markcrawl 0.6.0

Fast Python web crawler for AI & RAG ingestion — crawl, extract, and embed website content with one tool.

MarkCrawl by iD8 🕷️📝

Turn any webpage or website into clean Markdown for LLM pipelines — in one command.

pip install markcrawl
markcrawl --base https://docs.example.com --out ./output --show-progress

MarkCrawl is a crawl-and-structure engine. It fetches one page or crawls an entire website, strips navigation/scripts/boilerplate, and writes clean Markdown files with a structured JSONL index. Every page includes a citation with the access date. No API keys needed.

Everything else — LLM extraction, Supabase upload, MCP server, LangChain tools — is optional and installed separately.

Want a hosted API instead of running locally? Join the waitlist — we're gauging interest.

LLM agents: Load docs/LLM_PROMPT.md as a system prompt to generate correct MarkCrawl commands automatically.

Quickstart (2 minutes)

pip install markcrawl
markcrawl --base https://quotes.toscrape.com --out ./demo --max-pages 5 --show-progress

Your ./demo folder now contains:

demo/
├── index__a4f3b2c1d0.md    ← clean Markdown of the page
├── page-2__b7e2d1f0a3.md
├── ...
└── pages.jsonl              ← structured index (one JSON line per page)

Each line in pages.jsonl:

{
  "url": "https://quotes.toscrape.com/",
  "title": "Quotes to Scrape",
  "crawled_at": "2026-04-04T12:30:00Z",
  "citation": "Quotes to Scrape. quotes.toscrape.com. Available at: https://quotes.toscrape.com/ [Accessed April 04, 2026].",
  "tool": "markcrawl",
  "text": "# Quotes to Scrape\n\n> "The world as we have created it is a process of our thinking..." — Albert Einstein\n\nTags: change, deep-thoughts, thinking, world..."
}

Common Recipes

Scrape a single page:

markcrawl --base https://example.com/pricing --no-sitemap --max-pages 1

Scrape a single JS-rendered page (React, Vue, YouTube, etc.):

markcrawl --base "https://www.youtube.com/@channel/videos" \
  --no-sitemap --max-pages 1 --render-js
# → outputs one .md file with video titles, view counts, and dates

For infinite-scroll pages like YouTube, this captures the first ~28 videos from the initial render.

Crawl a docs site:

markcrawl --base https://docs.example.com --max-pages 500 --concurrency 5 --show-progress

Crawl a subsection without sitemap wandering:

Large sites (YouTube, GitHub, etc.) have sitemaps with thousands of unrelated pages. Use --no-sitemap to crawl only from your target URL:

markcrawl --base https://docs.example.com/guides \
  --no-sitemap --max-pages 50 --show-progress

Competitive analysis (crawl 3 competitors, extract pricing):

markcrawl --base https://competitor-one.com/pricing --no-sitemap --max-pages 1 --out ./comp1
markcrawl --base https://competitor-two.com/pricing --no-sitemap --max-pages 1 --out ./comp2
markcrawl --base https://competitor-three.com/pricing --no-sitemap --max-pages 1 --out ./comp3
markcrawl-extract \
  --jsonl ./comp1/pages.jsonl ./comp2/pages.jsonl ./comp3/pages.jsonl \
  --fields pricing_tiers features free_trial --show-progress
# → extracted.jsonl with structured pricing data across all three

Docs site → RAG chatbot (full pipeline: crawl, embed, query):

markcrawl --base https://docs.example.com --out ./docs --max-pages 500 --concurrency 5 --show-progress
markcrawl-upload --jsonl ./docs/pages.jsonl --show-progress
# → pages are chunked, embedded, and uploaded to Supabase/pgvector
# Wire your chatbot to query the vector table — see docs/SUPABASE.md

API docs → code generation prompt:

markcrawl --base https://api.example.com/docs --out ./api-docs --max-pages 200 --show-progress
# Feed the output to an LLM:
# "Using the API documentation in ./api-docs/pages.jsonl, generate a
#  typed Python client with methods for each endpoint."

