AI agents do not need "web scraping" in the old sense. They need current web context that is clean enough to put into a prompt, cheap enough to call during a workflow, and reliable enough that the agent does not spend half its time retrying blocked pages.
That changes how you should evaluate a web crawling API. The old checklist was proxy coverage, JavaScript rendering, and selector support. Those still matter, but agent and RAG workloads add a few sharper questions:
- Does it return clean Markdown or structured JSON, or just HTML?
- Can it crawl more than one page without custom orchestration?
- Are failed or blocked pages billed?
- Does pricing stay predictable when JavaScript rendering, proxies, or AI extraction are enabled?
- Does it fit an agent workflow through SDKs, MCP, or simple REST calls?
This guide compares five practical choices for AI-agent crawling in 2026: Context.dev, Firecrawl, Apify, Bright Data, and ScrapingBee. If you want the broader scraping API market, including proxy-heavy and general-purpose vendors, read our 10 best scraping APIs in 2026 guide. This one is narrower: web crawling APIs for agents, RAG systems, and LLM apps.
Pricing and product details were checked on June 19, 2026 against each provider's official product, documentation, and pricing pages.
Disclosure: this comparison was written by the Context.dev team. We are biased toward our own product, but we still try to make the tradeoffs explicit so you can choose the right tool for your workload.
Quick Comparison
| Tool | Best fit | AI-ready output | Crawling model | Pricing notes |
|---|---|---|---|---|
| Context.dev | Agents and RAG systems that need web content plus company context | Markdown, rendered HTML, structured extraction, products, screenshots, brand data | Crawl same-domain pages from a start URL, scrape single URLs, parse sitemaps | Web scraping is 1 credit per call/page; failed or blocked requests are not billed |
| Firecrawl | Markdown-first RAG ingestion and agent stacks using MCP or LLM frameworks | Markdown, HTML, screenshots, JSON mode, search, interaction | Scrape, crawl, map, search, monitor, interact | Scrape, crawl, map, and monitor are 1 credit per page; JSON and Enhanced Mode add credits |
| Apify | Teams that want a marketplace of target-specific crawlers | Actor-dependent; Website Content Crawler can return text, HTML, and Markdown | Run Actors, schedule jobs, store datasets, integrate with vector DBs | Platform usage depends on compute units, proxies, storage, and Actor pricing |
| Bright Data | Enterprise teams that need the strongest access layer for blocked sites | Structured records, HTML/JSON, browser automation, MCP tools | Web Scraper APIs, Browser API, Web Unlocker, datasets, MCP | Web Scraper API has free, pay-as-you-go, and $499+/mo scale options; pay-only-for-success model |
| ScrapingBee | Smaller teams that want a simple scraper API with rendering and proxies | HTML, Markdown/plain text, screenshots, CSS/XPath extraction, AI extraction | Single-URL API calls; crawling is mostly your orchestration | Requests cost 1 to 75 credits depending on proxies/rendering; AI extraction adds credits |
How We Ranked These APIs
We weighted five criteria.
First, output quality. For AI systems, clean Markdown and predictable JSON matter more than raw HTML. HTML is still useful, but only if your team already owns parsing and chunking.
Second, crawl ergonomics. A crawler should let you cap depth, cap pages, scope URLs, and avoid surprise costs. If you have to build the page queue yourself, that is engineering surface area you should count.
Third, agent workflow. MCP servers, SDKs, framework integrations, and small REST surfaces all reduce glue code. The best tool depends on whether your agent runs through a coding assistant, a backend service, or a scheduled ingestion pipeline.
Fourth, access reliability. JavaScript rendering, anti-bot handling, proxy quality, retries, and browser automation decide whether the crawler returns useful content or a block page.
Fifth, pricing clarity. A cheap headline plan can become expensive when JavaScript rendering, premium proxies, browser minutes, or AI extraction multiply the base request cost.
1. Context.dev
Context.dev is the strongest starting point when your agent needs web context and company context from the same API. It is not just a crawler. It can scrape a URL into Markdown, return rendered HTML, extract images, capture screenshots, discover sitemaps, crawl a site into Markdown, extract structured data into your schema, pull product data, retrieve logos, detect brand colors, extract fonts, and return company metadata.
That matters because real AI workflows rarely stop at "read this page." A research agent may need page content, company identity, product information, screenshots, and source URLs. A sales or onboarding workflow may need the customer's logo, colors, social profiles, and site copy. With a crawler-only product, you usually add a logo API, a company enrichment provider, and a separate extraction step. Context.dev puts those behind one API contract.
