One API, Any Website: Inside Context.dev’s Web Scraping Platform

Context.dev turns websites into clean Markdown, rendered HTML, images, screenshots, and structured JSON—giving developers a way to connect AI products to the live web without building an entire scraping stack themselves.

Large language models know an enormous amount about the world, but their knowledge is neither complete nor permanently current.

A model may understand how to compare software products, summarize a company, or answer questions about an industry. It cannot automatically know that a pricing page changed yesterday, a business launched a new product this morning, or documentation was updated an hour ago.

Developers can solve this problem by connecting their applications to the web. Doing so reliably, however, is much more complicated than sending a request to a URL.

Modern websites render content with JavaScript, distribute information across hundreds of pages, load assets dynamically, and use bot-detection systems to restrict automated access. Even after the page is retrieved, developers must remove navigation and advertising, convert the content into a format an AI model can understand, and maintain the scraper when the website changes.

Context.dev packages this work into a Web Scraping API designed for software products, data workflows, retrieval systems, and AI agents.

A developer provides a URL or domain and selects the required output. Context.dev handles page retrieval and can return clean Markdown, raw or rendered HTML, images, screenshots, sitemaps, multi-page crawl results, or data extracted into a developer-defined schema.

The company’s central proposition is straightforward: product teams should be able to use the live web as a data source without becoming web-scraping infrastructure companies.

A basic web scraper can be written relatively quickly.

The program sends an HTTP request, downloads the HTML, and parses the elements containing the required information. This works well when the target is a small, stable, publicly accessible website.

Production scraping is different.

The same system may need to handle:

Every target website introduces its own behavior. A parser built for one layout may stop working after a redesign. A static request may return an almost empty page because the real content loads in the browser. A previously accessible endpoint may begin returning a challenge page.

Maintaining these systems consumes engineering time even though scraping infrastructure may not be the feature customers are paying for.

Context.dev turns this maintenance burden into a hosted API. The developer defines what information the application needs, while Context.dev manages page retrieval, JavaScript rendering, proxy escalation, anti-bot handling, conversion, and extraction.

This tradeoff replaces infrastructure ownership with a metered service. Teams give up some low-level control in exchange for faster integration and less maintenance.

“Scraping a website” can mean different things depending on the application.

An archival tool may need the complete HTML. A retrieval-augmented generation system needs clean text. A design platform may care about images and fonts. A market-intelligence product may need a precise set of fields returned as JSON.

Context.dev offers different endpoints for these requirements rather than forcing every developer to process the same raw response.

The Markdown endpoint turns a web page into text organized for people and language models.

Raw HTML contains tags, scripts, style information, navigation menus, tracking code, and other material that adds little value to an AI system. Sending all of it into a model wastes context-window capacity and increases token costs.

Markdown preserves useful semantic structure—headings, paragraphs, lists, tables, and links—while removing much of the surrounding page machinery.

Context.dev also provides a useMainContentOnly option that removes elements such as headers, footers, sidebars, and navigation. This can make the result more suitable for knowledge bases, research tools, and RAG pipelines.

A support chatbot, for example, could crawl a company’s documentation and store the Markdown in a vector database. When a user asks a question, the system retrieves the relevant sections and gives them to the language model as current context.

This allows the model to answer from the latest website content rather than relying exclusively on knowledge learned during training.

Markdown is useful for language understanding, but some applications require the original page structure.

Context.dev’s HTML scraping endpoint returns the HTML content of a specified URL. Developers can use this when they need to inspect DOM elements, run their own parsers, preserve structured attributes, or identify information that might be lost during conversion.

The endpoint can use browser rendering for JavaScript-heavy websites and automatically escalate proxy capabilities when a site blocks a simpler request.

Context.dev’s HTML scraping documentation says the same endpoint can handle open and more heavily protected pages without requiring customers to configure a separate proxy system.

This does not mean every website is guaranteed to be accessible. Anti-bot systems evolve continuously, and authenticated or tightly restricted pages present different legal and technical constraints. The value lies in moving most of that complexity behind a consistent interface.

A single URL is often insufficient for applications that need broad company or product context.

Context.dev’s Website Crawler API begins with one page, follows links within the same domain, and converts each discovered page into Markdown. Developers can control how many pages are visited, how deep the crawler follows links, whether subdomains are included, and which URLs should match a regular-expression filter.

The crawl response includes metadata such as page URLs, titles, status codes, crawl depth, and the number of successful or failed requests.

