scrapit-scraper 0.1.0

YAML-driven web scraper framework with AI agent integrations

Scrapit

A modular, YAML-driven web scraper framework. Describe any scraping target in a config file — Scrapit handles fetching, parsing, transforming, validating, and storing the data.

No code required for new targets. Just write a YAML.

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Features

Feature	Description
YAML directives	Declarative scrape configs — selectors, transforms, validation, cache
Two backends	BeautifulSoup (fast, static) or Playwright (JS-rendered)
Fallback selectors	Per-field list of CSS selectors tried in order
all: true	Extract all matches for a selector, not just the first
Pagination	Follow "next page" links automatically
Spider mode	Discover and scrape all linked pages from an index
Multi-site	Scrape multiple URLs with the same spec in one directive
Transform pipeline	Declarative field transforms: strip, regex, float, replace, split…
Validation	Per-field rules: required, type, min/max, pattern, enum
Four output backends	JSON, CSV (append), SQLite (zero-config), MongoDB
HTTP cache	File-based cache with TTL — avoid re-fetching during dev
Change detection	Diff result against previous run, fire webhook on change
Webhook notifications	POST JSON payload to any URL when changes detected
Stats reporter	Field coverage %, timing, error count per run
Hook system	Register callbacks for scrape lifecycle events
Async queue	RabbitMQ producer/consumer for background processing
Structured logging	Console + output/scraper.log

---

Installation

git clone <repo-url>
cd scrapit

python3 -m venv .venv
source .venv/bin/activate

pip install -r requirements.txt

# Only if you use the playwright backend:
playwright install chromium

Copy and fill in your credentials:

cp scraper/.env.example .env

# MongoDB (optional)
MONGO_URI=mongodb+srv://user:pass@cluster/
MONGO_DATABASE=mydb
MONGO_COLLECTION=scraped

# RabbitMQ (optional)
RABBITMQ_HOST=localhost
RABBITMQ_PORT=5672
RABBITMQ_USER=guest
RABBITMQ_PASS=guest

# Webhook notifications (optional)
SCRAPIT_WEBHOOK_URL=https://hooks.example.com/...

---

Quick Start

# scrape Wikipedia, save to JSON
python -m scraper.main scrape wikipedia --json

# scrape Hacker News (paginated), save to SQLite
python -m scraper.main scrape hn --sqlite

# spider Books to Scrape, preview only
python -m scraper.main scrape books --preview

# scrape all directives in the default folder
python -m scraper.main batch --json

# list available directives
python -m scraper.main list

---

CLI Reference

scrape — single directive

python -m scraper.main scrape <directive> [--json|--csv|--sqlite|--mongo] [--preview] [--diff]

<directive> can be a name (wikipedia), filename (wikipedia.yaml), or path.

Flag	Description
--json	Save to output/<name>.json (default)
--csv	Append to output/<name>.csv
--sqlite	Save to output/scrapit.db
--mongo	Save to MongoDB
--excel	Append to output/<name>.xlsx
--preview	Print result, do not save
--diff	Compare with previous JSON output and show changes

batch — all directives in a folder

python -m scraper.main batch [folder] [--json|--csv|--sqlite|--mongo|--excel] [--preview] [--diff]

Default folder: scraper/directives/

list — inspect directives

python -m scraper.main list [--dir path/to/folder]

Shows site, backend, fields, transforms, validation rules, and cache config.

query — read stored data

# recent scrapes from SQLite
python -m scraper.main query --backend sqlite --limit 10

# filter by directive name
python -m scraper.main query --directive wikipedia

# filter by URL fragment
python -m scraper.main query --url wikipedia.org

cache — manage HTTP cache

python -m scraper.main cache stats        # show cache size and entry count
python -m scraper.main cache clear        # delete all cached responses
python -m scraper.main cache invalidate --url https://example.com

---

Writing Directives

Minimal directive

site: https://example.com
use: beautifulsoup   # or playwright

scrape:
  field_name:
    - 'css-selector'
    - attr: text     # 'text' = inner text, or any HTML attribute (href, src, …)

All directive options

site: https://example.com
use: beautifulsoup       # or playwright

# ── Mode ─────────────────────────────────────────────────────────────────────
mode: single             # single (default) | spider

