Wikipedia is an open encyclopedia created in 2001 by Jimmy Wales and Larry Sanger. It is maintained by a community of enthusiasts and has been translated into hundreds of languages worldwide (360+). Over time, the encyclopedia has been expanded with many supporting projects, such as Wiktionary, Wikimedia Commons, Wikiquote, Wikisource, and others. This ecosystem is now unified under the Wikimedia Foundation.
Since Wikimedia projects contain more than 300 million pages of subject-specific information, they form the most comprehensive database that can be used for a wide range of tasks: AI training, building reference sections, clarifying translations and word meanings, selecting images, icons, and other types of content, and more.
This article explains how Wikipedia can be scraped: whether it has an API, where and what ready-made libraries can be found, and which approach is better in different situations — direct scraping or using the API.
Yes, Wikipedia does have an API, but it is important to understand that this is not the result of a microservices architecture. It became possible thanks to the open-source MediaWiki engine written in PHP. It is somewhat like WordPress, but designed not for blogs, but for online encyclopedia websites. The necessary programming interfaces are already built into it.
At the moment, the Wikipedia API is available in three formats:
For many common tasks, the following are already available:
Wikipedia’s APIs allow you to retrieve structured data directly, without having to download an HTML page and parse its syntax with BeautifulSoup or similar libraries. This approach is faster, more reliable, and easier.
What exactly can you get out of the box through the MediaWiki Action API (/w/api.php)?
Everything listed above is returned either in JSON format or as HTML/plain text. You do not need to manually search for the necessary tags or clean the text from service elements. The Wikipedia API already does all of this for you. As a result, automated processing of Wiki data becomes simpler and faster.
Let’s look at a few examples of direct HTTP requests for retrieving the data you need. This is roughly how Wikipedia web scraping can be organized without complex scripts or libraries:
GET https://en.wikipedia.org/w/api.php?action=parse&format=json&formatversion=2&page=Python_(programming_language)&prop=text&disableeditsection=1This request returns the full article content inside JSON, without page editing controls and service elements. Configuration is handled through the parameters format, formatversion, prop, and disableeditsection. The target page is passed in the page parameter.
GET https://en.wikipedia.org/w/api.php?action=query&format=json&formatversion=2&list=search&srsearch=Python programming language&srlimit=10&srwhat=textThis request returns a list of articles with titles, snippets (text fragments), and result positions.
GET https://en.wikipedia.org/w/api.php?action=opensearch&format=json&formatversion=2&search=Python&limit=10&namespace=0It returns the classic OpenSearch format — an array in the following structure: [query, [titles], [descriptions], [urls]].
Wikipedia is very strict about automated requests. If you do not follow the rules, your IP may be temporarily or permanently blocked. This is why it is important to understand how to avoid IP-based blocks.
Here is what you should pay attention to:
Example of a proper User-Agent:
headers = {"User-Agent": "MyWikipediaParser/1.2 (https://example.com/myproject; contact@example.com)"}All of these requirements and recommendations for bot usage follow directly from Wikimedia policy.
As you may have noticed, the official Wikipedia API is not well suited for large-scale scraping — you need to wait 5–10 seconds between requests, and increasing the number of parallel threads is not allowed. This is more of a pet-project level solution, nothing beyond that.
To work around these limitations, there are several possible approaches:
When manual scraping really makes sense:
Scrape Google, Bing, and others — fast, stable, and convenient with SERP scraper.
This Wikipedia parsing script takes a search query (a term), finds the most relevant page through Wikipedia search, extracts the first paragraph (the definition), and stores the result in a DataFrame.
import requests
from bs4 import BeautifulSoup
import pandas as pd
def get_wikipedia_intro(query):
# 1. Build the search URL
search_url = "https://en.wikipedia.org/w/index.php"
params = {
"search": query
}
response = requests.get(search_url, params=params)
soup = BeautifulSoup(response.text, "html.parser")
# 2. Try to find the first article link
result = soup.select_one(".mw-search-result-heading a")
if result:
article_url = "https://en.wikipedia.org" + result.get("href")
else:
# If the search immediately opened an article page (redirect)
article_url = response.url
# 3. Load the article page
article_response = requests.get(article_url)
article_soup = BeautifulSoup(article_response.text, "html.parser")
# 4. Find the first paragraph
paragraphs = article_soup.select("p")
intro_text = None
for p in paragraphs:
text = p.get_text(strip=True)
if text:
intro_text = text
break
return {
"query": query,
"url": article_url,
"intro": intro_text
}
if __name__ == "__main__":
query = input("Enter a term: ")
data = get_wikipedia_intro(query)
df = pd.DataFrame([data])
print(df)
# save the DataFrame if needed
df.to_csv("wikipedia_intro.csv", index=False)Of course, writing a parser from scratch is always more difficult. Wikipedia encourages open-source development and even promotes a wide range of ready-made libraries and automation tools on its pages. There is even a special community-maintained tool directory called Toolhub.
