Context.dev: Web Scraping API for AI Agents & LLMs

Quick answer: Context.dev: Web Scraping API for AI Agents & LLMs is context.dev is a web scraping API for AI agents: scrape any URL into LLM-ready Markdown, crawl entire sites, and extract structured data with one REST API.

Listed 2026-08-28 · Request removal

Definition: Context.dev is a developer-oriented web scraping and extraction API for AI agents and LLM applications. It converts URLs and domains into formats such as Markdown, HTML, screenshots, structured JSON, and crawl data, helping software systems retrieve and prepare information from the web.

What is Context.dev used for?

Context.dev is designed for applications that need reliable web content as an input. Rather than building separate systems for page fetching, browser rendering, extraction, and crawling, development teams can use its REST API to retrieve website data through a single service.

A common use is gathering source material for retrieval-augmented generation workflows. In this setup, an application can pull content from selected pages or domains, convert it into LLM-ready Markdown, and pass the resulting text into indexing, embedding, retrieval, or agent pipelines. This can help teams work with web pages that are not already available in clean text form.

Context.dev also supports web research automation, site crawling, structured data extraction, and brand or domain intelligence lookups. Its audience includes AI engineers, LLM application developers, data extraction teams, RAG pipeline builders, and growth or research teams that need programmatic access to web information.

• LLM and RAG data ingestion from URLs or domains
• Web context retrieval for AI agents
• Multi-page website crawling
• Schema-based JSON extraction
• Automated web research workflows
• Brand and domain profile retrieval

How does Context.dev scrape and transform web pages?

Context.dev can scrape an individual URL and return its contents as Markdown or HTML. Markdown is particularly useful when a downstream language model, retrieval system, or text-processing workflow needs content with less page-layout noise than a full browser document. HTML remains an option for workflows that need markup or want to perform their own downstream parsing.

The platform also provides screenshot capture, which can be useful when visual page state matters alongside textual output. For sites that depend on client-side JavaScript, Context.dev offers browser rendering capabilities. It also lists automatic proxy and bot-challenge handling as part of its web access tooling.

These functions are relevant because a basic HTTP request does not always return the information a visitor sees in a browser. Modern websites may populate content after scripts run, apply access controls, or present challenges to automated traffic. Context.dev positions its rendering, proxy, and challenge-handling features as tools for managing those conditions through the same API-oriented workflow.

In practice, developers can decide whether a task needs a one-page scrape, a visual capture, browser-rendered content, or a broader crawl. The right selection depends on the website, the required output format, and the data pipeline that will consume the result.

Can Context.dev crawl websites and extract structured data?

Yes. Context.dev supports crawling entire websites, allowing applications to collect content from more than one page within a domain. Crawling is useful when a project needs a larger body of material, such as documentation, product catalogs, help centers, research sites, or company pages, instead of information from a single URL.

The service can also extract structured JSON from a supplied schema. This is useful when a workflow needs defined fields rather than raw page text. For example, a team may want consistent information from many pages for use in an internal database, research process, or agent task. Structured extraction can make downstream processing more predictable because the application can request an organized JSON result instead of trying to identify fields after collection.

Context.dev additionally includes web search with optional scraping. This can support a workflow in which software first discovers relevant web pages and then retrieves page contents for later analysis. Brand and domain profile retrieval adds another option for applications that need company or website-related intelligence.

Which developers and platforms can use Context.dev?

Context.dev is delivered as a web API. Teams can integrate it through REST requests or use available software development kits for TypeScript, Python, Ruby, PHP, and Go. The listed package and distribution options include npm, PyPI, RubyGems, Packagist, and a GitHub Go SDK.

This range is useful for teams whose AI infrastructure is distributed across different services or programming languages. For example, an application backend may use TypeScript while a data preparation job uses Python. A REST interface can also be suitable for environments where a team prefers direct HTTP integration over an SDK.

The product is primarily aimed at technical users. Developers will generally need to determine which endpoint and output type fit their task, manage API credentials, design extraction schemas when structured results are needed, and connect returned data to their own storage, agent, or retrieval systems. Context.dev is not presented as a desktop or mobile application for manual browsing and scraping.

What does Context.dev cost?

Context.dev offers a free account, while its pricing page indicates that usage is metered with API credits. Paid plans listed for the service are organized around monthly credit allocations, so the practical cost depends on the volume and types of API operations a team performs.

Before selecting a plan, teams should estimate expected usage across page scraping, crawling, structured extraction, browser-rendered requests, searches, and any other operations included in their workflow. Credit-based pricing can suit projects that need flexible, API-driven usage, but costs may rise for high-volume deployments. Reviewing current pricing and credit rules directly with Context.dev is important before committing to an implementation.

How does Context.dev fit into an AI agent or RAG workflow?

Context.dev can serve as the web acquisition layer in an AI system. An application can send a URL or domain to the API, receive content in Markdown, HTML, JSON, screenshot, or crawl-oriented formats, and then route that result to its own processing stack. The next stages may include cleaning, chunking, embedding, indexing, ranking, storage, or model prompting.

For an AI agent, this means the agent can request web context as part of a task rather than relying only on static training data or manually curated documents. For a RAG application, the API can help turn website content into material that is ready for retrieval. Schema-based extraction may be more appropriate when the application needs specific fields, while Markdown can be useful for document-style ingestion.

Context.dev combines scrape, crawl, search, extraction, and browser-related features under one API. That consolidated approach may reduce the number of separate web data services a development team needs to connect. However, teams remain responsible for choosing appropriate sources, evaluating returned content, maintaining their own data pipelines, and complying with applicable website terms and legal requirements.

What are the limitations of Context.dev?

Context.dev is primarily a developer product, so it may not be the best fit for users seeking a no-code desktop tool or a mobile app. Successful use generally requires API integration and technical decisions about output formats, crawl scope, schemas, and downstream data handling.

Its metered credit model can also require close usage monitoring, especially for larger crawling or high-volume extraction workloads. Features such as rendering, crawling, and structured extraction should be assessed against expected credit consumption and operational needs. As with any web scraping API, accessibility of individual sites can vary, and teams should verify that their intended usage follows relevant rules and requirements.

FAQ

What is Context.dev?

Context.dev is a web scraping and extraction API for AI agents and LLM applications. It can turn URLs and domains into Markdown, HTML, screenshots, structured JSON, and crawl data.

Can Context.dev scrape JavaScript-heavy websites?

Context.dev lists stealth browser rendering along with automatic proxy and bot-challenge handling. These features are intended to support scraping workflows where basic page requests may not be sufficient.

Does Context.dev support structured JSON extraction?

Yes. Context.dev can extract structured JSON using a schema, which can help applications collect consistent fields from web pages.

Which programming languages have Context.dev SDKs?

Context.dev provides SDK options for TypeScript, Python, Ruby, PHP, and Go, in addition to its REST API. Package options include npm, PyPI, RubyGems, Packagist, and a GitHub Go SDK.

What is a web scraping API for AI agents?

A web scraping API for AI agents lets software retrieve and convert website information into formats an AI workflow can process. Context.dev is one example, with outputs including Markdown, HTML, JSON, screenshots, and crawl data.

How do RAG applications use web scraping APIs?

RAG systems can use a web scraping API to collect web content, then clean, chunk, index, and retrieve it during model interactions. Context.dev can provide LLM-ready Markdown and other formats for that ingestion process.

What should I look for in a web scraping API?

Consider output formats, crawling support, JavaScript rendering, structured extraction, SDK availability, pricing, and website-access requirements. Context.dev includes scrape, crawl, search, schema-based extraction, and multiple SDK options in one API.