Back up a blog before it shuts down:

markcrawl --base https://engineering.example.com/blog \
  --no-sitemap --max-pages 1000 --concurrency 5 --out ./blog-archive --show-progress
# → every post saved as clean Markdown with citations and access dates

Skip junk pages (job listings, login walls, SEO spam):

markcrawl --base https://example.com \
  --exclude-path "/job/*" --exclude-path "/careers/*" --exclude-path "/login" \
  --max-pages 500 --out ./output --show-progress

Preview URLs before committing to a long crawl:

markcrawl --base https://example.com --dry-run
# → prints every URL that would be crawled (from sitemap), then exits
# Pipe to wc -l to get a count, or grep to check for junk patterns
markcrawl --base https://example.com --dry-run | wc -l
markcrawl --base https://example.com --dry-run | grep "/job/"

Only crawl specific sections (blog + pricing, ignore everything else):

markcrawl --base https://example.com \
  --include-path "/blog/*" --include-path "/pricing" \
  --max-pages 200 --out ./output --show-progress

Safe crawl of a job board (dry-run + exclude):

# Step 1: see what you'd get
markcrawl --base https://tealhq.com --dry-run | head -50
# Step 2: exclude the job listings, crawl just the content pages
markcrawl --base https://tealhq.com \
  --exclude-path "/job/*" --exclude-path "/resume-examples/*" \
  --max-pages 200 --out ./tealhq --show-progress

Choose an extraction backend:

# Default (BS4 + markdownify) — fastest, good for most sites
markcrawl --base https://docs.example.com --out ./output --show-progress

# Ensemble — runs default + trafilatura, picks best per page
markcrawl --base https://docs.example.com --out ./output --extractor ensemble --show-progress

# ReaderLM-v2 — ML-based extraction (requires: pip install markcrawl[ml])
markcrawl --base https://docs.example.com --out ./output --extractor readerlm --show-progress

Skip pages you've already crawled (cross-crawl dedup):

# First crawl
markcrawl --base https://docs.example.com --out ./docs --show-progress
# Later — only fetches new/changed pages
markcrawl --base https://docs.example.com --out ./docs --cross-dedup --show-progress

Crawl high-value pages first (link prioritization):

markcrawl --base https://docs.example.com --out ./docs \
  --prioritize-links --max-pages 100 --show-progress
# Prioritizes content-rich pages (guides, docs) over low-value ones (legal, login)

Smart-sample a large site (e-commerce, job boards, real estate):

# Preview the pattern clusters first
markcrawl --base https://bigsite.com --dry-run --smart-sample --show-progress
# Crawl with sampling — 5 pages per templated cluster instead of thousands
markcrawl --base https://bigsite.com --out ./bigsite \
  --smart-sample --sample-size 5 --sample-threshold 20 --show-progress

Download images alongside content (photography blogs, product pages):

# Crawl a photography blog and save images from the content area
markcrawl --base https://photography-blog.example.com --out ./photos \
  --download-images --max-pages 50 --show-progress
# Output:
#   ./photos/assets/mountain-abc123.jpg
#   ./photos/assets/sunset-def456.png
#   ./photos/post-1__a1b2c3.md  ← Markdown with ![alt](assets/filename.ext) refs
#   ./photos/pages.jsonl         ← index includes "images" array per page

# Adjust minimum image size to skip thumbnails (default: 5000 bytes)
markcrawl --base https://example.com/gallery --out ./gallery \
  --download-images --min-image-size 20000 --show-progress

Resume an interrupted crawl:

markcrawl --base https://docs.example.com --out ./docs --resume --show-progress

How it compares to other crawlers

Different tools make different tradeoffs. This table summarizes the main differences:

	MarkCrawl	FireCrawl	Crawl4AI	Scrapy
License	MIT	AGPL-3.0	Apache-2.0	BSD-3
Install	pip install markcrawl	SaaS or self-host	pip + Playwright	pip + framework
Output	Markdown + JSONL	Markdown + JSON	Markdown	Custom pipelines
JS rendering	Optional (--render-js)	Built-in	Built-in	Plugin
LLM extraction	Optional add-on	Via API	Built-in	None
Best for	Single-site crawl → Markdown	Hosted scraping API	AI-native crawling	Large-scale distributed

Each tool has strengths: FireCrawl excels as a hosted API, Crawl4AI has deep browser automation, and Scrapy handles massive distributed workloads. MarkCrawl focuses on simple local crawls that produce LLM-ready Markdown.

