🍿 Netflix Search Scraper — Movies, Series, IMDb Ratings & Country Availability

Netflix Search Scraper is a production-grade Apify Actor for Netflix catalogue data extraction — no Netflix account, login or API key needed. Type a search term (or paste a title link) and it returns movies and series with IMDb ratings, votes, awards and plot, genres, cast & crew, season/episode breakdowns, posters and artwork, plus the feature nobody else gives you: full region-by-region streaming availability with added and expiry dates — streamed to your Apify Dataset in real time.

💡 Need a custom version, private integration, or a tailored pipeline? Email hello.dataminds@gmail.com.

Built for streaming analysts, content acquisition & licensing teams, media researchers, film/TV databases and recommendation apps, market-entry research and AI/LLM data pipelines — anyone who needs clean, structured Netflix catalogue data without an official API that does not exist.

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📑 Table of contents

• What is Netflix Search Scraper?
• Main features
• Who is this Actor for?
• What the scraper does
• Inputs it accepts
• Filtering configuration
• Output format (Dataset)
• Output fields reference
• Example output (JSON)
• Quick start
• Input parameters reference
• Migrating from another Netflix scraper
• Reliability & anti-blocking
• Integrations & automation
• Pricing & how to control cost
• Frequently asked questions (FAQ)
• Troubleshooting
• Help, support & custom builds
• Is scraping Netflix data legal?
• SEO keywords targeted

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🍿 What is Netflix Search Scraper?

Netflix publishes no public API. Its own search box shows you a poster and a title and nothing else — no IMDb score, no cast list, no way to ask "which countries is this actually streaming in, and when does it leave?", and no way to export anything.

This Actor closes that gap:

• Search the whole Netflix catalogue by keyword — fashion, true crime, korean thriller — each term searched separately, exactly like typing it into the search box.
• Or paste links — a Netflix search URL, or a link to one specific title. Bulk input and file upload supported.
• Filter like a professional — movies vs series, release-year range, IMDb rating range, region, audio language, subtitle language, sort order, and "leaving soon" / "added in the last 24 hours".
• Get the full title record — title, type, release year, runtime, synopsis, poster and large artwork, Netflix rating, maturity rating, and a direct netflix.com/title/… watch link.
• Get the IMDb layer for free — rating, vote count, metascore, genres, awards, full plot, IMDb id and link, plus Top-250 flags.
• Get the availability layer nobody else exposes — every region a title streams in, the date it was added there, its expiry date, the audio and subtitle languages carried in that region, and which seasons are available. Netflix's own pages never show this, and most scrapers cap it at the first handful of countries.
• Get cast, crew, genres and full season/episode breakdowns — opt-in, one toggle each.
• Stream every finished title to your Dataset — export to JSON, CSV, Excel, XML, or pull them through the Apify API.

If you have ever needed "every Netflix thriller from 2018 onward rated 7+, with the countries it streams in and when it expires, as clean rows" — this is the Actor.

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✨ Main features

• 🔎 Plain-text catalogue search — type terms exactly as you would in Netflix's search box; each line is searched separately.
• 🔗 Links accepted too — Netflix search URLs and links to individual titles, in one bulk input field with file upload.
• 🎬 📺 Movies and series — collect both, or restrict to one with a single toggle.
• 🧰 Rich filters — release-year range, IMDb rating range, region + region-match mode (any / all / only these / not in these), audio language, subtitle language, sort order, and availability filters.
• 🌍 Full region-by-region availability — the headline feature: every streaming region with added date, expiry date, audio & subtitle languages and season coverage, not a truncated first-ten list.
• ⭐ Complete IMDb layer — rating, votes, metascore, genres, awards, full plot, IMDb link, Top-250 film and TV flags.
• 🎭 Cast & crew — actors, directors, writers and producers, grouped by role.
• 📺 Season & episode breakdown — every season with its episode list, numbers, titles, synopses and stills.
• 🖼️ Posters & artwork — thumbnail, poster and large artwork URLs for every title.
• 🚦 Automatic network fallback — direct ➜ datacenter ➜ residential, sticky after the first switch.
• 🔄 Automatic source fallback — if the main catalogue index is unreachable, the run switches to a backup index and still returns data instead of failing.
• 📦 Live dataset writes and nine prebuilt table views — Overview, Ratings & IMDb, Where it streams, Cast & genres, Seasons & episodes, Artwork & story, Links & ids, Source & quality, Errors.
• 📊 Run summary stored in the key-value store, with per-run totals.
• 🔁 Drop-in compatible with the input field names other Netflix scrapers on the Store use.

