Wikipedia

open

Access normalized article JSON for 6M+ English entries with consistent metadata, sections, and usage telemetry.

6M+ English articles
Updated daily
Open dataset

Use a single URL prefix to fetch canonical articles or experiments.

Ideal for grounding general knowledge agents and evaluation harnesses.

Clean JSON output reduces time spent building brittle scrapers.

API Endpoints

Available

Coming Soon

GET/search
Search by keyword
GET/batch
Batch fetch
GET/{source_url}

Fetch by URL

Fetch a single article from Wikipedia by providing its canonical URL.

Example

https://alpha.projectdatax.com/https://en.wikipedia.org/wiki/Machine_learning

Response fields

idsource_urltitlesummarybodysectionsreferencesmetadatausage

Code snippet

curl "https://alpha.projectdatax.com/https://en.wikipedia.org/wiki/Machine_learning"

Try in browser

Enter a Wikipedia article title or URL and click "Run request" to see results.

Rate limits

Account state determines throughput. Wikipedia follows global open-data guidance.

Account typePer-day requestsNotes
Anonymous50/dayNo signup required
Free1,000/dayVerified email, open datasets
DeveloperCustomHigher limits, premium sources
ScaleCustomDedicated support
Create free account

Use cases

Build with confidence using templates tuned for publishers.

RAG chatbot

Ground LLM responses in high-coverage reference articles and keep responses citation-friendly.

Evaluation & benchmarking

Generate consistent evaluation sets and enrich with trustable metadata for agent feedback loops.

Knowledge enrichment

Normalize Wikipedia info into your knowledge graph or context windows without writing custom scrapers.