This is useful for:

The crawler currently supports up to 500 pages in one operation, according to the company’s changelog. Each successfully crawled page consumes one credit.

Context.dev also offers a time-budget option. A developer can instruct the crawler to stop after a defined period and return whatever it has collected rather than allowing a large crawl to continue indefinitely.

Sometimes an application only needs to know which pages exist.

The Sitemap endpoint reads a site’s XML sitemap files, follows sitemap indexes, removes duplicates, and returns the discovered URLs. It can return a much larger URL inventory than a normal crawl without rendering and extracting the content of every page.

The distinction matters:

A developer might first retrieve the sitemap, filter it for product or documentation pages, and then send only those URLs to the scraping endpoints.

This can reduce unnecessary requests and give the application more control over its data-collection strategy.

Text is only one part of the web.

AI design platforms, creative tools, media databases, and multimodal applications may need images from a page. Extracting them reliably requires more than reading standard <img> tags. Websites may use picture elements, CSS backgrounds, inline SVG files, video posters, or base64-encoded data.

Context.dev’s image endpoint collects assets from these different sources and returns them in a structured array. Optional enrichment can add dimensions, file types, hosted URLs, and classifications such as photography, illustration, logo, wordmark, icon, pattern, or graphic.

The company also provides separate endpoints for logos, colors, fonts, and full style guides. This reflects Context.dev’s earlier identity as Brand.dev, a brand-data API. The company rebranded in March 2026 as its product expanded from brand extraction into broader web context.

A marketing application could consequently retrieve the text describing a company, its logo, its visual style, and relevant website images through the same provider.

Some tasks require a visual representation of the page rather than its underlying content.

Context.dev can generate viewport or full-page screenshots with configurable dimensions. Its screenshot tools can also attempt to locate common page types such as pricing, login, signup, careers, contact, privacy, or terms pages.

Screenshots can support:

A screenshot costs more credits than a standard page scrape because it requires browser rendering and image generation.

The most important layer for many AI products is structured extraction.

Raw HTML gives the developer everything. Markdown gives the model cleaner text. Structured extraction attempts to return only the facts the application needs.

A developer can define a JSON schema containing fields such as a company name, pricing tiers, plan descriptions, currencies, billing frequencies, or product features. Context.dev crawls the relevant site and returns information matching that schema.

This reduces the need to build separate CSS selectors for every website.

A traditional scraper assumes the same piece of information appears in a predictable HTML element. Schema-based extraction describes the desired meaning instead. The system can then interpret different layouts and map their content into a consistent response.

This is particularly valuable when an application needs to process many unrelated websites. One pricing page may use a table, another may use cards, and a third may reveal prices inside an accordion. A semantic extractor can attempt to normalize all three into the same data structure.

Potential applications include:

Structured extraction is also one of the areas where verification matters most. AI-based extraction can misinterpret ambiguous information, combine incompatible pricing tiers, or return a plausible field unsupported by the page.

Developers should validate critical outputs, retain source URLs, and design their schemas to allow missing or uncertain values rather than assuming every response is authoritative.

Context.dev is positioning its scraping infrastructure specifically for AI agents.

An agent can call the API when it needs current information, convert relevant pages into Markdown, and insert the result into its working context. It can also request structured fields instead of reasoning over an entire page.

This creates a simple research loop:

This helps address a fundamental limitation of language models: their internal knowledge is not a live database.

A sales agent could research a prospective customer immediately before drafting an email. A support agent could retrieve recently updated documentation. A financial application could inspect current company information. A marketing system could build a brand profile during customer onboarding.

Context.dev provides an MCP integration for compatible AI assistants alongside TypeScript, Python, Ruby, Go, and PHP software development kits. Zapier and Make integrations support workflows that do not require a custom application.

Context.dev combines web scraping with brand intelligence and entity enrichment.

The same account can retrieve page content, identify company logos, extract style guides, classify businesses using NAICS or SIC codes, identify merchants from transaction strings, and collect product information.

This unified model can reduce the number of vendors an application needs to coordinate.

For example, an AI marketing platform could:

Context.dev’s customer page says GooseWorks uses its Brand, Styleguide, and Markdown APIs in parallel to build agent-ready brand kits. SiteGPT reportedly moved from Firecrawl to Context.dev to turn entire websites into customer-support knowledge bases, while Similarweb uses its scraping and brand APIs to supply data points to AI agents.