# ── Multiple sites (same scrape spec applied to each) ────────────────────────
sites:
  - https://example.com/page-1
  - https://example.com/page-2

# ── Request options ───────────────────────────────────────────────────────────
retries: 3               # HTTP retries with exponential backoff (bs4)
timeout: 15              # seconds (bs4) or milliseconds (playwright)
headers:                 # extra HTTP headers
  Authorization: Bearer xxx
cookies:                 # bs4: dict  |  playwright: list of {name,value,domain}
  session_id: abc123
proxy: http://proxy:8080

# Proxy with authentication:
# proxy: http://user:password@proxy:8080
# proxy: https://user:password@proxy:8080
# proxy: socks5://user:password@proxy:1080

# Using environment variable (set in .env):
# proxy: ${PROXY_URL}

# ── Cache ─────────────────────────────────────────────────────────────────────
cache:
  ttl: 3600              # seconds (0 = disabled)

# ── Playwright-only ───────────────────────────────────────────────────────────
wait_for: '#content'     # wait for selector before parsing
screenshot: true         # save full-page screenshot to output/

# ── Scrape spec ───────────────────────────────────────────────────────────────
scrape:
  title:
    - 'h1'               # single selector
    - attr: text

  image:
    - ['img.hero', 'img.main', 'img']   # fallback selectors
    - attr: src

  all_links:
    - 'a.result'
    - attr: href
      all: true          # return list of all matches

# ── Pagination (bs4 only) ─────────────────────────────────────────────────────
paginate:
  selector: 'a.next-page'
  attr: href
  max_pages: 5

# ── Spider mode (bs4 only) ────────────────────────────────────────────────────
follow:
  selector: 'a.article-link'
  attr: href
  max: 50                # max pages to scrape
  same_domain: true      # stay on same domain

# ── Transform pipeline ────────────────────────────────────────────────────────
transform:
  price:
    - strip
    - {replace: {"€": "", ",": "."}}
    - float
  title:
    - strip
    - upper
  description:
    - strip
    - {slice: {end: 200}}
  tags:
    - {split: ","}
    - first

# ── Validation ────────────────────────────────────────────────────────────────
validate:
  title:
    required: true
    min_length: 2
    max_length: 500
  price:
    type: float
    min: 0
  status:
    in: [active, inactive, pending]

# ── Notifications ─────────────────────────────────────────────────────────────
notify:
  webhook: https://hooks.slack.com/...   # called when --diff detects changes

Available transforms

Transform	Argument	Description
strip	—	Strip whitespace
lower / upper / title	—	Change case
int / float	—	Parse number (removes non-numeric chars)
regex	pattern	Extract first regex match
regex_group	{pattern, group}	Extract specific capture group
replace	{old: new}	String substitution (multiple pairs)
split	","	Split string into list
join	", "	Join list into string
first / last	—	Pick first/last item from list
default	value	Fallback if value is None
slice	{start, end} or N	Substring / sublist
prepend / append	"str"	Add text before/after
remove_tags	—	Strip HTML tags
template	"prefix {value}"	String template with {value} or {other_field}

Available validation rules

Rule	Example	Description
required	true	Must not be None
type	float	Type check: str, int, float, list, bool
not_empty	true	Must not be empty string/list
min / max	0 / 1000	Numeric range
min_length / max_length	2 / 500	String/list length
pattern	^\d{4}$	Regex must match
in	[a, b, c]	Value must be in enum
not_in	[a, b, c]	Value must NOT be in enum

---

Output

All outputs go to output/ at the project root.