Do not forget to install the required libraries first: pip install Wikipedia-API pandas.
import requests
import wikipediaapi
import pandas as pd
def create_wiki_client(lang="en"):
"""
Initialize the Wikipedia API client
"""
return wikipediaapi.Wikipedia(
language=lang,
extract_format=wikipediaapi.ExtractFormat.WIKI,
user_agent="my-wiki-parser/1.0 (example@mail.com)" # Be sure to replace this with your own details!
)
def search_wikipedia(query, lang="en"):
"""
Search for a relevant article through the Wikipedia API
"""
url = f"https://{lang}.wikipedia.org/w/api.php"
params = {
"action": "query",
"list": "search",
"srsearch": query,
"format": "json"
}
try:
response = requests.get(url, params=params, timeout=10)
response.raise_for_status()
data = response.json()
results = data.get("query", {}).get("search", [])
if results:
return results[0]["title"]
except Exception as e:
print(f"[Search error]: {e}")
return None
def get_wikipedia_intro(query, wiki, lang="en"):
"""
Main function:
- searches for an article
- retrieves the page
- extracts the first paragraph
"""
title = search_wikipedia(query, lang)
if not title:
return {
"query": query,
"title": None,
"url": None,
"intro": None,
"status": "not_found"
}
page = wiki.page(title)
if not page.exists():
return {
"query": query,
"title": title,
"url": None,
"intro": None,
"status": "page_not_exists"
}
intro = page.summary.split('\n')[0]
return {
"query": query,
"title": page.title,
"url": page.fullurl,
"intro": intro,
"status": "ok"
}
if __name__ == "__main__":
query = input("Enter the term: ").strip()
# you can change the language: en / ru / de
lang = "en"
# 1. create the client
wiki = create_wiki_client(lang)
# 2. retrieve the data
result = get_wikipedia_intro(query, wiki, lang)
# 3. output
df = pd.DataFrame([result])
print(df)
# 4. save
df.to_csv("wikipedia_result.csv", index=False)Unlike direct HTML parsing, this approach uses the official API, which makes the code more stable and easier to maintain.
Let us first look at the advantages and disadvantages of each approach.
The official Wikipedia API provides maximum stability and predictability for your parser. You can plan the load in advance and run almost any task with minimal coding effort. Data can be collected immediately in the required format, without additional processing. And if you have an active corporate subscription to the Wikimedia Enterprise API, you do not have to worry about rate limits at all. As long as you follow the platform’s recommendations, the risk of being blocked remains minimal. In practice, the Wikipedia API covers about 90% of all possible tasks. If you do not need to modify encyclopedia pages or publish new content, the API can work perfectly well without authorization.
Direct Wikipedia scraping involves a number of risks. If your request rate is too high, you may receive a temporary or permanent IP ban. In addition, page layouts may occasionally change, which means the scraper may need to be partially rewritten. The scraper itself must be developed and maintained, and you also need to monitor its operation on your own infrastructure. However, if you use a system of proxy servers, you can bypass potential bans and request limits across the Wikimedia ecosystem. Another advantage is that you can collect absolutely any data, including information that cannot be accessed through the API.
So, which option should you choose?
If you have a pet project and need to collect a relatively small amount of information on a specific topic, the official Wikipedia API is the more sensible choice. There are ready-made libraries and detailed documentation for working with the encyclopedia and free media repositories. Building a parser in this case will require very little time and effort.
But if you need to collect large volumes of data in a short period of time, or if you need to extract special or non-standard content from pages, then you will have to build your own scraper. It will not work reliably without high-quality rotating proxies.
Wikipedia does indeed support the open distribution of information. All services within the Wikimedia ecosystem have official API interfaces, and not just one, but several, designed for different tasks and purposes. To quickly and smoothly collect a small amount of data, as well as receive update information, Wikipedia offers a wide range of ready-made tools and libraries. In addition, there are many technical solutions created by third-party developers.
However, when it comes to large-scale scraping, the Wikipedia API may not always be suitable, since the interface has significant request-rate limits and is not appropriate for every type of task. For this reason, it may become necessary to build a custom scraper that works independently of the API.
No large-scale scraper can operate reliably without high-quality proxies. Froxy offers a huge pool of residential, mobile, and datacenter proxies with automatic rotation. With them, you do not have to worry about blocks and can run hundreds of parallel data-collection threads at the same time.