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For AI and retrieval-augmented generation (RAG) over articles, documentation, and other prose, start with clean Markdown. Choose schema-based JSON when your application needs repeatable named fields, and keep processed or raw HTML when markup or attributes matter. Separately decide whether to scrape a known URL or crawl a site to discover pages. No cited controlled benchmark establishes one format as universally better for retrieval.
Choose by what the next stage needs
A scraping format is not a quality ranking. It is a representation choice: what information should leave the scraper, and in what shape should your application receive it? Make that decision by tracing the data into its next use—chunking and indexing, record processing, markup-aware parsing, or another task.
- Readable content for indexing, summarization, or RAG: begin with clean Markdown.
- Consistent records for application code: use schema-based JSON and validate the result.
- Markup, attributes, or page-specific structures: retain processed or raw HTML, or extract the required attributes explicitly.
- Known page versus unknown pages: scrape a URL you already have; crawl when the service must discover subpages.
Scrapy describes clean page content as useful input for a search index, summarizer, or RAG pipeline, and demonstrates Markdown extraction from a response: Scrapy documentation. Firecrawl describes Markdown as its default scrape output: Firecrawl scrape documentation. Those capabilities support a practical starting point, not a universal claim about retrieval quality.
Compare the main output formats
| Format | Choose it when | Watch for |
|---|---|---|
| Clean Markdown | The useful payload is readable page content: headings, prose, lists, and code for indexing, summarization, or retrieval. | Conversion can remove page details and HTML attributes. Fidelity varies by page and conversion behavior, so inspect representative outputs. |
| Schema-based JSON | You need normalized records with named fields that downstream code can consume consistently. | Define and validate a schema. Extraction depends on what the conversion exposes; do not assume it can recover every source-page attribute. |
| Processed HTML | You need more markup structure than plain text, but want a processed version with some unnecessary elements removed. | Check what processing removes for your pages and parser. |
| Raw HTML | You need to parse original response markup, attributes, embedded structures, or page-specific markup yourself. | It carries more source complexity and requires downstream parsing. Prefer it only when that extra information is useful. |
Firecrawl’s JSON mode documentation describes JSON extraction from Markdown-converted visible text; it also says HTML attributes are stripped from that conversion. If a field exists only in an attribute, a JSON schema alone may not retrieve it. Use raw HTML parsing or an extraction/preprocessing action that exposes the value. See Firecrawl JSON mode documentation and scrape options.
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#1 Best Overall
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When Markdown is the right starting point
Markdown is a sensible default when the goal is to give a language model or text pipeline the page’s meaningful visible content without having to process an entire browser document. Its headings and lists preserve useful reading structure, while code blocks can remain distinguishable from surrounding prose. This can simplify later chunking and make documents easier to inspect.
Markdown is not a guarantee of perfect page fidelity. Conversion may omit attributes or other details that are not represented as ordinary text. Pages with tables, product metadata, diagrams, dynamic content, or unusual layouts deserve inspection. If a RAG answer must cite or reason over a value encoded in markup rather than visible text, make sure that value is captured before relying on Markdown alone.
When to use schema-based JSON
Use JSON when your application expects fields such as a title, date, author, or selected product facts, and a stable record shape is more useful than a whole readable document. A schema gives extraction an explicit target and makes downstream validation possible.
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JSON solves structure and parseability, not source completeness. Define required fields and types, validate each response, and decide what your system should do when a field is missing or malformed. If a needed value is held in an HTML attribute, first verify that the extraction path preserves or exposes it. Otherwise parse the relevant HTML separately and combine the result with the visible-text extraction.
When HTML is worth retaining
Processed HTML is a middle ground when you want some markup while removing elements your workflow considers unnecessary. Raw HTML is the better fit when your own parser needs source markup, attributes, or embedded structures. Because processing can remove content, compare its output with the original for the kinds of pages you actually collect.
Keeping all HTML “just in case” shifts work to later stages: your pipeline must parse, filter, and handle more structure. Conversely, converting immediately to text can discard information that cannot be reconstructed. Preserve the minimum representation that meets the use case, and retain raw source where a real downstream requirement justifies it.
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Choose scrape or crawl independently of format
Format answers “how should each page be represented?” Scope answers “which pages should be collected?” A scrape request generally targets a known URL; a crawl discovers and processes subpages across a domain. A crawl can return Markdown or JSON, so crawling does not dictate the output representation. Firecrawl documents these as distinct scrape and crawl operations: scrape and crawl.
- Known URLs: scrape those pages directly when the input list is already authoritative.
- Site discovery: crawl when you need the service to find subpages. Set appropriate scope and inspect what the crawl actually includes.
