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Scrape framework/package documentation and generate an AGENTS.md knowledge file.

Project description

kgrab logo

Scrape any framework's documentation and generate an AGENTS.md knowledge file for AI agents.

PyPI GitHub Stars License


What is kgrab?

kgrab crawls the full documentation of any framework or package and generates a structured AGENTS.md file — a knowledge base that makes AI agents aware of all the features, APIs, and patterns of a library.

It works as a CLI tool, a pip package, and an MCP server (Model Context Protocol) so AI assistants like Claude, Gemini, or Cursor can call it as a tool.


Quick Start

pip install kgrab

CLI usage

# Scrape docs and generate AGENTS.md (default max 500 pages)
doc-scrape https://docs.agno.com/introduction

# Compact mode (70% smaller output)
doc-scrape https://docs.agno.com/introduction --compact

# Full scrape (ignores the 500 max pages limit)
doc-scrape https://docs.agno.com/introduction --full

# Custom output path and package name
doc-scrape https://docs.agno.com/introduction -o agno_AGENTS.md -n "Agno" --compact

MCP server usage

Add to your MCP client config (Claude Desktop, Gemini CLI, Cursor, etc.):

{
  "mcpServers": {
    "kgrab": {
      "command": "doc-scrape-mcp"
    }
  }
}

The MCP server exposes two tools:

Tool Description
scrape_documentation Scrapes docs and saves AGENTS.md to disk
scrape_documentation_to_text Scrapes docs and returns the markdown content directly

CLI Options

Flag Default Description
url (required) Entry-point URL of the documentation
-o / --output AGENTS.md Output file path
-n / --name auto-detect Human-friendly package name
--max-pages 500 Maximum pages to scrape
--full off Scrape all reachable pages (ignores --max-pages)
--delay 0.25 Seconds between HTTP requests
-c / --compact off Compress output: truncate prose, cap code, remove thin pages
-v / --verbose off Enable debug logging

Compact Mode

Large documentation sites can produce huge AGENTS.md files (200K+ tokens). Use --compact to aggressively reduce size:

What it does Effect
Removes thin/empty pages Drops index and redirect pages
Deduplicates by title Keeps first occurrence only
Strips boilerplate Removes copyright, "Was this helpful?" etc.
Truncates prose 2 sentences per section
Caps code blocks 10 lines max, first block per section only
Cleans titles Strips redundant site-name suffixes

Example: Agno docs (500 pages) goes from ~247K tokens → ~75K tokens (70% reduction).


Tested Frameworks & Layouts

kgrab handles HTML layouts generated by diverse tooling (Docusaurus, VitePress, Sphinx, custom, etc.). Here is a smoke test across 10 sites using --compact:

Framework Pages* Sections Est. Tokens Size Status
FastAPI 15 327 ~2.4k 9.4 KB
Flask 15 115 ~14.8k 57.9 KB
React 15 207 ~13.8k 54.2 KB
Vue.js 15 160 ~15.4k 60.4 KB
Next.js 15 53 ~3.2k 12.5 KB
Pydantic 15 283 ~17.3k 67.8 KB
Tailwind 15 159 ~7.0k 27.7 KB
Stripe 15 168 ~11.6k 45.5 KB
Hono 15 116 ~7.4k 29.0 KB
LangChain 1 1 ~168 0.7 KB ⚠️

* Capped at 15 pages for testing speed. Run with --full to scrape all pages.

⚠️ Known Limitation: Single Page Apps (SPAs)

kgrab relies on standard HTTP fetching (requests) and does not evaluate client-side JavaScript. Sites like LangChain's python docs that load content purely dynamically via JS will only yield the initial rendered HTML (often a single blank page). Supporting JS-heavy sites via a headless browser may be considered depending on use case.


How It Works

  1. Crawl — starting from the given URL, kgrab follows internal navigation links (sidebar, next/prev, etc.) and collects all reachable documentation pages under the same domain scope.
  2. Extract — for each page it extracts headings, body text, and code examples while discarding chrome (nav, footer, scripts).
  3. Generate — the collected content is assembled into a structured AGENTS.md with a table of contents, per-page sections, and inline code blocks.

Development

git clone https://github.com/Bonhollow/kgrab.git
cd kgrab
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest -v

License

Apache 2.0

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