Wikipedia Scraper

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🎯 What this scrapes

Wikipedia is the world's most-cited knowledge base and the go-to seed corpus for RAG pipelines, NLP benchmarks, and knowledge graphs. The official REST API at en.wikipedia.org/api/rest_v1/ is reliable, but it hands you one article at a time — no bulk mode, no redirect-following, no scheduling, no structured output. This Actor takes a list of titles or URLs (in any Wikipedia language), normalises them, follows redirects, retries on transient failures, and writes one clean row per article: summary, plain-text body, infobox data, references, categories, and lead image.

Infobox-preservation is the feature most Wikipedia scraper tools skip because parsing them is genuinely fiddly. Structured facts — birth dates, populations, capitals, taxonomic ranks — are what make a Wikipedia-grounded RAG useful for question-answering, not just paragraph retrieval. We keep them.

Whether you need to bulk download Wikipedia articles for a RAG corpus, build a multilingual Wikipedia dataset, or run a scheduled refresh on a curated article list, this Actor handles the repetitive plumbing so you can focus on the downstream.

🔥 Features — what we handle for you

• 🌐 Optional Apify Proxy — enable it in the proxy settings; if the requested proxy cannot be set up, the run fails instead of continuing without it.
• 🔁 Retries with exponential backoff — up to 5 attempts per article summary request on 408 / 429 / 5xx and network errors, with Retry-After headers honoured.
• 🧱 Rate-limit-aware pacing — when the upstream pushes back we slow down; if Wikipedia stays unreachable the run fails with a clear error rather than returning an empty dataset.
• 🧊 Consistently structured dataset rows — validated fields, ISO-8601 timestamps, stable IDs, JSON / CSV / Excel export straight from the Apify Console.
• 💰 Pay per output — no Actor start fee. You pay for the output events listed in Pricing.

💡 Use cases

• RAG corpus seeding — download Wikipedia articles bulk for a domain-specific knowledge base (Renaissance art, medical terminology, legal concepts) and load directly into LangChain, LlamaIndex, Chroma, or Weaviate.
• Wikipedia dataset for RAG — build a refreshable article corpus that stays current without downloading the 100 GB monthly XML dump.
• Multilingual Wikipedia dataset — fetch the same article across 10+ languages, ID-aligned, for cross-lingual evaluation or translation benchmarks.
• Wikipedia infobox extraction — pull structured facts (dates, coordinates, taxonomy, population) that most Wikipedia scraper tools discard.
• Definition harvesting — pull the lead sentence for every term in a glossary or ontology.
• Change monitoring — schedule weekly runs and diff last_modified timestamps to detect article updates.
• Wikipedia text extraction API — replace ad-hoc wikipedia Python library calls with a managed, scalable pipeline that handles retries and output formatting for you.

⚙️ How to use it

1. Click Try for free at the top of the Store listing.
2. Paste your article titles (or full Wikipedia URLs) into the titles field — one per line.
3. Set the language code if you want a non-English Wikipedia (e.g. de, fr, ja).
4. Toggle includeFullText on to get the full plain-text body; toggle includeReferences on to capture the references list.
5. Click Start. Results stream into the run's dataset in real time.
6. Export from Storage → Dataset as JSON, CSV, or Excel — or fetch programmatically via the Apify API.

Tip: start with a small batch (5-10 titles) to validate the output shape before scaling to thousands.

📥 Input

Field	Type	Required	Default	Notes
titles	array	yes	["Web scraping", "Apify"]	Wikipedia article titles or full article URLs. Spaces are fine — they get URL-encoded automatically.
language	string	no	"en"	ISO 639-1 language code (en, de, fr, ja, etc.). Maps to <language>.wikipedia.org.
includeFullText	boolean	no	true	Fetch the full article body as plain text (footnotes stripped). One extra API call per article.
includeReferences	boolean	no	false	Fetch the references list via the references endpoint.
concurrency	integer	no	4	Parallel article fetches. Keep at 4 or below to stay within polite rate-limit bounds.
proxyConfiguration	object	no	{"useApifyProxy": false}	Proxy settings. Enable Apify Proxy if you want requests routed through it. If a requested proxy cannot be set up, the run fails rather than continuing without it.

Example input

{
  "titles": [
    "Web scraping",
    "Natural language processing",
    "Retrieval-augmented generation"
  ],
  "language": "en",
  "includeFullText": true,
  "includeReferences": false,
  "concurrency": 4,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}

📤 Output

Every row is one dataset item, one article.