Benchmark results (7 tools, April 2026 — v2 methodology)

Speed: markcrawl is fastest (12.1 pages/sec), scrapy+md second (9.5). Playwright-based tools (crawlee, playwright, crawl4ai) average 1.5–2.2 pages/sec.

Content signal: markcrawl leads at 99% (ratio of answer-bearing tokens to total output) — almost no navigation, footer, or boilerplate makes it into your embeddings.

RAG quality: markcrawl scores 4.52/5 on LLM-judged answer quality (tied #2, leader at 4.53 within noise) and 0.698 MRR (3rd, leader crawlee at 0.733) — with 2.1x fewer chunks than crawlee, keeping embedding costs low.

Tool	Speed (p/s)	Content Signal	MRR	Answer (/5)	Annual cost (100K pages)
markcrawl	12.1	99%	0.698	4.52	$4,505
scrapy+md	9.5	93%	0.459	4.03	$5,464
colly+md	4.2	67%	0.677	4.53	$7,213
playwright	2.2	64%	0.727	4.42	$7,320
crawlee	1.7	63%	0.733	4.52	$7,467
crawl4ai	1.5	83%	0.694	4.43	$6,960

Full benchmark data: docs/BENCHMARKS.md | Methodology: llm-crawler-benchmarks

RAG-optimized recipe (v0.6.0): With --i18n-filter --title-at-top and the opt-in chunker flags (auto_extract_title=True, prepend_first_paragraph=True, strip_markdown_links=True on chunk_markdown), markcrawl reaches 0.8148 MRR on the same 57-query benchmark — a +0.18 jump over the default config and +0.08 over the next best tool (crawlee at 0.733).

Installation

The core crawler is the only thing you need. Everything else is optional.

pip install markcrawl                # Core crawler (free, no API keys)

Optional add-ons:

pip install markcrawl[extract]       # + LLM extraction (OpenAI, Claude, Gemini, Grok)
pip install markcrawl[js]            # + JavaScript rendering (Playwright)
pip install markcrawl[upload]        # + Supabase upload with embeddings
pip install markcrawl[ml]            # + ReaderLM-v2 extraction backend
pip install markcrawl[mcp]           # + MCP server for AI agents
pip install markcrawl[langchain]     # + LangChain tool wrappers
pip install markcrawl[all]           # Everything

For Playwright, also run playwright install chromium after installing.

Install from source (for development)

git clone https://github.com/AIMLPM/markcrawl.git
cd markcrawl
python -m venv .venv
source .venv/bin/activate
pip install -e ".[all]"

Crawling

markcrawl --base https://www.example.com --out ./output --show-progress

Add flags as needed:

markcrawl \
  --base https://www.example.com \
  --out ./output \
  --include-subdomains \        # crawl sub.example.com too
  --render-js \                 # render JavaScript (React, Vue, etc.)
  --concurrency 5 \             # fetch 5 pages in parallel
  --proxy http://proxy:8080 \   # route through a proxy
  --max-pages 200 \             # stop after 200 pages
  --format markdown \           # or "text" for plain text
  --show-progress

Resume an interrupted crawl:

markcrawl --base https://www.example.com --out ./output --resume --show-progress

Output

Each page becomes a .md file with a citation header:

# Getting Started

> URL: https://docs.example.com/getting-started
> Crawled: April 04, 2026
> Citation: Getting Started. docs.example.com. Available at: https://docs.example.com/getting-started [Accessed April 04, 2026].

Welcome to the platform. This guide walks you through installation...

Navigation, footer, cookie banners, and scripts are stripped. Only the main content remains.

