies from the category dropdown.
4. Apply a city filter using the city dropdown to restrict the search to specific municipalities, like Vilnius or Kaunas, to optimize the execution speed.
5. Disable the fetchDetails boolean to run a rapid, listing-only crawl if you only need high-level fields like name, address, and primaryCategory.
6. Leave fetchDetails enabled to visit individual profile pages and pull rich details including description, openingHours, photos, and branches.
7. Input exact company page links into the companyUrls array and switch mode to byUrls if you need to refresh specific directory profiles rather than discovery.
8. Check the dataset output for the recordType field to confirm each item has successfully written as a company record.

How do you apply it? Three worked playbooks

These are Info Lithuania Scraper's own documented use cases, each worked through as an operating pattern rather than a description.

Use case 1: Lead generation

Outcome: Build targeted prospect lists by category and city

Configure: Set mode to "byCategory", category to "Automobili%C5%B3-remontas-lengvieji/100209494", city to "Vilnius", requirePhone to true, requireWebsite to true, and maxItems to 100.

Working method: Select your target business category and geographic area to isolate verified leads. Enable phone and website filters to strip out stale listings that lack active contact channels. Execute the run and review the output to ensure the fields contain valid dialable numbers and live web links.

Deliverable: A spreadsheet containing company names, addresses, validated phone numbers, and website URLs of active automotive repair shops in Vilnius.

Stop condition: The scraper finishes without returning any results, signaling an invalid category key or restrictive search criteria.

Use case 2: Market research

Outcome: Map business density and category coverage across Lithuania

Configure: Set mode to "byCategory", category to "Restoranai/100209630", city to "", fetchDetails to true, and maxItems to 500.

Working method: Run the extraction nationwide by leaving the city parameter empty. Keep the detailed profile scraping enabled to ensure you collect coordinate pairs. Extract the resulting dataset to plot business counts and physical locations across regional maps.

Deliverable: A dataset with company names, geographical coordinates (latitude and longitude), and category tagging for competitive mapping.

Stop condition: The latitude and longitude coordinate values are missing on more than half of the returned records.

Use case 3: Directory aggregation

Outcome: Feed a comparison site or local-search product

Configure: Set mode to "search", searchQuery to "viešbučiai", fetchDetails to true, maxItems to 1000.

Working method: Execute a broad keyword search to gather rich company listings. Ensure detailed page extraction is active so you pull auxiliary content like descriptions, photo galleries, and operating schedules. Import this structured content directly into your platform database.

Deliverable: A clean dataset featuring company profiles with comprehensive openingHours, photo URLs, descriptions, and branch office listings.

Stop condition: The text in the description field displays truncated strings or HTML elements.

What breaks, and how do you design around it?

Because the directory does not offer a distance-based radius search via URL parameters, you must structure your regional scraping campaigns around the predefined city filters instead. If you hit pagination caps on massive queries, run separate targeted searches across distinct Lithuanian municipalities.

When should you not use Info Lithuania Scraper?

Do not use this scraper if your business workflows rely on obtaining direct email addresses for cold outreach campaigns, as the platform cannot extract them. If you require business information from neighboring European regions, you should look at alternative directories. For Portuguese directory data, consider using the PaiPt Scraper - Portugal Business Directory (Paginas Amarelas). If you are targeting Dutch businesses, use the DeTelefoongids (Dutch Business Directory) Scraper instead. If your directory target is Poland, the PanoramaFirm Business Directory Scraper is the better choice. For projects where geographic points of interest and latitude/longitude coordinates are the only priority, the OpenStreetMap Places & POI Scraper will deliver more comprehensive geospatial coverage without the limitations of a commercial directory.

What should you check before trusting the output?

• Monitor the ratio of omitted fields, as empty attributes like website, phone, and openingHours are dropped from the output instead of returning null values.
• Confirm that the scrapedAt timestamp is present on all records to keep track of when the live data was retrieved from the directory.
• Set up an automated filter to flag records where the city field parsed from the fullAddress is empty, which can occur with highly irregular address formats.
• Check that the primaryCategory value matches your targeted industry segment when running in byCategory mode.
• Stop the scheduled run if the dataset returns zero items, indicating that the category slug or search query has no active matching listings.

None of this proves a record is correct. It gives a scheduled Info Lithuania Scraper run defined points where it should stop instead of quietly passing bad data downstream.

Frequently asked questions

Can I obtain email addresses using this scraper?

No. The directory hides email addresses behind client-side JavaScript reveal triggers that do not resolve to reliable strings. The scraper excludes this field to avoid saving corrupted data. However, you can use the requireEmailListed filter to target only businesses that have an email on file.

How much does it cost to scrape 10,000 listings with this Actor?

On the free plan, results cost $0.005 each, which translates to $5.00 per 1,000 results. Gathering 10,000 listings would cost $50.00 in result charges, plus platform usage. Paid Apify plans reduce the per-result cost, meaning you will pay less on higher plans.

What is the company registration code field in the output?

This field does not exist in the output dataset. The directory platform uses registration codes for its internal search filters but does not display them on company listing profiles, making it impossible for the scraper to collect them.

Why did my search run finish with zero results?

This is normal behavior rather than an Actor error. It means there are no business listings in the directory that match your chosen keyword, category, or city filter combination. Try broadening your searchQuery or clearing the city filter.

Can I target specific locations using GPS coordinate radius queries?

No. The directory's radius search requires browser geolocation access and cannot be automated server-side. To collect regional results, select one of the 54 municipalities from the city input dropdown instead.

Where to go next

When you are ready to run it, open Info Lithuania Scraper on Apify; the free plan covers up to 1,000 results a month.

Start with the Info Lithuania Scraper Actor page for the current input schema, pricing tier, and run history.

Other Actors we maintain for related data:

• SeccionAmarilla Mexico Business Directory Scraper: Scrape SeccionAmarilla.com.mx, Mexico's leading business directory.
• PaiPt Scraper - Portugal Business Directory (Paginas Amarelas): Scrape pai.pt (Portugal's Yellow Pages / Paginas Amarelas business directory) listings.
• OpenStreetMap Places & POI Scraper: Scrape Points of Interest (POIs) and business listings from OpenStreetMap via the free Overpass API.
• BusinessList.my Malaysia Business Directory Scraper: Scrape businesslist.my, Malaysia's verified online business directory with 280,000+ listings.
• DeTelefoongids (Dutch Business Directory) Scraper: Scrape Telefoonboek.nl, the Netherlands' official business and phone directory.
• PanoramaFirm Business Directory Scraper: Scrape PanoramaFirm.pl - Poland's major business directory.
• Krak.dk Business Directory Scraper: Scrape Krak.dk, Denmark's leading business and phone directory.
• Imenik.eu Croatia Business Directory Scraper: Scrape Imenik.eu, Croatia's business directory.

Related guides:

• Operational Guide for the BusinessList.my Malaysia Business Directory
• Gulesider Scraper: Practical Guide for Norwegian Directory Workflows
• GuiaMais Brazil Business Directory Scraper: 36 Data Fields per Record
• Firmy.cz Business Directory Scraper: 3 Practical Use Cases

Resources

• Actor documentation, input schema, and pricing: verified against the published Actor on 2026-09-29.

• Actor last updated by its maintainers on 2026-07-21.

• Run outcome figures cover the 30 day public window ending 2026-09-29.

• Info Lithuania Scraper on Apify