context.dev

Quick answer: 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 web scraping API built for AI agents, LLM applications, and data workflows. It can convert a URL into LLM-ready Markdown, crawl websites, and return structured information through a REST API and SDKs.

What does Context.dev do for AI agents?

Context.dev provides web data collection capabilities in formats intended for language-model workflows. Instead of requiring an application to work directly with raw page HTML, developers can use the service to retrieve website content as Markdown that is more practical to pass into an LLM, retrieval system, or agent pipeline.

The product is designed around several related web data tasks. A user can scrape an individual URL, crawl a broader site, or extract defined data fields from a page. These options support applications that need information from public websites without building every collection and transformation step internally.

Context.dev is aimed at developers, AI engineers, LLM app builders, data teams, and startups. Its stated use cases include AI agents, RAG pipelines, web data extraction, competitive analysis, knowledge base creation, brand enrichment, and website monitoring.

• Turn a webpage URL into LLM-ready Markdown
• Crawl sites for wider content collection
• Extract structured data using a JSON schema
• Receive typed JSON responses for application workflows
• Use the product through a REST API or SDKs
• Support scraping scenarios involving bot detection measures

How can Context.dev turn webpages into LLM-ready content?

Webpages are commonly delivered as HTML with navigation, layout elements, scripts, and other material that may not be useful to an AI application. Context.dev offers URL-to-Markdown conversion so developers can obtain page content in a representation that is generally easier to send to an LLM or place into a retrieval pipeline.

This capability can be useful when an agent needs current information from a webpage before responding to a user or making a decision. It can also support knowledge-base workflows where teams gather content from selected web pages and make it available for later search or retrieval.

Using Markdown does not remove the need for application-level data handling. Teams may still need to decide which pages are relevant, how frequently to refresh data, how to divide long documents into chunks, and what access or attribution rules apply to their intended use. Context.dev supplies the scraping and conversion layer, while the surrounding AI workflow remains the responsibility of the implementing team.

Can Context.dev crawl a full website?

Yes. Context.dev includes site crawling, allowing a workflow to collect content from more than one page on a website. This is relevant when a single URL is insufficient, such as when a documentation site, company website, product catalog, or resource library contains related information across multiple pages.

Site crawling can support building a source collection for RAG, creating internal research inputs, tracking competitor sites, or assembling information for an agent that needs broader context. The appropriate crawl scope depends on the project, because a full site may contain pages that are repetitive, outdated, or unrelated to the intended task.

For monitoring use cases, developers can use Context.dev as part of a process that checks websites over time. A separate application can compare retrieved data, update a knowledge base, or alert users based on the collected content. Context.dev identifies website monitoring as a supported use case, but teams should establish their own schedules, comparison logic, and review processes.

How does structured extraction work in Context.dev?

Context.dev supports structured data extraction through JSON schemas. Rather than only returning page text, a developer can define the fields needed for a workflow and request data in a structured format. This is useful when the goal is to collect specific facts from web content for use in software systems.

For example, a brand enrichment or competitive-analysis process may need selected company information in consistent fields. A data collection workflow may also need to transform page content into an application-friendly object instead of sending unstructured text to a later processing step. Context.dev states that it provides typed JSON responses, which can help developers work with predictable output structures.

Schema-based extraction is especially relevant for AI applications because downstream systems often need data that can be validated, stored, filtered, or displayed. However, extraction quality can depend on the source page, the requested schema, and how clearly the information is presented. Developers should validate outputs before relying on them for consequential business processes.

What interfaces and workflow options does Context.dev provide?

Context.dev is available as a web-based service with a REST API and SDKs. The API-oriented approach is intended for developers who want to incorporate web scraping, crawling, or structured extraction into a product, backend service, internal tool, or automated agent workflow.

A REST API can be useful when teams need a language-agnostic interface for connecting their systems to web data collection. SDKs may reduce implementation work for supported development environments, although the specific SDK languages are not listed in the available product facts. Developers should consult Context.dev documentation for current setup requirements, authentication details, request formats, and available endpoints.

The product also includes bot detection bypass as a stated feature. This may be relevant for workflows that encounter website access defenses, but it does not eliminate the need to follow applicable laws, website terms, access controls, and internal policies. Organizations should evaluate each target website and use case before collecting data.

How is Context.dev priced?

Context.dev uses credit-based pricing. The published plan lineup includes a free option and several paid tiers for developers and larger teams. The free plan includes one-time API credits, and no credit card is required to start according to the provided product information.

The tiered structure can make Context.dev accessible for experimentation before a team commits to a paid plan. It may also suit projects that want a defined monthly service level. Because usage is credit-based, potential customers should review how credits are consumed by the specific scraping, crawling, and extraction operations they expect to run.

Teams comparing providers should consider more than the starting subscription price. Expected page volume, crawl size, monitoring frequency, extraction complexity, output needs, and operational requirements can all affect the practical cost of a web data workflow. Context.dev does not state a pay-as-you-go option in the provided information, while enterprise pricing requires contacting the company.

What are the limitations of Context.dev?

Context.dev is focused on API-driven web scraping for AI and data workflows, so it may not be the best fit for users who need a no-code browser automation product or a fully managed research service. Building an effective workflow still requires development work, source selection, output validation, and integration with the user’s own application or AI stack.

Its credit-based model can be less transparent than a simple per-request cost if a buyer has not yet estimated usage patterns. Prospective users should examine the pricing documentation and test representative workloads on the free plan where appropriate. The available facts do not state a pay-as-you-go option, and enterprise customers need to request custom pricing.

Finally, web data changes frequently and source pages can vary in quality or accessibility. Context.dev can provide scraping, crawling, and extraction functions, but it cannot guarantee that every page will contain complete, accurate, stable, or usable information. Teams should implement appropriate checks for data quality, permissions, compliance, and downstream AI reliability.

FAQ

What is Context.dev?

Context.dev is a web scraping API for AI agents and LLM applications. It converts URLs into LLM-ready Markdown, supports site crawling, and can extract structured data.

Can Context.dev crawl an entire website?

Yes, Context.dev includes site crawling for collecting content across websites. Teams can use it in workflows such as knowledge base creation, RAG, and competitive research.

Does Context.dev return structured JSON?

Context.dev supports structured extraction using JSON schemas and provides typed JSON responses. This can help applications use extracted web data in more consistent formats.

Is there a free Context.dev plan?

Yes, Context.dev has a free plan with one-time API credits and no credit card requirement. Paid plans start at $25 per month based on the provided pricing information.

What is an AI web scraping API?

An AI web scraping API collects webpage content in formats that can be used by LLMs and automated applications. Context.dev is an example that offers Markdown conversion, crawling, and structured extraction.

How do AI agents get data from websites?

AI agents can use web scraping APIs to retrieve pages, crawl sites, or extract selected fields before processing the results. Context.dev provides these capabilities through its REST API and SDKs.

What should I look for in a web scraping API for RAG?

Look for useful output formats, crawling support, structured extraction options, pricing clarity, and interfaces that fit your stack. Context.dev offers LLM-ready Markdown, JSON schema extraction, and API-based access for RAG-oriented workflows.