AI agents are becoming more capable of performing tasks independently. They can research topics, compare products, monitor websites, answer customer questions, analyze companies, and support business workflows. But there is one major limitation: an AI agent is only as useful as the information it can access.

Large language models have knowledge based on their training data, but that knowledge may not reflect what is currently published on the web. Websites change, prices are updated, products are launched, documentation is modified, and companies publish new information every day.

This creates a need for reliable ways to connect AI agents with live web information. Context.dev is designed to address this problem by providing APIs that turn web pages and company information into structured, AI-ready data.

What Is Context.dev?

Context.dev is a web data and context API platform designed for AI agents, applications, and LLM-powered products. Instead of requiring developers to build their own complete web-scraping infrastructure, the platform provides APIs for retrieving and processing information from websites.

Its capabilities include scraping webpages as Markdown or HTML, crawling websites, extracting images, discovering sitemaps, and extracting structured information from web content. It also provides company and brand-related data such as logos, colors, fonts, social profiles, and other information.

The basic idea is straightforward: give an AI system cleaner and more useful context from the live web so it can make better-informed decisions.

Why AI Agents Need Web Data

A traditional chatbot can answer questions using information available to its underlying model. An AI agent, however, often needs to perform actions based on information that changes frequently.

Imagine an AI sales assistant researching a potential customer. It might need to understand the company’s website, products, pricing, industry, and current messaging.

Similarly, a shopping assistant may need current product information, while a competitive-intelligence agent could need to monitor changes to competitor websites.

Without access to current information, the agent may rely on outdated knowledge or simply lack the information required to complete the task.

Context.dev provides an infrastructure layer that can retrieve this web information and make it easier for AI systems to consume.

Turning Websites Into AI-Ready Markdown

One of the most useful capabilities is converting webpages into clean Markdown.

Raw HTML contains much more than the actual information a person wants to read. A webpage can contain navigation elements, scripts, tracking code, styling information, advertisements, and other structural content.

Sending all of that directly to an AI model can waste context and make processing more complicated.

Context.dev’s Markdown endpoint is designed to return cleaner, semantically structured content that can be passed into an LLM. The company specifically positions this capability for RAG pipelines, knowledge bases, and AI agents.

For example, an agent could request a company’s pricing page, receive the page as Markdown, and then use that information when answering a user’s question.

Crawling Entire Websites

Sometimes one webpage is not enough.

An AI agent researching a company may need information from the homepage, product pages, documentation, pricing pages, and other sections of a website.

Context.dev provides a website crawling endpoint that can follow links within a domain and return pages as Markdown. This allows developers to gather information from multiple pages without building a complete crawler themselves.

This can be useful for building website-based knowledge systems. A company could, for example, crawl its documentation site and use the resulting content as a source for an AI support assistant.

Extracting Specific Information With AI

Sometimes an agent does not need an entire webpage. It needs a few specific fields.

For example, a developer might want to extract:

• Company name
• Industry
• Pricing model
• Free-trial availability
• Product categories
• Contact information
• Specific features

Context.dev provides an AI-powered website data extraction endpoint where developers define the information they want and receive extracted values from the website.

This is particularly useful when raw webpage content is too broad.

Instead of giving an AI model an entire website and asking it to find information itself, developers can request specific data points in a structured format.

Structured Data for AI Agents

Structured data is important because AI agents often need to do something with the information they receive.

A paragraph can be useful to a human, but software usually works better with predictable fields.

Context.dev’s extraction API allows developers to define a schema for the information they want. The response can then return data that follows that structure, along with the URLs analyzed.

For example, an application comparing competitors could define fields for product name, price, billing frequency, trial availability, and plan limitations.

The resulting information can then be stored in a database or passed to another part of the application.

This makes structured web extraction particularly valuable for automated workflows.

Helping AI Agents Research Competitors

Competitive intelligence is one practical use case.

Suppose a business wants an AI agent to monitor five competitors. The agent could retrieve relevant webpages from each company’s website and extract information about pricing, products, features, and messaging.

Instead of building separate scrapers for every competitor, developers can use a common API workflow.

Context.dev specifically describes competitor pricing and product monitoring as a use case for its extraction capabilities.

The resulting data can be compared over time, helping businesses identify changes that might otherwise be missed.

Supporting RAG Applications

Retrieval-augmented generation, or RAG, allows an AI model to use external information alongside its existing capabilities.

A basic RAG workflow might involve collecting documents, cleaning them, converting them into usable text, storing them in a searchable system, and retrieving relevant content when a user asks a question.

Context.dev can fit into the information-ingestion stage by providing web content in formats suitable for AI processing. Its platform specifically highlights Markdown, crawling, and extraction APIs for RAG systems.

This can help developers build systems that reference current website information instead of relying entirely on static model knowledge.

Images and Multimodal AI

Modern AI agents are not limited to text.

Vision-capable models can analyze images, screenshots, product photographs, diagrams, and other visual material.

Context.dev provides an image-scraping endpoint that can extract images from webpages, including different image formats and image sources.

This can be useful for multimodal applications.

For example, an AI shopping assistant could potentially use product-page text alongside product images when analyzing an item.

A website-monitoring system could also use screenshots or extracted visual assets as part of its analysis.

Company and Brand Context

Web data is not limited to page content.

An AI agent interacting with a company may also need to understand its identity. Context.dev provides APIs for company and brand information, including logos, colors, fonts, social links, descriptions, and industry classifications.

This can be useful for AI-powered onboarding, sales tools, dashboards, research applications, and automated reporting.

For example, an application could receive a customer’s domain and use the available company information to personalize an interface automatically.

Reducing the Need for Custom Scraping Infrastructure

Building a web-data pipeline from scratch can involve several components.

Developers may need a crawler, browser-rendering system, HTML parser, proxy infrastructure, extraction logic, validation processes, and monitoring.

Context.dev aims to consolidate many of these requirements into a unified API layer. Its current extraction documentation describes crawling, rendering, AI-based extraction, schema matching, and provenance information as part of the process.

This can allow development teams to spend more time building their AI application rather than maintaining scraping infrastructure.

Important Considerations

Although Context.dev can simplify access to web information, developers still need to think about data quality and responsible use.

Not every website contains accurate information. AI extraction can also misunderstand content, particularly when pages are ambiguous or information is presented in unusual ways.

For important applications, developers should validate extracted information and retain source URLs where possible. Context.dev’s extraction response includes the pages analyzed, which can help with traceability.

Legal and technical considerations also matter. Developers should respect applicable laws, website terms, robots.txt instructions, rate limits, and data-protection requirements when collecting public web information. Context.dev itself notes that responsibility for how its APIs are used rests with the developer.

Final Thoughts

AI agents need access to more than the static knowledge contained in a language model. To perform useful real-world tasks, they often need current information from websites, documentation, company pages, product catalogs, and other online sources.

Context.dev provides APIs designed to bridge that gap. It can scrape webpages into AI-friendly Markdown, crawl websites, extract specific data, provide images and screenshots, discover sitemaps, and supply company and brand context.

For developers building AI agents, RAG systems, research applications, competitive-intelligence tools, or other LLM-powered products, this approach can reduce the amount of custom web-data infrastructure required.

The larger idea is simple: AI agents become more useful when they can access relevant, current, structured context. By providing an API layer between the web and AI applications, Context.dev can help developers turn the constantly changing internet into information their agents can actually use.