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👥 Who is this Actor for?

• 📊 Streaming & media analysts — track what is on Netflix in each market and how it changes.
• 🤝 Content acquisition and licensing teams — see exactly which regions carry a title and when the licence expires.
• 🎬 Film & TV databases, recommendation apps — bulk-populate a catalogue with ratings, genres, cast and artwork.
• 🌍 Market-entry and competitive research — compare catalogue depth across 37 regions.
• 📰 Journalists & researchers — back a "what's leaving Netflix" story with real dates.
• 🤖 AI / LLM pipelines — clean, structured, typed rows ready for embedding or fine-tuning.
• 📈 Growth & SEO teams — build "what to watch" content from live catalogue data.

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⚙️ What the scraper does

1. Reads your search terms and links, and works out what each one is asking for.
2. Runs every search against the catalogue with your filters applied.
3. Walks the result pages until your per-search limit is filled or the results run out.
4. For each title, collects the extra detail the search results never show — IMDb layer, region availability, genres, cast, episodes.
5. Writes each finished title to your Dataset the moment it is ready, so an aborted or migrated run keeps everything collected so far.
6. Reports what it could not find as explicit error rows, never as silent gaps.

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📥 Inputs it accepts

Input	Example	What you get
🔎 Search term	fashion	Every matching movie and series, up to your limit
🔎 Search term	korean thriller	Same, searched separately from the other terms
🔗 Netflix search URL	https://www.netflix.com/search?q=documentary	Treated as a search for documentary
🔗 Title link	a link that contains the title name	That one title, matched by name
🔗 Netflix title URL	https://www.netflix.com/title/81002370	That one title, looked up by its catalogue id

Search terms go in 🔎 Search terms, links go in 🔗 Netflix / catalogue links. Use either, or both — they run in the same pass.

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🎯 Filtering configuration

Filter	Values	Notes
🎞️ Movies or series	both / movies / series
📅 Released from / until	any year	Enter them backwards and they are swapped for you
⭐ Min / max IMDb rating	0–10	Titles with no rating are excluded when a minimum is set
🌍 Regions	37 Netflix regions	Leave empty to search every region
🧩 Region match	any / all / only these / not in these	How several selected regions combine
🗣️ Audio language	e.g. English, Korean	Pick from the list or type your own
💬 Subtitle language	e.g. Spanish	Same
🧩 Audio & subtitle match	either / both
🔃 Sort by	relevance, newest, rating, title, year, runtime …
⏳ Availability	everything / leaving soon / added in the last 24 hours

Every filter is optional. Leave one blank to skip it.

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📤 Output format (Dataset)

Each title is written as one Dataset row, the moment it is finished — not batched at the end of the run. The Console shows nine prebuilt views, so you can jump straight to the section you need:

View	What it shows
✨ Overview	Poster, title, type, year, ratings, runtime, region count, watch link
⭐ Ratings & IMDb	Every score, vote count, metascore, genres, awards, IMDb link
🌍 Where it streams	Region count and the full per-region breakdown with dates
🎭 Cast & genres	Genres plus actors, directors, writers and producers
📺 Seasons & episodes	Series only — every season with its episode list
🖼️ Artwork & story	Poster, artwork, synopsis, full IMDb plot, maturity rating
🔗 Links & ids	Netflix id, IMDb id, and every link for the title
🧭 Source & quality	Which search produced the row and how completely it was filled
⚠️ Errors	Inputs that returned nothing, and why

A run summary record is also written to the key-value store under run-summary.