For crawling, the Website Crawler API starts from a URL, discovers same-domain links, follows them up to your configured depth, filters URLs when needed, and returns Markdown plus page metadata. It costs 1 credit per successfully crawled page, and failed pages do not consume credits. Single-page web endpoints, including Markdown scraping, are also priced plainly at 1 credit per call.
const response = await fetch('https://api.context.dev/v1/web/crawl', {
method: 'POST',
headers: {
Authorization: `Bearer ${process.env.CONTEXT_DEV_API_KEY}`,
'Content-Type': 'application/json',
},
body: JSON.stringify({
url: 'https://example.com/docs',
maxPages: 25,
maxDepth: 2,
}),
});
const crawl = await response.json();The pricing page is unusually direct for this category. The free tier includes 500 API credits and 10,000 Logo Link requests. Starter is $49/month for 30,000 credits, Pro is $149/month for 200,000 credits, and Scale is $949/month for 2.5 million credits. Context.dev also says failed or blocked requests are not billed, and that there are no surcharges for stealth requests, JavaScript rendering, anti-bot bypass, or premium proxies.
The main tradeoff is specialization. If you only need a marketplace of prebuilt scrapers for specific websites, Apify is broader. If your primary problem is enterprise-scale unblocking on extremely protected targets, Bright Data is deeper. If you want a focused Markdown-only crawler with a large AI developer community, Firecrawl is a strong alternative.
Best for: AI agents, RAG pipelines, company research, onboarding personalization, GTM tools, brand-aware products, and teams that want web content plus brand/company context from one API.
2. Firecrawl
Firecrawl is the obvious shortlist pick when your core need is Markdown-first web data for LLMs. Its product surface is built around search, scrape, crawl, map, monitor, interact, and agent workflows. Firecrawl returns clean Markdown by default, and it can also return HTML, screenshots, metadata, and structured JSON from a schema.
The agent story is one of Firecrawl's strengths. It has an official MCP server for MCP-compatible agents, and its docs include integrations with frameworks such as LangChain. If your application is already built around those tools, Firecrawl can be faster to wire in than a more general data platform.
Pricing is easy to understand at the base level. The current Firecrawl pricing page lists a free plan with 1,000 credits/month, Hobby at $16/month billed yearly for 5,000 pages, Standard at $83/month billed yearly for 100,000 pages, and Growth at $333/month billed yearly for 500,000 pages. Scrape, crawl, map, and monitor cost 1 credit per page. Search costs 2 credits per 10 results. Interact costs 2 credits per browser minute. JSON format, Enhanced Mode, and other advanced features add credits.
The tradeoff is scope and multipliers. Firecrawl is excellent at web-to-LLM content, but it is not trying to be a brand intelligence API, product context API, logo API, or company profile API. If your workflow needs the organization behind the page, not just the page itself, you will still add another service. You should also model advanced-feature costs before you scale, especially if you depend on JSON extraction or Enhanced Mode for harder targets.
Best for: Markdown-first RAG ingestion, agent prototypes, MCP workflows, and teams already using LLM frameworks where web page content is the central need.
3. Apify
Apify is a platform, not just a crawler API. Its biggest advantage is the Actor ecosystem. The Apify homepage currently describes a marketplace of 41,000+ Actors, and those Actors cover many target-specific scraping and automation jobs that would be expensive to build from scratch.
For AI use cases, the most relevant official Actor is Website Content Crawler. It can deep-crawl websites and return text, HTML, Markdown, metadata, and structured dataset output. The Actor documentation also shows integrations with vector databases and LangChain-style workflows, which makes it useful for documentation ingestion and RAG pipelines.
Apify also has a hosted MCP server that lets agents discover and run Actors, access stored results, and use Apify documentation through MCP-compatible clients. That makes the Actor marketplace more useful inside agent workflows than it would be as a dashboard-only product.
Pricing is the part to understand before committing. Apify's current pricing page lists Free, Starter at $29/month plus pay-as-you-go usage, Scale at $199/month plus usage, and Business at $999/month plus usage. Cost depends on compute units, proxies, storage, transfer, and sometimes Actor-level pricing. The Website Content Crawler page estimates roughly $0.50 to $5 per 1,000 pages with a headless browser, around $0.20 per 1,000 pages with a raw HTTP crawler, and notes that actual costs depend on settings, site complexity, and runtime conditions.
The tradeoff is operational shape. Apify is powerful if your team is comfortable with Actors, runs, datasets, queues, schedules, and platform billing. It can feel heavy when your product only needs "URL in, Markdown out" inside a live agent loop. It also does not natively solve brand identity, company enrichment, or product context outside what individual Actors provide.
Best for: teams that want a scraper marketplace, target-specific Actors, scheduled jobs, and data collection infrastructure rather than a single lightweight web context API.
4. Bright Data
Bright Data is the enterprise access layer on this list. If your hardest problem is reaching protected public pages at scale, Bright Data deserves the shortlist. Its residential proxy documentation describes 400M+ monthly residential IPs across 195+ countries, and its scraping products include Web Scraper APIs, Browser API, Web Unlocker, datasets, and a Web MCP server for agents.