File	Description
output/<name>.json	Last scrape as JSON
output/<name>.csv	All scrapes in append-mode CSV
output/scrapit.db	SQLite database with all scrapes
output/scraper.log	Full log (also printed to console)
output/<name>_<ts>.png	Screenshot (Playwright + screenshot: true)

---

Project Structure

scrapit/
  scraper/
    main.py                   CLI entry point (scrape/batch/list/query/cache)
    config.py                 Environment variables and paths
    logger.py                 Logging → console + output/scraper.log
    hooks.py                  Lifecycle hook registry
    reporter.py               Timing and field coverage stats
    directives/               Built-in example directives
      wikipedia.yaml
      hn.yaml                 Hacker News (paginated)
      books.yaml              Books to Scrape (spider mode)
      github_trending.yaml    GitHub trending (all: true)
    scrapers/
      __init__.py             Pipeline dispatcher
      bs4_scraper.py          BeautifulSoup backend
      playwright_scraper.py   Playwright backend
      paginator.py            Pagination support
      spider.py               Spider / link-following
    transforms/
      __init__.py             Transform pipeline engine
    validators/
      __init__.py             Validation engine
    storage/
      mongo.py                MongoDB (lazy connect)
      json_file.py            JSON output
      csv_file.py             CSV output (append)
      sqlite.py               SQLite (zero-config)
      diff.py                 Change detection
    cache/
      __init__.py             HTTP cache with TTL
    notifications/
      __init__.py             Webhook notifications
    queue/
      producer.py             RabbitMQ producer
      consumer.py             RabbitMQ consumer
  output/                     Generated data (gitignored)
  .cache/                     HTTP cache (gitignored)
  requirements.txt
  .env

---

Hook System

Register Python callbacks for scrape lifecycle events:

from scraper import hooks

@hooks.on("after_scrape")
def log_result(result, dados):
    print(f"scraped {result['url']} — {len(result)} fields")

@hooks.on("on_change")
def alert(changes, result):
    print(f"change in {result['url']}: {list(changes.keys())}")

@hooks.on("on_error")
def handle_error(exc, dados):
    print(f"failed on {dados['site']}: {exc}")

Available events: before_scrape, after_scrape, on_error, on_save, on_change

---

AI Agent Integrations

Scrapit integrates natively with every major AI agent framework. Give any agent the ability to scrape the web on demand — no boilerplate required.

MCP Server (Claude Desktop, Cursor, Claude Code)

The fastest way to add Scrapit to Claude:

# Claude Code
claude mcp add scrapit -- python -m scraper.integrations.mcp

For Claude Desktop, add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "scrapit": {
      "command": "python",
      "args": ["-m", "scraper.integrations.mcp"],
      "cwd": "/path/to/scrapit"
    }
  }
}

After adding, Claude will have 4 web scraping tools available automatically.

Anthropic SDK (native tool use)

import anthropic
from scraper.integrations.anthropic import as_anthropic_tools, handle_tool_call

client = anthropic.Anthropic()
tools  = as_anthropic_tools()

response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=1024,
    tools=tools,
    messages=[{"role": "user", "content": "What are the top posts on Hacker News?"}],
)

for block in response.content:
    if block.type == "tool_use":
        result = handle_tool_call(block.name, block.input)

# Or use the built-in agent loop:
from scraper.integrations.anthropic import ScrapitAnthropicAgent

agent = ScrapitAnthropicAgent(model="claude-opus-4-6")
answer = agent.run("Summarize the top 3 Hacker News posts today.")

LangChain / CrewAI / LangGraph

from scraper.integrations.langchain import ScrapitToolkit
from langchain.agents import initialize_agent, AgentType
from langchain_openai import ChatOpenAI

tools = ScrapitToolkit().get_tools()
# → [ScrapitTool, ScrapitPageTool, ScrapitSelectorTool]

agent = initialize_agent(
    tools=tools,
    llm=ChatOpenAI(model="gpt-4o"),
    agent=AgentType.OPENAI_FUNCTIONS,
)
agent.run("What does the Wikipedia article on Python say?")

Works with CrewAI — pass ScrapitToolkit().get_tools() to any Agent(tools=[...]).

OpenAI SDK (function calling)

from openai import OpenAI
from scraper.integrations.openai import as_openai_functions, handle_function_call

client = OpenAI()
tools  = as_openai_functions()

response = client.chat.completions.create(
    model="gpt-4o", tools=tools,
    messages=[{"role": "user", "content": "Scrape the top GitHub trending repos."}],
)

# Or use the built-in agent loop:
from scraper.integrations.openai import ScrapitOpenAIAgent

agent = ScrapitOpenAIAgent(model="gpt-4o")
answer = agent.run("What are the trending Python repos on GitHub today?")