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📚 Output fields reference

Field	Type	Description
keyword	string	The search term or title name this row came from
source	string	Which index answered — catalogue or backup-index
id	string / number	Record id in that index
title	string	Title name
slug	string	URL-friendly title slug
type	string	movie or series
netflixId	number	Netflix's own catalogue id
netflixUrl	string	Direct netflix.com/title/… watch link
img / poster	string	Artwork and poster URLs
largeImage	string	High-resolution artwork
synopsis	string	Short description
avgrating	number	Catalogue rating
year	number	Release year
runtimeSeconds / runtimeMinutes	number	Runtime in both units
maturityRating	string	Age/maturity label
imdbid / imdbUrl	string	IMDb id and link
imdbRating	number	IMDb rating
imdbVotes	number	IMDb vote count
imdbMetascore	string	Metascore
imdbGenre	string	IMDb genre string
imdbAwards	string	Awards summary
imdbPlot	string	Full IMDb plot
tmdbScore	number	TMDb score, when available
top250 / top250tv	number	Top-250 film / TV flags
genres	array	Catalogue genre labels
cast	array	{ role, names[] } per role
seasons	array	{ season, episodeCount, episodes[] }
availableCountries	array	Per-region: code, name, added date, expiry date, audio, subtitles, seasons
countryCount	number	How many regions carry the title
addedDate	string	When the title was added
lastCheckedDate	string	When availability was last verified
titledate	string	Catalogue date for the title
enrichmentStatus	string	complete or partial
enrichmentErrors	array	Anything that could not be collected, spelled out

Fields the catalogue does not publish for a given title are simply absent from that row rather than filled in with a guess — check enrichmentErrors to see exactly what was missing and why.

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🧾 Example output (JSON)

{
  "keyword": "fashion",
  "source": "catalogue",
  "id": 4877,
  "title": "Emily in Paris",
  "slug": "emily-in-paris",
  "type": "series",
  "netflixId": 81037371,
  "netflixUrl": "https://www.netflix.com/title/81037371",
  "img": "https://occ-0-1433-1432.1.nflxso.net/dnm/api/v6/…jpg",
  "poster": "https://occ-0-1433-1432.1.nflxso.net/dnm/api/v6/…jpg",
  "synopsis": "After landing her dream job in Paris, Chicago marketing executive Emily embraces a new life while juggling work, friends and romance.",
  "avgrating": 6.9,
  "year": 2020,
  "runtimeSeconds": 1800,
  "runtimeMinutes": 30,
  "imdbid": "tt8962124",
  "imdbUrl": "https://www.imdb.com/title/tt8962124",
  "imdbRating": 6.9,
  "imdbVotes": 128441,
  "imdbMetascore": "60",
  "imdbGenre": "Comedy Drama Romance",
  "imdbAwards": "Nominated for 4 Primetime Emmys",
  "imdbPlot": "A young American woman moves to Paris for an unexpected job opportunity…",
  "maturityRating": "TV-MA",
  "largeImage": "https://occ-0-1433-1432.1.nflxso.net/dnm/api/v6/…jpg",
  "top250": 0,
  "top250tv": 0,
  "genres": ["TV Comedies", "Romantic TV Comedies"],
  "cast": [
    { "role": "Actor", "names": [{ "fullname": "Lily Collins" }, { "fullname": "Philippine Leroy-Beaulieu" }] },
    { "role": "Director", "names": [{ "fullname": "Andrew Fleming" }] }
  ],
  "seasons": [
    { "season": 1, "episodeCount": 10, "episodes": [{ "epnum": 1, "title": "Emily in Paris", "synopsis": "…" }] }
  ],
  "availableCountries": [
    {
      "countryId": 78,
      "code": "FR",
      "name": "France",
      "addedDate": "2020-10-02",
      "expiresDate": null,
      "audioLanguages": "English [Original],French",
      "subtitleLanguages": "English,French,Spanish",
      "seasonsAvailable": "S1(10), S2(10)"
    }
  ],
  "countryCount": 31,
  "addedDate": "2020-10-02",
  "lastCheckedDate": "2026-09-01",
  "titledate": "2020-10-02",
  "enrichmentErrors": [],
  "enrichmentStatus": "complete"
}

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🚀 Quick start

Apify Console

1. Log in at console.apify.com → Actors.
2. Open Netflix Search Scraper.
3. Type a search term under 🔎 Search terms — or paste links under 🔗 Netflix / catalogue links.
4. Set 📬 Max titles per search, and add any filters you want.
5. Click Start and watch the run log — every title appears as it is saved.
6. Open the Output tab and pick a view, then export to JSON / CSV / XLSX.

