MarkCrawl by iD8 🕷️📝

Turn any 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 crawls a 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.

Quickstart (2 minutes)

pip install markcrawl
markcrawl --base https://httpbin.org --out ./demo --show-progress

Your ./demo folder now contains:

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

Each line in pages.jsonl:

{
  "url": "https://httpbin.org/",
  "title": "httpbin.org",
  "crawled_at": "2026-04-04T12:30:00Z",
  "citation": "httpbin.org. httpbin.org. Available at: https://httpbin.org/ [Accessed April 04, 2026].",
  "tool": "markcrawl",
  "text": "# httpbin.org\n\nA simple HTTP Request & Response Service..."
}

<details> <summary>How it compares to other crawlers</summary>

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.

See benchmarks/SPEED_COMPARISON.md for head-to-head performance data (3 tools, 4 sites, 3 iterations each). </details>

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[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.

<details> <summary>Install from source (for development)</summary>

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

</details>

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.

<details> <summary>All crawler CLI arguments</summary>

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
</details>

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.

<details> <summary>Extraction details</summary>

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."
}

</details>

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.

<details> <summary>MCP Server (Claude Desktop, Cursor, Windsurf)</summary>

pip install markcrawl[mcp]

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

Tools: crawl_site, list_pages, read_page, search_pages, extract_data </details>

<details> <summary>LangChain Tool</summary>

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")

</details>

<details> <summary>OpenClaw Skill (WhatsApp, Telegram, Slack)</summary>

npx clawhub install markcrawl-skill

See AIMLPM/markcrawl-clawhub-skill. </details>

<details> <summary>LLM assistant prompt</summary>

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

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 without --render-js — add markcrawl[js] for React/Vue/Angular

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

<details> <summary>Project structure</summary>

.
├── README.md
├── LICENSE
├── PRIVACY.md
├── SECURITY.md
├── CONTRIBUTING.md
├── CODE_OF_CONDUCT.md
├── Dockerfile
├── 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
│   └── SUPABASE.md
├── tests/
│   ├── test_core.py
│   ├── test_chunker.py
│   ├── test_extract.py
│   └── test_upload.py
└── markcrawl/
    ├── __init__.py
    ├── cli.py
    ├── core.py
    ├── chunker.py
    ├── exceptions.py
    ├── utils.py
    ├── extract.py
    ├── extract_cli.py
    ├── upload.py
    ├── upload_cli.py
    ├── langchain.py
    └── mcp_server.py

</details>

Roadmap

• [ ] Canonical URL support
• [ ] Fuzzy duplicate-content detection
• [ ] PDF support
• [ ] Authenticated crawling
• [ ] Multi-provider embeddings

<details> <summary>Shipped features</summary>

• pip install markcrawl on PyPI
• 102 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
• Supabase/pgvector upload for RAG
• JavaScript rendering via Playwright
• Concurrent fetching and proxy support
• Resume interrupted crawls
• LLM extraction (OpenAI, Claude, Gemini, Grok) with auto-field discovery
• MCP server, LangChain tools, OpenClaw skill </details>

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.