All crawler CLI arguments

Argument	Description
--base	Base site URL to crawl
--out	Output directory
--format	markdown or text (default: markdown)
--show-progress	Print progress and crawl events
--render-js	Render JavaScript with Playwright before extracting
--concurrency	Pages to fetch in parallel (default: 1)
--proxy	HTTP/HTTPS proxy URL
--resume	Resume from saved state
--include-subdomains	Include subdomains under the base domain
--max-pages	Max pages to save; 0 = unlimited (default: 500)
--delay	Minimum delay between requests in seconds (default: 0, adaptive throttle adjusts automatically)
--timeout	Per-request timeout in seconds (default: 15)
--min-words	Skip pages with fewer words (default: 20)
--user-agent	Override the default user agent
--use-sitemap / --no-sitemap	Enable/disable sitemap discovery. Use --no-sitemap when you want to scrape a specific page or subsection — without it, large sites (YouTube, GitHub) may discover thousands of unrelated pages via their sitemap
--exclude-path	Glob pattern to exclude URL paths (e.g. '/job/*'). Can be repeated
--include-path	Glob pattern to include URL paths (e.g. '/blog/*'). Only matching paths are crawled. Can be repeated
--dry-run	Discover URLs (via sitemap/links) and print them without fetching content
--smart-sample	Auto-detect templated URL patterns and sample from large clusters instead of crawling every page
--sample-size	Pages to sample per templated cluster (default: 5, used with --smart-sample)
--sample-threshold	Clusters larger than this are sampled (default: 20, used with --smart-sample)
--auto-resume	Automatically resume if saved state exists, otherwise start fresh
--cross-dedup	Skip pages already seen in previous crawls to the same output directory
--prioritize-links	Score discovered links by predicted content yield — crawl high-value pages first
--extractor	Content extraction backend: default, trafilatura, ensemble, or readerlm
--download-images	Download images from the content area to assets/ and use local paths in Markdown
--min-image-size	Minimum image file size in bytes to keep (default: 5000). Smaller images are skipped
--i18n-filter	Skip URLs under locale path segments (/fr/, /de-DE/, /zh-Hans/, ...) — generic, no per-domain config
--title-at-top	Prepend # {title} to the text field of every JSONL row when not already present — top-MRR RAG recipe

Optional: structured extraction

If you need structured data (not just text), the extraction add-on uses an LLM to pull specific fields from each page.

pip install markcrawl[extract]

markcrawl-extract \
  --jsonl ./output/pages.jsonl \
  --fields company_name pricing features \
  --show-progress

Auto-discover fields across multiple crawled sites:

markcrawl-extract \
  --jsonl ./comp1/pages.jsonl ./comp2/pages.jsonl ./comp3/pages.jsonl \
  --auto-fields \
  --context "competitor pricing analysis" \
  --show-progress

Supports OpenAI, Anthropic (Claude), Google Gemini, and xAI (Grok) via --provider.

Extraction details

Provider and model selection

markcrawl-extract --jsonl ... --fields pricing --provider openai         # default
markcrawl-extract --jsonl ... --fields pricing --provider anthropic      # Claude
markcrawl-extract --jsonl ... --fields pricing --provider gemini         # Gemini
markcrawl-extract --jsonl ... --fields pricing --provider grok           # Grok
markcrawl-extract --jsonl ... --fields pricing --model gpt-4o           # override model

Provider	API key env var	Default model
OpenAI	OPENAI_API_KEY	gpt-4o-mini
Anthropic	ANTHROPIC_API_KEY	claude-sonnet-4-20250514
Google Gemini	GEMINI_API_KEY	gemini-2.0-flash
xAI (Grok)	XAI_API_KEY	grok-3-mini-fast

All extraction CLI arguments

Argument	Description
--jsonl	Path(s) to pages.jsonl — pass multiple for cross-site analysis
--fields	Field names to extract (space-separated)
--auto-fields	Auto-discover fields by sampling pages
--context	Describe your goal for auto-discovery
--sample-size	Pages to sample for auto-discovery (default: 3)
--provider	openai, anthropic, gemini, or grok
--model	Override the default model
--output	Output path (default: extracted.jsonl)
--delay	Delay between LLM calls in seconds (default: 0.25)
--show-progress	Print progress

