Markdown extraction APIs for RAG pipelines
RAG systems do not just need a webpage. They need clean, attributable text that can be chunked, embedded, searched, cited, and refreshed. Markdown extraction APIs sit between raw web pages and AI-agent context.
Short answer
Start with Firecrawl when...
Your main job is turning documentation, help pages, changelogs, blogs, or public site content into LLM-readable markdown for RAG or agent context.
Test ScrapingBee when...
You want a broader managed scraping API and may later need request controls, field extraction, JavaScript rendering, screenshots, or browser-like options.
This is a workflow recommendation, not a claim that either vendor is universally better for RAG.
What a RAG pipeline needs from extraction
Readable text
The output should be useful before heavy cleanup, with the main content visible and repeated page furniture reduced.
Structure
Headings, links, lists, code blocks, and tables should survive well enough for chunking and citations.
Traceability
The pipeline needs source URLs, titles, status, and enough metadata to support refresh and audit workflows.
Failure clarity
A failed extraction should be obvious enough for retry, skip, or manual review logic.
Small matched test
Agent API Atlas ran a small matched extraction test on 2026-07-02 across two public documentation pages: Firecrawl scrape documentation and ScrapingBee documentation. The test measured HTTP status, output size, headings, links, code fences, expected terms, early-page noise, latency, and a heuristic RAG-fit score.
| Vendor | Tested pages | HTTP result | Average heuristic RAG-fit score | Editorial reading |
|---|---|---|---|---|
| Firecrawl | 2 public docs pages | 200 on both | 94 | Strong markdown-first candidate for docs-to-RAG ingestion. |
| ScrapingBee | 2 public docs pages | 200 on both | 94 | Usable markdown/text output in this small test, especially as a managed API workflow. |
This is not a production benchmark. It does not prove either vendor is universally better for RAG. It supports treating both as credible candidates for a first RAG ingestion test, with Firecrawl remaining the more natural markdown-first starting point.
Vendor fit for RAG ingestion
| Question | Firecrawl | ScrapingBee | What to avoid claiming |
|---|---|---|---|
| Is the API markdown-first? | Strong fit Official docs emphasize markdown and LLM-oriented output. | Available output Official docs include markdown/text output parameters. | Do not say either output is always clean enough without review. |
| Is it useful for docs-to-RAG? | Yes as a first candidate, based on official docs and a small matched test. | Yes as a candidate, especially if you also want a broader scraping API surface. | Do not turn this into a production success-rate ranking. |
| Does it preserve structure? | The small test showed headings, links, and code fences. More table cases are still needed. | The small test also showed headings, links, and code fences. More table cases are still needed. | Do not claim robust table preservation yet. |
| Does it handle dynamic pages? | Not covered by this RAG test. | Not covered by this RAG test. | Do not claim JS-rendered content quality from this page. |
| What is the commercial path? | Good vendor-profile click potential later. | Good comparison and vendor-profile click potential later. | No affiliate or referral links are used in this soft-launch page. |
Evaluation checklist
Practical recommendation
If your AI agent mainly needs docs, help-center pages, or public content for RAG, test Firecrawl first and compare it against ScrapingBee on the same URLs. If your workflow may expand into rendered pages, screenshots, or selector-based extraction, include ScrapingBee early rather than treating markdown extraction as the whole problem.
Do not choose only by whether the request returns HTTP 200. Choose by whether the output reduces downstream cleanup while keeping extraction failures visible.