Keep extraction format separate from export format
The representation produced from a page and the serialization used to deliver records are related but distinct choices. For example, a pipeline can extract clean content, normalize selected fields into items, then export those items in a format and storage backend suited to the next system. Scrapy’s feed-export documentation covers serialization formats and storage backends: Scrapy feed exports.
This separation prevents a common design mistake: choosing a scraping representation solely because a destination accepts that file type. Decide first what information needs extracting; then choose how to package and store it.
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A practical selection and validation workflow
- Write down the downstream task. Specify whether the next stage needs readable passages, named fields, markup-aware parsing, or page discovery.
- List required information. Mark which values are visible text and which may live in attributes or embedded markup.
- Select the least complex adequate representation. Start with Markdown for prose; add schema-based JSON for records; retain HTML when its structure carries required information.
- Decide collection scope. Use individual scrapes for known URLs and a crawl for subpage discovery.
- Inspect representative outputs. Include ordinary pages and edge cases from the actual site. Check headings, lists, tables, code, fields, attributes, and omitted content.
- Validate downstream behavior. Test schema parsing, chunking, indexing, and retrieval against the questions the system must answer. Track missing fields and parsing failures.
- Revise only where evidence points. If needed information disappeared in conversion, change extraction or preserve HTML. If HTML adds no value, simplify the pipeline.
Compare choices on four axes: content fidelity, structure and attribute retention, schema stability and parseability, and fit to the downstream task. There is no controlled, general RAG comparison in the cited documentation showing that one output format wins across representative corpora.
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For a screenshot or PDF of a page, ScreenshotNeo offers a one-request capture API; it returns an image or PDF, not a Markdown or JSON scrape. A screenshot can complement text extraction when visual layout matters, but it does not replace structured page content for text retrieval. See ScreenshotNeo and the API documentation.
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpimport requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
open("shot.webp", "wb").write(r.content)const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' });
const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);ScreenshotNeo removes cookie banners, newsletter popups, and chat widgets before capture; bot checks, blank pages, and failed loads are not billed. Its MCP server lets AI agents take screenshots, and the free plan includes 1,000 screenshots a month with no card; paid plans start at $5 for 3,000. Sign up free for ScreenshotNeo.
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Troubleshooting format and ingestion problems
- A field is absent from JSON: check whether the value is visible text or an HTML attribute. The cited JSON mode converts Markdown and strips attributes; extract the attribute from HTML or expose it in preprocessing.
- Markdown loses layout-dependent information: inspect processed and raw HTML for the page type, then adjust the extraction path to retain the needed structure.
- JSON records parse inconsistently: validate against an explicit schema and handle missing or invalid fields rather than treating every extraction as complete.
- A crawl misses expected pages: distinguish URL discovery from output format; inspect crawl scope and discovered URLs, or scrape a known URL directly.
- Retrieval quality is disappointing: inspect the collected content and downstream chunking and indexing. The available documentation does not establish a universal format-based fix or retrieval winner.
What the evidence does—and does not—establish
The product documentation establishes that these output modes and collection workflows are available and describes important conversion behavior. It does not provide a controlled, cross-format benchmark of RAG accuracy, nor an independently supported general statistic for token reduction or retrieval improvement. Treat the recommendation to start with Markdown for prose as a practical workflow choice, then validate it on your own content and retrieval task. Documentation and service capabilities can change; the linked vendor and Scrapy pages were accessed on September 29, 2026.
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- 5-in-1 Connectivity: Equipped with a 4K HDMI port, a 5 Gbps USB-C data port, two 5 Gbps USB-A ports, and a USB C 100W PD-IN port. Note: The USB C 100W PD-IN port supports only charging and does not support data transfer devices such as headphones or speakers.
- Powerful Pass-Through Charging: Supports up to 85W pass-through charging so you can power up your laptop while you use the hub. Note: Pass-through charging requires a charger (not included). Note: To achieve full power for iPad, we recommend using a 45W wall charger.
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- What You Get: Anker 332 USB-C Hub (5-in-1), welcome guide, our worry-free 18-month warranty, and friendly customer service.
Frequently Asked Questions
Does JSON perform better than Markdown for AI agents?
The cited documentation does not establish a general performance winner. JSON is useful when an agent needs named fields; Markdown is useful when it needs readable page content.
Can I use Markdown for a crawl?
Yes. Crawl scope and output representation are separate choices; a crawl can provide Markdown or JSON.
Should I store raw HTML for every page?
Only when the pipeline needs original markup, attributes, or structures that processed output would lose. Otherwise, retaining it adds parsing work without an established benefit.
Quick Recap
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