Output format

Extracted rows include LLM attribution:

{
  "url": "https://competitor.com/pricing",
  "citation": "Pricing. competitor.com. Available at: ... [Accessed April 04, 2026].",
  "pricing_tiers": "Starter ($29/mo), Pro ($99/mo), Enterprise (contact sales)",
  "extracted_by": "gpt-4o-mini (openai)",
  "extraction_note": "Field values were extracted by an LLM and may be interpreted, not verbatim."
}

Optional: Supabase vector search (RAG)

Chunk pages, generate embeddings, and upload to Supabase with pgvector:

pip install markcrawl[upload]

markcrawl --base https://docs.example.com --out ./output --show-progress
markcrawl-upload --jsonl ./output/pages.jsonl --show-progress

Requires SUPABASE_URL, SUPABASE_KEY, and OPENAI_API_KEY. See docs/SUPABASE.md for table setup, query examples, and recommendations.

Optional: agent integrations

MarkCrawl includes integrations for AI agents. Each is an optional add-on.

MCP Server (Claude Desktop, Cursor, Windsurf)

pip install markcrawl[mcp]

{
  "mcpServers": {
    "markcrawl": {
      "command": "python",
      "args": ["-m", "markcrawl.mcp_server"]
    }
  }
}

Tools: crawl_site, list_pages, read_page, search_pages, extract_data

LangChain Tool

pip install markcrawl[langchain]

from markcrawl.langchain import all_tools
from langchain_openai import ChatOpenAI
from langchain.agents import initialize_agent, AgentType

agent = initialize_agent(tools=all_tools, llm=ChatOpenAI(model="gpt-4o-mini"),
                         agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION)
agent.run("Crawl docs.example.com and summarize their auth guide")

OpenClaw Skill (WhatsApp, Telegram, Slack)

npx clawhub install markcrawl-skill

See AIMLPM/markcrawl-clawhub-skill.

LLM assistant prompt

Copy the system prompt from docs/LLM_PROMPT.md into any LLM to get an assistant that generates correct MarkCrawl commands.

When NOT to use MarkCrawl

• Sites behind login/auth — no cookie or session support
• Aggressive bot protection (Cloudflare, Akamai) — no anti-bot evasion
• Millions of pages — designed for hundreds to low thousands; use Scrapy for scale
• PDF content — HTML only (PDF support is on the roadmap)
• JavaScript SPAs — add markcrawl[js] and use --render-js for React/Vue/Angular
• Infinite-scroll pages — --render-js renders the initial page load but does not scroll; you'll get the first screenful of content (e.g., ~28 of 82 YouTube videos). For complete listings, combine with the platform's API or RSS feed (e.g., YouTube's /feeds/videos.xml?channel_id=...)

Architecture

MarkCrawl is a web crawler. The optional layers (extraction, upload, agents) are separate add-ons that work with the crawler's output.

CORE (free, no API keys)              OPTIONAL ADD-ONS
┌──────────────────────────┐
│ 1. Discover URLs         │          markcrawl[extract]  — LLM field extraction
│    (sitemap or links)    │          markcrawl[upload]   — Supabase/pgvector RAG
│ 2. Fetch & clean HTML    │          markcrawl[js]       — Playwright JS rendering
│ 3. Write Markdown + JSONL│          markcrawl[mcp]      — MCP server for agents
│    + auto-citation       │          markcrawl[langchain] — LangChain tools
└──────────────────────────┘

For internals, see docs/ARCHITECTURE.md.

Extending MarkCrawl

from markcrawl import crawl

result = crawl("https://example.com", out_dir="./output")
print(f"Saved {result.pages_saved} pages")

# Process output in your own pipeline
import json
with open(result.index_file) as f:
    for line in f:
        page = json.loads(line)
        your_db.insert(page)  # Pinecone, Weaviate, Elasticsearch, etc.