These are vendor-published customer accounts rather than independent performance benchmarks, but they demonstrate how the Web Scraping API fits into working products.

Another case study describes how ION replaced an internally maintained brand-scraping system with several Context.dev endpoints in an afternoon. The reported benefit was not only better extraction; it was removing a fragile side system from the company’s core codebase.

That captures the economic argument behind scraping APIs: the cost is not simply the server request. It is the engineering time spent fixing scrapers when websites change.

Context.dev uses credit-based pricing.

Its free tier includes a one-time allocation of up to 500 credits without requiring a card. According to the website’s FAQ, users registering with a free email provider may receive 250 credits with a lower rate limit, while work-email registrations receive the full 500.

The Developer plan starts at $25 per month and includes 10,000 credits. The Pro plan starts at $149 per month with 200,000 credits. Larger Scale and Enterprise configurations are available for higher volumes.

A standard HTML or Markdown scrape generally costs one credit. A multi-page crawl costs one credit for each successfully retrieved page. Requests involving browser actions may cost two credits. Screenshots cost five, while schema-based structured extraction costs ten.

The company says failed or blocked requests are not billed.

One of Context.dev’s pricing differentiators is the absence of separate multipliers for JavaScript rendering, premium proxies, or anti-bot handling during standard scrapes. Some competitors calculate those capabilities as several times the base request cost, making the final bill less predictable.

Businesses should still model their expected traffic carefully. A product that repeatedly crawls large sites or performs structured extraction for every user action can consume credits quickly. Caching and selective scraping remain important even when the infrastructure is managed externally.

Context.dev was founded in 2025 by Yahia Bakour and is part of Y Combinator’s Summer 2026 batch.

Its Y Combinator profile describes the company as an infrastructure provider that gives AI agents structured, real-time web data. YC says Context.dev powers products at more than 280 companies, including Mintlify, Super, Vizzy, and Klarna.

Bakour previously worked in software engineering at Sunrun and Amazon and co-founded StockAlarm.io, which was later acquired.

Context.dev reports that it has completed SOC 2 Type I certification, with its Type II observation period still in progress. Companies handling sensitive data should inspect the current Trust Center and data-processing terms rather than treating a scraping API as a purely technical integration.

A managed API removes technical complexity. It does not remove the developer’s responsibility for how data is collected and used.

Public accessibility does not automatically grant unlimited permission to copy, store, republish, or commercialize information. Website terms may restrict automated access. Personal data can trigger GDPR, CCPA, and other privacy obligations. Copyright rules remain relevant even when content can be retrieved programmatically.

Context.dev itself advises customers to review target-site terms, respect robots.txt where applicable, observe rate limits, and avoid scraping authenticated or sensitive information without appropriate authorization.

Developers should also prefer an official API when it provides the required data under clear terms.

The ability to rotate proxies or bypass common bot protections should not be treated as permission to evade access controls. Legal review becomes particularly important when a product collects data at scale, processes personal information, or depends commercially on a source that explicitly prohibits scraping.

Responsible implementations should minimize requests, cache results, preserve source attribution, support deletion requirements, and avoid collecting information that the product does not genuinely need.

Context.dev can make websites easier to access and interpret, but it cannot guarantee that the websites themselves are accurate.

A company may leave an old pricing page online. A product description may be promotional rather than factual. A page can contain conflicting information, and an extraction model can misunderstand it.

AI agents using web context should therefore distinguish retrieval from verification.

For high-stakes applications, developers may need to compare multiple sources, record retrieval dates, expose citations, and require human review before acting on extracted information.

Scraping APIs solve the engineering problem of obtaining web content. They do not automatically solve the epistemic problem of deciding whether that content should be trusted.

The web was designed primarily for people reading pages, not for applications requesting standardized facts.

Context.dev is trying to bridge that gap.

Its Web Scraping API converts the messy variety of websites into outputs software can use: HTML for custom parsing, Markdown for language models, images for multimodal applications, screenshots for visual workflows, crawls for knowledge bases, and structured JSON for databases and automation.

The platform will not replace every custom scraper. Teams with a small number of stable targets, specialized extraction rules, and experienced infrastructure engineers may prefer the control and economics of an internal system.

For AI startups and product teams that need broad, changing web coverage, however, maintaining proxies, browsers, parsers, and anti-bot systems can become an expensive distraction.

Context.dev’s bet is that web context will become a foundational layer of AI software—and that most teams will prefer to access that layer through an API rather than build it from the ground up.