The site publishes the speed it wants: one request every five seconds. A source that tells you that deserves a collector that listens.
Plans from €169/month · Free project assessment · Reply within 1 business day
ScrapeIt runs the collection as a managed service. You name the libraries, subjects or books; we build the pipeline keeping the book, chapter and section hierarchy, record licence and contributors per page, and hand back CSV, JSON, Excel or a push into your warehouse.
Collection is paced, which makes large jobs slow and reliable rather than fast and fragile. We quote the realistic duration up front instead of discovering it during delivery.
We collect only publicly available data - everything a visitor can see on LibreTexts - and we collect it legally. Section text, hierarchy path, contributors and licence are recorded per page, exactly as each page shows them.
Records carry the library or subject, book title, chapter, section title, the hierarchy path, the section text, and the contributors and licence as published on the page.
The hierarchy path is kept as a field rather than reconstructed from breadcrumbs afterwards. It is what allows a section to be placed in its book and chapter without guesswork, and it is free to capture at collection time and expensive to recover later.
Licence is recorded per page rather than assumed for the project. Open licences on this platform are common and they vary between books and sometimes within them, so the licence is attached to the material rather than stated once in a covering note.
Contributors are collected as credited on the page, so every section keeps the authors and adapters it names next to its text.
Every row carries the collection timestamp and the source address.
Coverage mapping across libraries answers which topics are well served by open material and which are thin, which for anyone building or funding open education is the question that directs the next investment.
Course alignment is the most practical institutional use: matching an existing syllabus against available open sections to find what could replace a commercial textbook and what has no open equivalent yet. It works only with the hierarchy intact.
Change tracking over time shows which books are actively maintained and which have gone quiet, which matters to anyone planning to depend on a text.
Licence and contributors are recorded with every section, so a selection by library, book, chapter or licence comes straight out of the export rather than from a second pass.
LibreTexts is an open education project publishing free textbooks across chemistry, biology, mathematics, physics, engineering, social sciences and more, each subject on its own library. The material is openly licensed and written or adapted by academic contributors.
Structurally it is a nested hierarchy rather than a list of documents. A library contains books, a book contains chapters, a chapter contains sections, and the text lives at the section level. Flattened into a list of pages, the material loses the structure that makes it a textbook rather than an archive of articles - and the structure is often exactly what a client needs, because course alignment and coverage analysis are questions about the hierarchy.
Each section page shows its place in the book and chapter, the section text, and the contributors and licence as published on the page, so a complete record comes from the page itself.
Content pages respond directly with substantial text, and a sitemap is published per library.
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Delivery: CSV, JSON or XLSX files, our API or a JSON feed. MCP server on request.
Full plan details →Open educational content is one of the few sources in this field where the text itself is published rather than only described, and that changes what a good dataset looks like.
When the text is the payload, the licence and contributor credits shown on each section stop being metadata and become part of it. A section of text collected without them is incomplete, which makes a collection that dropped them misleading - it looks complete and is missing what the page itself displays.
The second point is the hierarchy. Coverage analysis, course alignment and gap finding are all questions about where material sits in a book rather than about the text in isolation. Keeping the path means those are queries; losing it means they are manual work.
The third is pacing. A collector that paces its requests runs indefinitely, and one that does not gets blocked and takes the project with it. Large collections here are therefore slow by design and we schedule them that way rather than promising speed we could not sustain.
The fourth is that this is academic material maintained by contributors, so it changes - corrections, revisions, new editions - and a client relying on specific text should re-collect rather than assume permanence.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
ScrapeIt is a managed extraction company, not a tool you have to learn. Our team builds the pipeline, follows the per-library sitemaps as books are added and revised, and repairs the collector when page templates change.
You see a sample first, in your format, over the subjects you actually need, with the hierarchy preserved and licences recorded so you can judge both structure and licence fields on real sections.
Slowly, deliberately. We pace requests, because a paced collector runs indefinitely and one that is not gets blocked and takes the project with it. We quote the real duration up front.
Because coverage analysis, course alignment and gap finding are all questions about where material sits in a book. With the hierarchy path those are queries; without it they are manual work, and the path is free to capture at collection and expensive to recover later.
Yes. Library, book, chapter, section text, contributors and licence are all shown to any visitor without logging in. We collect only publicly available data - everything a visitor can see on LibreTexts - and we collect it legally.
Because each section credits them on the page, next to the text. A dataset that drops the credits looks complete and is not, which is worse than a dataset that is obviously missing something.
Yes - corrections, revisions and new editions, since it is maintained by contributors. Anyone depending on specific text should re-collect rather than assume permanence, and change tracking also shows which books are actively maintained and which have gone quiet.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
LibreTexts data from €169/month. Free project assessment, reply within 1 business day.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
NIP: 5423457175
REGON: 523384582