# Use individual components
from markcrawl import chunk_text
from markcrawl.extract import LLMClient, extract_fields

See docs/ARCHITECTURE.md for the full module map and extensibility guide.

Cost

The core crawler is free. Two optional features have API costs:

Feature	Cost	When
Structured extraction	~$0.01-0.03 per page	markcrawl-extract
Supabase upload	~$0.0001 per page	markcrawl-upload

Setting up API keys

Only needed for extraction and upload. The core crawler requires no keys.

# .env — in your working directory
OPENAI_API_KEY="sk-..."           # extraction (--provider openai) + upload
ANTHROPIC_API_KEY="sk-ant-..."    # extraction (--provider anthropic)
GEMINI_API_KEY="AI..."            # extraction (--provider gemini)
XAI_API_KEY="xai-..."             # extraction (--provider grok)
SUPABASE_URL="https://..."        # upload
SUPABASE_KEY="eyJ..."             # upload (service-role key)

source .env

Project structure

.
├── README.md
├── LICENSE
├── PRIVACY.md
├── SECURITY.md
├── CONTRIBUTING.md
├── CODE_OF_CONDUCT.md
├── Dockerfile
├── Makefile
├── glama.json
├── pyproject.toml
├── requirements.txt
├── .github/
│   ├── pull_request_template.md
│   └── workflows/
│       ├── ci.yml
│       └── publish.yml
├── docs/
│   ├── ARCHITECTURE.md
│   ├── LLM_PROMPT.md
│   ├── MCP_SUBMISSION.md
│   ├── RAG_RETRIEVAL_RESEARCH.md
│   └── SUPABASE.md
├── tests/
│   ├── __init__.py
│   ├── test_chunker.py
│   ├── test_core.py
│   ├── test_extract.py
│   └── test_upload.py
└── markcrawl/
    ├── __init__.py
    ├── cli.py
    ├── core.py               # orchestrator
    ├── fetch.py              # HTTP/Playwright fetching
    ├── robots.py             # robots.txt parsing
    ├── throttle.py           # adaptive rate limiting
    ├── state.py              # crawl state & resume
    ├── urls.py               # URL normalization & filtering
    ├── extract_content.py    # HTML → Markdown conversion
    ├── dedup.py              # cross-crawl deduplication
    ├── link_scorer.py        # link prioritization
    ├── chunker.py
    ├── exceptions.py
    ├── utils.py
    ├── extract.py            # LLM field extraction
    ├── extract_cli.py
    ├── upload.py
    ├── upload_cli.py
    ├── langchain.py
    └── mcp_server.py

Roadmap

• Canonical URL support
• PDF support
• Authenticated crawling
• Multi-provider embeddings

Shipped features

• pip install markcrawl on PyPI
• 200 automated tests + GitHub Actions CI (Python 3.10-3.13) + ruff linting
• Markdown and plain text output with auto-citation
• Sitemap-first crawling with robots.txt compliance
• Text chunking with configurable overlap + semantic chunking
• Supabase/pgvector upload for RAG
• JavaScript rendering via Playwright
• Concurrent fetching and proxy support
• Resume interrupted crawls + auto-resume
• LLM extraction (OpenAI, Claude, Gemini, Grok) with auto-field discovery
• MCP server, LangChain tools, OpenClaw skill
• Image alt text preservation
• Python API (result.pages)
• Page-type extraction and content-region heuristics
• Multiple extraction backends (default, trafilatura, ensemble, ReaderLM-v2)
• Cross-crawl deduplication (--cross-dedup)
• Link prioritization by predicted content yield (--prioritize-links)
• Smart sampling of templated URL clusters (--smart-sample)
• URL path filtering (--include-path, --exclude-path) and dry-run preview

Contributing

See CONTRIBUTING.md. If you used an LLM to generate code, include the prompt in your PR.

Security

See SECURITY.md.

Privacy

MarkCrawl runs locally. No telemetry, no analytics, no data sent anywhere. See PRIVACY.md.

License

MIT. See LICENSE.