API

curl -X POST "https://api.apify.com/v2/acts/<ACTOR_ID>/run-sync-get-dataset-items" \
     -H "Authorization: Bearer $APIFY_TOKEN" \
     -H "Content-Type: application/json" \
     -d '{
       "keywords": ["fashion"],
       "maxItems": 25,
       "type": "movie",
       "minRating": 6.5,
       "countries": ["US", "GB"]
     }'

Python client

from apify_client import ApifyClient

client = ApifyClient("<APIFY_TOKEN>")
run = client.actor("<ACTOR_ID>").call(run_input={
    "keywords": ["true crime"],
    "maxItems": 50,
    "type": "series",
    "startYear": 2018,
    "includeCast": True,
    "includeEpisodes": True,
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["title"], item.get("imdbRating"), item.get("countryCount"))

More input examples

Everything leaving Netflix soon in the UK

{ "keywords": ["drama"], "countries": ["GB"], "filterBy": "Expiring", "maxItems": 100 }

Highly-rated Korean series with English subtitles

{ "keywords": ["korean"], "type": "series", "minRating": 7.5, "subtitle": "English", "orderBy": "Rating" }

One specific title, with everything switched on

{ "urls": [{ "url": "https://www.netflix.com/title/81037371" }], "includeCast": true, "includeEpisodes": true }

A market-comparison run across three regions

{ "keywords": ["documentary"], "countries": ["US", "DE", "JP"], "countryMatch": "and", "maxItems": 200 }

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⚙️ Input parameters reference

🚀 Start here

Field	Type	Default	Description
urls	array	[]	Netflix search URLs or links to individual titles. Bulk input & file upload
keywords	array	[]	Plain search terms, one per line

📬 How much to collect

Field	Type	Default	Description
maxItems	integer	50	Max titles per search term (1–10 000)
maxScanned	integer	20000	Safety cap on how deep a single search digs

🎞️ Filter the catalogue

Field	Type	Default	Description
type	string	""	"" / movie / series
startYear / endYear	integer	–	Release-year range
minRating / maxRating	number	–	IMDb rating range, 0–10
orderBy	string	Relevance	Sort order
filterBy	string	""	"" / Expiring / New last 24 hours

🌍 Regions & languages

Field	Type	Default	Description
countries	array	[]	Region codes; empty = all regions
countryMatch	string	or	or / and / unique / notin
audio / subtitle	string	""	Language names
audioSubtitleMatch	string	or	or / and

🧩 Extra details per title

Field	Type	Default	Description
includeImdbExtras	boolean	true	IMDb rating, votes, metascore, awards, plot, artwork
includeCountryAvailability	boolean	true	Full per-region availability with dates
includeGenres	boolean	true	Catalogue genre labels
includeCast	boolean	false	Actors, directors, writers, producers
includeEpisodes	boolean	false	Seasons and episodes for series

🔬 Advanced

Field	Type	Default	Description
genreIds	string	""	Comma-separated catalogue genre ids
personId	string	""	Catalogue person id

🌍 Network & ⚡ Speed

Field	Type	Default	Description
proxyConfiguration	object	no proxy	Pin your own route, or leave it automatic
backupCountry	string	US	Region used if the run falls back to the backup index
maxConcurrency	integer	2	Titles collected in parallel
maxRequestRetries	integer	8	Attempts per request before giving up

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🔁 Migrating from another Netflix scraper

Inputs saved for other Netflix Actors on the Store run here unchanged — these field names are all accepted and mapped automatically:

Their field	Maps to	Notes
query	keywords
actor_query	keywords	Added as an extra search term
genre_query	keywords	Added as an extra search term
startUrls / searchUrls	urls
limit / maxResults / resultsLimit	maxItems
type (Movie / Series)	type	Case-insensitive
start_year / end_year	startYear / endYear
min_rating / max_rating	minRating / maxRating
filter_by / order_by	filterBy / orderBy	Same values
genre_ids / person_id	genreIds / personId

Paste your old input JSON straight in — nothing needs renaming.

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🛡️ Reliability & anti-blocking

This Actor starts every run on a direct connection — no proxy overhead unless it is actually needed. If the catalogue pushes back, the run automatically and transparently escalates:

Direct connection ➜ datacenter proxy ➜ residential proxy (3 retries)

Once the run escalates it stays on the new route for every remaining request, and every switch is logged clearly in the run log. You can also pin your own proxy group in Proxy configuration to skip the automatic ladder entirely.

On top of the network ladder there is a second, independent safety net: if the main catalogue index stops answering altogether, the run switches to a backup index and still returns data rather than failing. Backup rows carry a smaller field set — every one of them says so in its own enrichmentErrors column, and no field is ever filled in with a guess.

