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

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

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

{
  "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..."
}

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

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

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

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

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

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

# 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...

pip install markcrawl[extract]

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

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

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."
}

pip install markcrawl[upload]

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

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

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
└──────────────────────────┘

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

# .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
├── 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