Power LLMs with Web Context

Give your AI models real-time access to the live web

LLMs hallucinate when they lack current information. Give them exactly what they need: live web content, on demand, in the format models understand best.

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Daydream logo
Passionfroot logo
Klarna logo
Super.com logo
Orange logo
SendX logo
Kovai logo

How It Works

01

Scrape web context

Pass any URL or domain to retrieve HTML, Markdown, images, or a full sitemap in one API call

02

Inject into your LLM prompt

Include the scraped content in your system prompt or context window alongside your user query

03

Get grounded, accurate responses

Your model answers with real web context—no hallucinations about outdated or missing information

The Endpoints

Four endpoints, each optimized for a different stage of the LLM context pipeline. Use them together or independently depending on what your model needs.

Scrape Raw HTML

HTML
GET /v1/web/scrape/html

Retrieve the full HTML source of any URL. Use this when you need structured DOM data, need to extract specific elements, or want to feed raw page content into a custom parser before handing off to an LLM.

Best for

DOM parsing, structured extraction, custom pipelines

View documentation

Scrape to Markdown

Markdown
GET /v1/web/scrape/markdown

Convert any webpage to clean GitHub Flavored Markdown. The best format for LLM context windows—strips HTML noise while preserving semantic structure. Reduces token usage dramatically versus raw HTML.

Best for

LLM context, RAG pipelines, knowledge bases

View documentation

Scrape Images

Images
GET /v1/web/scrape/images

Extract every image from a page—img tags, inline SVGs, base64 data URIs, picture/source elements, and video posters. Returns src, element type, format, and alt text for each. Use for visual AI pipelines and multimodal models.

Best for

Multimodal AI, visual search, image indexing

View documentation

Crawl Sitemap

Sitemap
GET /v1/web/scrape/sitemap

Discover up to 500 page URLs on any domain by crawling its sitemap. Supports sitemap index files recursively. Use this to build a URL list before batch-scraping an entire site's content into your vector store.

Best for

Batch indexing, content discovery, RAG ingestion

View documentation

Who This Is For

RAG Pipeline Engineers

Building retrieval-augmented generation systems

The Challenge

Keeping LLM knowledge current requires constant re-indexing. Models hallucinate when their training data is outdated relative to live web content.

How This Helps

Use the Markdown endpoint to fetch and embed fresh web pages on demand. Combine with the Sitemap endpoint to bulk-ingest entire domains into your vector store.

Always-current RAG context without manual scraping infrastructure. Your models answer with facts, not guesses.

AI Agent Developers

Building autonomous agents that browse the web

The Challenge

Agents need to read web pages, but running a browser per-agent is expensive, slow, and hard to scale. Managing proxies and bot detection is a full-time job.

How This Helps

Replace headless browser calls with single API requests. Get HTML or Markdown with automatic proxy escalation built in—no infrastructure to maintain.

Agents that browse the web reliably at scale. Reduce per-agent infrastructure cost and eliminate browser management complexity.

AI Search Products

Building AI-powered search and answer engines

The Challenge

Answering questions about current events or specific URLs requires live web access. Training data cutoffs leave models unable to answer about recent content.

How This Helps

Scrape the relevant URL to Markdown and inject it as context before generating an answer. Give your model real-time access to any page on the web.

Search results grounded in real, current content. Users trust answers backed by live sources.

Competitive Intelligence Platforms

Monitoring competitors and market changes

The Challenge

Tracking competitor websites for pricing, product, and messaging changes requires scrapers that constantly break against site updates and bot detection.

How This Helps

Use the HTML or Markdown endpoint to snapshot competitor pages on a schedule. Diff the output and feed changes to an LLM for automated analysis and alerts.

Always-current competitive intelligence. Know when a competitor changes pricing or messaging within hours, not weeks.

Document & Knowledge Base Tools

Ingesting web content into knowledge systems

The Challenge

Users want to import web pages and articles into their knowledge bases, but HTML is noisy and hard to chunk correctly for embedding.

How This Helps

Use the Markdown endpoint for clean, well-structured content ready for chunking. Use the Sitemap endpoint to discover all pages in a documentation site for bulk import.

Faster, cleaner knowledge base ingestion. Better embeddings and retrieval from content that is actually readable.

LLM Evaluation & Testing Teams

Testing and benchmarking language models

The Challenge

Evaluating how well a model handles real-world web content requires a reliable way to fetch diverse, current web pages at scale.

How This Helps

Programmatically scrape a wide range of pages using the HTML or Markdown API to build evaluation datasets grounded in real, current web content.

Richer, more realistic model evaluations. Test against the actual web your users interact with—not stale snapshots.

Content Automation Platforms

Generating content from web sources at scale

The Challenge

Content teams want AI that can research topics from the live web before writing, but connecting models to live web data requires custom infrastructure.

How This Helps

Fetch relevant pages as Markdown and inject into the generation prompt. Your content AI researches, summarizes, and cites live sources automatically.

AI-written content grounded in current sources. Higher quality output with citations users can verify.

Multimodal AI Applications

Building apps that reason over images and text together

The Challenge

Extracting images from web pages to feed into vision models requires parsing complex HTML, handling SVGs, base64 URIs, and multiple image element types.

How This Helps

Use the Images endpoint to extract every image from any URL in a structured array. Each image is classified by type and element source—ready for multimodal model input.

Reliable image extraction without brittle scrapers. Feed vision models web images without building custom extraction logic.

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