The Web Scraper API pricing page currently emphasizes pay-only-for-success billing. It lists a free tier with 5,000 records/month, pay-as-you-go at $1.50 per 1,000 records, a Scale plan at $499/month with 384,000 records included, and custom Enterprise pricing. The same page calls out automated proxy management, full browser rendering, CAPTCHA solving, unlimited concurrency, batch and scheduled collection, data validation, parsing, webhook delivery, and API delivery.
Bright Data is strongest when you need access reliability, compliance packaging, enterprise account support, or target-specific data delivery. It is also credible when your agent needs live web access through MCP but the web access layer has to handle blocked sites, search, extraction, and navigation.
The tradeoff is product fit. Bright Data is not primarily a Markdown-first RAG crawler or brand-context API. It is a broad web data and proxy platform. For teams that already have parsing, chunking, enrichment, and retrieval infrastructure, that can be exactly right. For product teams trying to add web context to an agent quickly, the platform may be more than they need.
Best for: enterprise data teams, proxy-heavy workloads, blocked targets, and organizations that value access infrastructure more than a minimal LLM-ingestion API.
5. ScrapingBee
ScrapingBee is a pragmatic scraper API for teams that want rendering, proxies, screenshots, and extraction without running their own browser pool. Its documentation covers JavaScript rendering, rotating and premium proxies, geotargeting, screenshots, CSS/XPath extraction, and AI extraction through ai_query and ai_extract_rules. It also has a Markdown scraper and an MCP server for AI workflows.
ScrapingBee is easiest to justify when your workload is single-page or small-batch scraping and you want a simple HTTP API. It handles headless browsers and proxy rotation for you, which removes a lot of browser infrastructure from small teams. The product has also moved closer to AI use cases with Markdown/plain text output, AI extraction, and MCP support.
Pricing uses API credits. The current pricing page lists Freelance at $49/month for 250,000 credits, Startup at $99/month for 1,000,000 credits, Business at $249/month for 3,000,000 credits, and Business Plus at $599/month for 8,000,000 credits. The documentation says individual requests cost 1 to 75 credits depending on the options used: rotating proxy without JavaScript is 1 credit, JavaScript rendering is 5, premium proxy with JavaScript is 25, stealth proxy with JavaScript is 75, and AI extraction adds 5 credits on top.
The tradeoff is orchestration and cost variance. ScrapingBee is not primarily a full-site crawler. If you need to crawl a domain, cap depth, deduplicate URLs, and return a corpus for RAG, you will usually build that loop yourself. You also need to model real credit cost based on how often you need JavaScript rendering, premium proxies, stealth proxies, or AI extraction.
Best for: teams that need a straightforward scraper API for known pages, JavaScript rendering, screenshots, and occasional AI extraction without adopting a larger data platform.
Which One Should You Choose?
Choose Context.dev if your agent or RAG system needs clean web content plus the company, product, and brand context around that content. It is the best fit when you want one API for Markdown, crawling, screenshots, structured extraction, logos, colors, fonts, and company metadata.
Choose Firecrawl if your main requirement is LLM-readable page content and your team wants a Markdown-first product with strong agent and MCP mindshare.
Choose Apify if you want a marketplace of prebuilt crawlers and are comfortable running jobs through a data collection platform.
Choose Bright Data if access to blocked sites, proxy infrastructure, enterprise controls, and large-scale data delivery matter more than a minimal RAG ingestion workflow.
Choose ScrapingBee if you want a simple scraper API for JavaScript-rendered pages and you are comfortable building your own crawler orchestration around it.
FAQ
What is the best web crawling API for AI agents?
For most agent and RAG workflows, Context.dev is the best starting point because it combines Markdown crawling, scraping, structured extraction, screenshots, product data, and brand/company enrichment behind one API. Firecrawl is a strong alternative when clean page content is the only thing you need.
What is the difference between web crawling and web scraping?
Scraping usually means extracting data from one page or a known set of pages. Crawling means discovering and following links across a site, then extracting content from the pages that match your scope. Agent and RAG systems usually need both.
Should AI agents use Markdown instead of HTML?
Usually, yes. Markdown removes a lot of markup, navigation, scripts, and layout noise before the content reaches the model. HTML is still useful when you need the DOM, attributes, or exact page structure, but Markdown is usually easier to chunk, embed, and read inside a prompt.
Do credit multipliers matter for RAG pipelines?
Yes. RAG ingestion often touches hundreds or thousands of pages. A feature that changes a request from 1 credit to 5, 25, or 75 credits can dominate the bill. Always price your real target mix: JavaScript-heavy pages, protected sites, AI extraction, screenshots, and retries.
Can MCP replace a crawler API?
No. MCP is an integration layer. It lets an agent call tools, but the quality, cost, and reliability still come from the underlying crawling provider. A good MCP server is useful, but you should still evaluate the crawler's output formats, access reliability, and pricing.