LlamaIndex (RAG pipelines)

from scraper.integrations.llamaindex import ScrapitReader
from llama_index.core import VectorStoreIndex

reader = ScrapitReader()
docs   = reader.load_data(urls=["https://site1.com", "https://site2.com"])  # parallel

index  = VectorStoreIndex.from_documents(docs)
engine = index.as_query_engine()
response = engine.query("Summarize the main points.")

Quick programmatic API (no YAML needed)

from scraper.integrations import scrape_url, scrape_page, scrape_with_selectors, scrape_many

# Clean text — ready to feed to an LLM
text = scrape_url("https://news.ycombinator.com")

# Structured metadata: title, description, links, word_count
page = scrape_page("https://example.com")

# Agent-defined extraction with CSS selectors — no YAML needed
data = scrape_with_selectors(
    "https://books.toscrape.com/catalogue/a-light-in-the-attic_1000",
    selectors={"title": "h1", "price": "p.price_color"},
)

# Parallel scraping
pages = scrape_many(["https://a.com", "https://b.com"], mode="page")

# Run a directive and get structured data
data = scrape_directive("wikipedia")

Optional dependencies

All integration dependencies are lazy — Scrapit works without any of them installed. Install only what you need:

pip install anthropic          # Anthropic SDK integration
pip install openai             # OpenAI integration
pip install langchain-core     # LangChain / CrewAI / LangGraph
pip install llama-index-core   # LlamaIndex
pip install mcp                # MCP server (Claude Desktop / Cursor / Claude Code)

---

Async Queue (RabbitMQ)

Send a directive to the background queue:

from scraper.queue.producer import call_producer
call_producer("directives/wikipedia.yaml")

Start a consumer worker:

python -m scraper.queue.consumer

Workers scrape each received directive and save to MongoDB automatically.

---

Programmatic Usage

import asyncio
from scraper.scrapers import grab_elements_by_directive
from scraper.storage import json_file

result = asyncio.run(grab_elements_by_directive("scraper/directives/wikipedia.yaml"))
json_file.save(result, "wikipedia")

---

Contributing

Contributions are welcome! Whether it's a bug fix, a new transform, a new storage backend, or just sharing a directive YAML that works for a site you scraped.

See CONTRIBUTING.md for a full guide on how to get started.

Quick ways to contribute:

• Share a directive — open an issue with the "Share a Directive" template
• New transform — add a function to scraper/transforms/__init__.py and open a PR
• Bug report — use the bug report issue template

---

Requirements

• Python 3.10+
• requests, bs4, pyyaml — always required
• playwright — only for playwright backend
• pymongo, python-dotenv — only for MongoDB
• pika — only for RabbitMQ queue
• SQLite is included in Python's stdlib (no install needed)

---

License

MIT © João Benedet Machado

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Proxy Configuration

You can use proxies in your directives to route requests through proxy servers.

Basic Usage

site: example.com
use: bs4

scrape:
  url: https://example.com/products
  items:
    selector: ".product"
    fields:
      name:
        selector: "h3"
        method: text

# Proxy configuration
proxy:
  enabled: true
  url: "http://proxy.example.com:8080"
  # Or use environment variables
  # url: "${HTTP_PROXY}"

Using with Environment Variables

proxy:
  enabled: true
  url: "${HTTP_PROXY}"  # Reads from HTTP_PROXY or HTTPS_PROXY env var

Proxies for Different Protocols

proxy:
  http: "http://http-proxy.example.com:8080"
  https: "https://https-proxy.example.com:8080"

Rotating Proxies

proxy:
  enabled: true
  rotate: true
  proxies:
    - "http://proxy1.example.com:8080"
    - "http://proxy2.example.com:8080"

Common Proxy Providers

• SmartProxy - Residential proxies
• Oxylabs - Enterprise proxies
• ScraperAPI - API with proxy rotation
• ScrapingBee - Headless browser with proxies

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Best Practices

1. Use residential proxies for sensitive sites
2. Rotate proxies to avoid blocks
3. Set appropriate delay between requests
4. Monitor proxy health and replace dead proxies