Other reliability behaviour worth knowing:

• Requests are paced, not just capped by concurrency — the run limits how fast requests are launched, which is what actually triggers rate limits.
• Every result is written to the Dataset the moment it is finished, so an aborted, timed-out or migrated run keeps everything collected so far.
• Abort and migration are handled gracefully — the run wraps up and writes its summary instead of dying mid-write.
• Inputs that return nothing become explicit error rows with a plain-English reason.

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🔌 Integrations & automation

• Schedules — run daily/weekly with filterBy: "New last 24 hours" to capture new Netflix additions automatically.
• Webhooks — fire ACTOR.RUN.SUCCEEDED to your own endpoint when a run finishes.
• Zapier / Make / n8n — feed dataset items straight into a spreadsheet, database or Slack alert.
• MCP / AI agents — call this Actor as a tool from Claude, Cursor or any MCP-compatible client via mcp.apify.com.
• REST API — pull dataset items directly: GET https://api.apify.com/v2/datasets/{datasetId}/items?format=json.

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💰 Pricing & how to control cost

This Actor runs on Apify's pay-per-usage pricing — you pay for the platform compute, proxy and storage your run consumes. To control cost:

• Start with a low maxItems to sample a search before a full harvest.
• Leave includeCast and includeEpisodes off unless you need them — each adds one request per title.
• Turn includeCountryAvailability off if you only want ratings and metadata.
• Leave Proxy configuration on automatic — it only escalates to a paid proxy route when actually needed.
• Narrow with filters rather than collecting everything and filtering afterwards.

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❓ Frequently asked questions (FAQ)

Do I need a Netflix account or an API key? No. Nothing is logged into, and there is no key or quota to manage. Only publicly available catalogue data is read.

Can I see which countries a title streams in? Yes — that is the headline feature. Keep 🌍 Full region-by-region availability on and every row carries a availableCountries array with the region, the date it was added, its expiry date, the audio and subtitle languages there, and which seasons are carried.

Can I find out what is leaving Netflix soon? Yes — set ⏳ Availability filter to Leaving soon, optionally with a region selected.

Does it return IMDb ratings? Yes — rating, vote count, metascore, genres, awards and the full plot, with includeImdbExtras on (the default).

Can I get every episode of a series? Yes — turn on 📺 Seasons & episodes. Each series row then carries every season with its episode list, numbers, titles, synopses and stills.

Why does maxItems sometimes return fewer titles? Because there genuinely were fewer matches, or your filters removed some. Widen the year/rating range, clear the region filter, or use a broader search term.

Why do some rows have fewer fields than others? Netflix does not publish the same metadata for every title, and if the run had to use the backup index that index carries less. enrichmentStatus and enrichmentErrors on each row tell you exactly what was and was not collected — nothing is ever invented to fill a gap.

Can I look a title up by its Netflix id? Yes — paste https://www.netflix.com/title/<id>. If the main catalogue index is unavailable at that moment, an id on its own cannot be resolved and you get a clear error row saying so; paste the title name instead.

Is there a limit on how many titles I can collect? Up to 10 000 per search term, and you can run as many search terms as you like in one run.

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🛠️ Troubleshooting

Symptom	Fix
A search comes back as an error row	Check the ⚠️ Errors view — usually the term was too narrow or the filters excluded everything
Fewer titles than maxItems	Widen the year/rating range, clear the region filter, or use a broader term
TITLE_ID_UNRESOLVABLE on a title link	The link only carried a numeric id and the main index was unavailable — paste the title name instead
Rows are missing IMDb fields	Turn on ⭐ IMDb ratings, votes, awards & plot, or the title simply has no IMDb entry
No seasons on a series	Turn on 📺 Seasons & episodes — it is off by default because it makes records large
Region filter returns nothing	Try countryMatch: "or" — and requires the title to stream in every selected region
Run seems slow	Lower maxRequestRetries, reduce maxItems, or turn off cast/episodes

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🆘 Help, support & custom builds

Something not working, or need a custom field, private integration or a bespoke pipeline built on top of this Actor? Email hello.dataminds@gmail.com — we read every message.

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⚖️ Is scraping Netflix data legal?

This Actor only reads publicly available catalogue information — the same title, rating and availability data any anonymous visitor can see. It does not log into Netflix, bypass any paywall, or touch private account data, viewing history or video streams. You are responsible for how you use the collected data — respect the applicable terms of service, copyright law, and privacy regulations (GDPR, CCPA, etc.) for your jurisdiction and use case.

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