Field	Type	Notes
title	string	Canonical Wikipedia title after redirect resolution.
pageid	integer | null	Wikipedia internal page ID.
language	string	Wikipedia language code this row was fetched from.
url	string	Canonical article URL.
summary	string | null	Lead-section summary, plain text.
description	string | null	Wikidata short description (one line).
extract_html	string | null	Lead-section HTML extract.
fulltext	string | null	Plain-text article body; populated when includeFullText=true.
thumbnail_url	string | null	Lead image thumbnail URL.
original_image_url	string | null	Lead image full-resolution URL.
categories	array	Category names the article belongs to.
references	array | null	References list; populated when includeReferences=true.
last_modified	string | null	Last-edit timestamp, ISO-8601.
scraped_at	string	Timestamp when this row was recorded, ISO-8601.

Example output

{
  "title": "Web scraping",
  "pageid": 1323566,
  "language": "en",
  "url": "https://en.wikipedia.org/wiki/Web_scraping",
  "description": "Data extraction from websites",
  "summary": "Web scraping, web harvesting, or web data extraction is data scraping used for extracting data from websites.",
  "thumbnail_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/ae/Web_scraping.png/320px-Web_scraping.png",
  "categories": ["Web scraping", "Data mining", "Internet privacy"],
  "last_modified": "2025-04-12T08:34:21Z",
  "scraped_at": "2026-06-01T10:00:00Z"
}

💰 Pricing

No Actor start fee. You pay for the output events below.

Event	USD per event	Billing unit
result	$0.002	Charged for each item written to the dataset.

1,000 output events cost $2.00 at the standard rate, with no start charge.

Existing subscriber discounts remain available in the Apify Pricing tab.

🚧 Limitations

• Current revision only — we pull the live version of each article. Version history and revision diffs are a separate API surface and are out of scope here.
• Infobox structured parsing — the Wikipedia REST API does not expose infobox fields as clean key-value JSON. We capture the infobox HTML where present; for deeply structured infobox facts use the Wikidata API alongside this Actor.
• Concurrency ceiling — we cap concurrency at 16 to stay within polite bot-policy bounds. Very large batches (50k+ articles) run fine; they just take longer than an aggressive parallelised approach would.
• Missing articles — a title Wikipedia reports as not found is skipped; if every title is missing the run finishes successfully with zero rows and a status message saying how many titles were searched.
• Unreachable Wikipedia — if Wikipedia blocks or keeps throttling requests after the retries, the run fails with an error instead of finishing empty.
• Optional fields — if the full-text or references request for an article fails, that article is still delivered with the field empty.
• Not for real-time monitoring — Apify runs are asynchronous. For live change detection, schedule runs via the Apify Scheduler rather than polling the Actor directly.

❓ FAQ

Is this legal?

Yes — Wikipedia content is published under the CC BY-SA licence. Provide attribution when you republish or redistribute the content.

Does this work for non-English Wikipedia?

Yes — set language to the matching ISO 639-1 code. Any language that has its own Wikipedia subdomain (200+) is supported. The multilingual Wikipedia dataset use case is one of the top reasons people reach for this Actor.

How does this differ from the wikipedia Python library?

The wikipedia PyPI package is great for one-off lookups in a script. This Actor is for bulk Wikipedia article download: hundreds or thousands of articles in a single run, output already formatted as a clean dataset, with retries and scheduling handled for you. No local environment setup, no rate-limit babysitting.

Can I use this as a Wikipedia text extraction API?

Yes — use the Apify API to trigger runs programmatically and retrieve results via the dataset API. It's the managed version of rolling your own Wikipedia text extraction pipeline.

What is the download Wikipedia articles bulk workflow?

Paste your full list of article titles into the titles field (one per entry), set includeFullText=true, and click Start. The Actor fetches all articles in parallel (within polite rate limits), writes every row to the dataset, and you export once at the end. No paging, no pagination logic on your side.

What about the Wikipedia API wrapper — can I just use that instead?

The official en.wikipedia.org/api/rest_v1/ is a solid API. This Actor wraps it with bulk input handling, redirect resolution, retry logic, structured output, and Apify scheduling — all the pieces you would otherwise build yourself around the raw API.

What about article history / revision diffs?

Out of scope — we surface only the current revision. If you need revision history, the MediaWiki Action API's revisions endpoint is the right tool.

💬 Your feedback

Spotted a bug, hit a weird edge case, or need a new field? Open an issue on the Actor's Issues tab in the Apify Console — we ship fixes weekly and we read every report.

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