MarkCrawl by iD8 🕷️📝

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

Latest: v0.11.1 (2026-05-12) — default aggregator URL filter. See What's New below.

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.

What's New

Install or upgrade with pip:

pip install --upgrade markcrawl
pip show markcrawl | grep Version       # confirm the installed version
markcrawl --help | head -1              # confirm the binary on $PATH is the upgraded one

If markcrawl --help is missing flags you expect (e.g. --screenshot, --seed-file, --smart-sample, --download-images), your local install is stale. Run pip install --upgrade markcrawl against the same Python that owns the markcrawl binary on your PATH — head -1 $(which markcrawl) shows the right interpreter. PyPI is always the source of truth; see CHANGELOG.md for the full release history.

v0.11 highlights (changelog):

• Aggregator URL filter (default, v0.11.1) — rejects mdBook /print.html and Hugo /_print/ pages during crawl-time URL filtering. These bundle the entire docs tree on a single URL and otherwise dominate retrieval rankings on cosine similarity (markcrawl was returning them in 49% of rust-book and 39% of kubernetes-docs top-5 retrieval slots before the fix; competitors return 0%). Opt out via include_aggregator_pages=True / --include-aggregators.
• Binary downloads (v0.11.0) — new download_types=["pdf", "docx"] kwarg streams referenced files to <out_dir>/downloads/ with size + content-type guards. Pre-fetch download_filter callback receives URL + anchor text + parent-page context; reject candidates before any HTTP bytes transfer.
• Local embedder is the default since v0.10.1 — pip install markcrawl ships the full ML stack (torch + transformers + sentence-transformers). Zero API key required for embedding. Override with MARKCRAWL_EMBEDDER=text-embedding-3-small or the embedding_model kwarg if you want OpenAI back.
• Tenacity-backed HTTP retry — full-jitter exponential backoff (2 s → 30 s, 5 attempts) that honors the server's Retry-After header on 429s.

Where markcrawl stands on the public benchmark, honestly. The independent llm-crawler-benchmarks v1.4 leaderboard measures 7 web crawlers on how well their output supports RAG. Markcrawl ranks 1st on cost ($4,505/yr at 100,000-page scale) but 7th of 7 on answer quality (3.77/5) and retrieval accuracy (MRR 0.341 vs leaders at 0.76). We're actively working to close that gap on three fronts:

1. v0.11.1 (just shipped) filters out /print.html and /_print/ "whole-book-on-one-page" URLs that were stealing 39–49% of markcrawl's top-5 retrieval slots on documentation sites. Competitors already filter these. Expected MRR improvement: +0.02 to +0.04 on docs-heavy sites (formal measurement pending the next benchmark cycle).
2. Upcoming releases improve how markcrawl chooses which pages to crawl within its budget — markcrawl's deliberately-narrower crawl strategy (which keeps cost low and signal-to-noise high) is also the main cause of the retrieval gap.
3. The benchmark itself is being improved — v1.4's test questions were sampled from one specific crawler's output, which structurally penalizes any crawler whose discovery strategy differs from that anchor. The benchmark is being updated so each site's test questions come from the site's own sitemap, independent of any crawler. We expect this fix alone to surface ~5–10% of markcrawl's current "misses" as actually correct answers at different URLs — work shown in our audit notes.

Goal for the next benchmark cycle: move from 7th to mid-pack on retrieval (+0.10 to +0.20 MRR) and answer quality, while keeping the cost-efficiency lead. Honest, measured progress — we publish the numbers either way.

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..."
}

Schema — every page in pages.jsonl has these fields:

Common Recipes

Runnable examples for the most common patterns:

• Single-page scrapes — including JS-rendered pages (React, Vue, YouTube)
• Whole-site crawls — docs, blogs, subsections; resume interrupted runs
• URL filtering — --exclude-path, --include-path, --dry-run, smart sampling
• Extraction backends — BS4 (default), trafilatura, ensemble, ReaderLM-v2
• Binary downloads — images, PDFs (with pre-fetch filter callbacks), DOCX
• Screenshots — full-page or cropped, PNG or JPEG
• End-to-end use cases — competitive analysis, RAG chatbot, API-docs → code-gen

Full recipes with copy-paste commands and expected outputs: docs/RECIPES.md.

Installation

pip install markcrawl                # Core crawler + chunker + local embedder
                                     # (no API keys required for embedding)

Optional add-ons (tasks beyond the crawl-and-embed core):

pip install markcrawl[js]            # + JavaScript rendering (Playwright)
pip install markcrawl[extract]       # + LLM extraction (OpenAI, Claude, Gemini, Grok)
pip install markcrawl[upload]        # + Supabase upload integration
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.

Lean install (skip the local-embedder dep stack — you'll need an OPENAI_API_KEY and pass embedding_model="text-embedding-3-small" for any embedding work):

pip install --no-deps markcrawl beautifulsoup4 lxml markdownify requests certifi tenacity

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.

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.

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

{
  "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.

pip install markcrawl[mcp]

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

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")

npx clawhub install markcrawl-skill

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:

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

.
├── 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

Project info

• Contributing — see CONTRIBUTING.md. If you used an LLM to generate code, include the prompt in your PR.
• Security — see SECURITY.md for the disclosure policy.
• Privacy — MarkCrawl runs locally. No telemetry, no analytics, no data sent anywhere. See PRIVACY.md.
• License — MIT. See LICENSE.