matrx-scraper
Web scraping, HTML parsing, site crawling, headless-browser automation, and search for Python. An 8-stage parser turns raw HTML into clean AI-ready content plus structured extractions (tables, code blocks, categorized links, metadata, schema.org). It runs standalone with no database, and scales up to a full FastAPI microservice with a durable crash-safe crawl frontier.
Install
pip install matrx-scraper # core: HTTP fetch + parse + crawl + Brave Search
pip install "matrx-scraper[browser]" # + Playwright / curl_cffi for JS-rendered pages
pip install "matrx-scraper[metadata]" # + extruct (JSON-LD, microdata, RDFa, OpenGraph)
pip install "matrx-scraper[dedup]" # + MinHash / SimHash near-duplicate detection
pip install "matrx-scraper[pdf]" # + PyMuPDF
pip install "matrx-scraper[ocr]" # + Tesseract
pip install "matrx-scraper[db]" # + matrx-orm persistence
pip install "matrx-scraper[durable]" # + matrx-runtime crash-safe crawl frontier
pip install "matrx-scraper[server]" # + FastAPI microservice (includes browser, db, metadata, durable)
pip install "matrx-scraper[all]" # everything
Python 3.12+. Hard deps are a small set of well-known libraries (httpx,
beautifulsoup4, selectolax, markdownify, tldextract, tabulate,
croniter, python-dotenv) plus matrx-utils and matrx-files. Everything
heavy sits behind an extra so lean installs stay lean.
Quickstart
from matrx_scraper import scrape, scrape_many_stream, parse_html, audit_html
result = await scrape("https://example.com/article")
result.success # bool — outcome
result.failure_reason # str | None
result.title
result.ai_content # clean, AI-ready markdown
result.links # links by category
result.tables # parsed tables
result.organized_data # structured JSON of the page
# Many URLs — yields each page as it finishes, never a buffered batch.
async for page in scrape_many_stream(urls, concurrency=5):
print(page.url, page.success)
parsed = parse_html(open("page.html").read()) # no network
seo = audit_html(html, url) # full page evidence in one parse
Crawl a site
from matrx_scraper import crawl_site
async for page in crawl_site("https://example.com", max_pages=100):
print(page.url, page.title)
Drive a browser
from matrx_scraper.ai_browser import navigate, type_text, wait_for, screenshot, close_session
nav = await navigate("https://example.com/login", extract_text=True)
await type_text(nav.session_id, "input[name=email]", "user@example.com")
await wait_for(nav.session_id, selector="[data-ready]", timeout_ms=15_000)
shot = await screenshot(nav.session_id, full_page=True)
await close_session(nav.session_id)
Every action returns a Pydantic result with success: bool plus
error_type / error_message, so an agent loop recovers from timeouts and
selector misses without exception handling.
Other clients
BraveSearchClient (search), PsiClient (PageSpeed Insights v5), GscClient
(Google Search Console), compute_link_scores (PageRank over a link graph),
CustomExtractor (per-host CSS/XPath/regex/JSON-LD rules), quick_preview
(robots + homepage audit + screenshot). All pure — they return typed values and
persist nothing.
AI tools and MCP
matrx_scraper.ai_tools.ALL_TOOLS is 22 ToolSpec descriptors (14 browser, 4
scrape, 4 crawl), each with a full JSON Schema and an async handler — drop them
straight into an OpenAI/Anthropic tool call or any agent registry.
The bundled MCP server exposes the same list:
python -m matrx_scraper.mcp # stdio (canonical MCP transport)
python -m matrx_scraper.mcp --list # print registered tools and exit
python -m matrx_scraper.mcp --groups browser,scrape
stdio is the only transport. The MCP server has no network listener by design — one would hand a JS-executing browser to anyone who can reach the port. Network callers use the authenticated microservice below.
Running the microservice
# Docker (build context is the monorepo root, so sibling packages resolve)
cd packages/matrx-scraper && cp .env.example .env && docker compose up -d
# Or directly
pip install "matrx-scraper[server]" && playwright install chromium
matrx-scraper --port 8000
| Variable | Required | Purpose |
|---|---|---|
SUPABASE_MATRIX_{HOST,PORT,DATABASE_NAME,USER,PASSWORD} |
yes | The one connection, required. Both scraper.* (cache / domain config / retry queue) and the canonical web.* crawler bind here. Point these five at your own Postgres to run the scraper on its own database; there is never a second variable and never a fallback chain. |
SUPABASE_MATRIX_URL |
yes | Project URL; also derives the JWT JWKS URL |
SUPABASE_JWT_SECRET |
yes | HS256 compatibility (ES256 verifies via JWKS) |
DATACENTER_PROXIES |
for proxied fetches | Comma-separated pool. Missing or exhausted fails loudly — never a silent direct request that exposes the host IP. |
ADMIN_API_TOKEN |
for server-to-server | Shared secret for the approved-server identity |
BRAVE_API_KEY |
for search | |
AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY / AWS_REGION / AWS_S3_DEFAULT_BUCKET |
for the canonical crawler | Artifact bytes via matrx-files |
PORT / WORKERS / LOG_LEVEL |
no | Defaults 8000 / 1 / info |
Everything mounts under /api/scraper/: quick-scrape · batch · search ·
search-and-scrape · browser-fetch · browser/* (16 session endpoints) ·
preview · content/save · queue/* · config/domains · crawler/*.
GET /health (reports the installed version) and GET /health/ready are public;
everything else takes Authorization: Bearer <supabase-jwt>.
Embed it in another FastAPI app instead:
from matrx_scraper.server import create_app, ServerConfig
app = create_app(ServerConfig.from_env()) # DB comes from SUPABASE_MATRIX_*, never a config URL
Extending it in a host
The crawler takes four injection points; defaults are in-memory / no-op / discard, so nothing is required to get started:
| Protocol | Default | Swap in |
|---|---|---|
CrawlEventSink |
NoopEventSink |
your durable event writer |
QueueBackend |
InMemoryQueueBackend |
the [durable] runtime frontier — survives restarts |
BodyPersister |
discard | your storage |
RecipeBackend |
StaticRecipeBackend(DEFAULT_RECIPES) |
your per-host Playwright playbooks |
Host objects (cache, domain config, browser pool, file manager, work-queue
factory) are handed over with matrx_scraper.configure_ext(...); DB models bind
to an existing matrx-orm pool with matrx_scraper.configure_db("<db name>").
Documentation
| Doc | For |
|---|---|
matrx_scraper/FEATURE.md |
Engine contract: SSRF gates, proxy rules, browser runtimes, tool surface, auth, persistence |
matrx_scraper/web_crawl/FEATURE.md |
The canonical site crawler |
CLAUDE.md |
Contributing to the package |
License
MIT. Developed in the aidream monorepo.
1 maintainer
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