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Hyperbrowser is a platform for running and scaling headless browsers. It lets you launch and manage browser sessions at scale and provides easy to use solutions for any webscraping needs, such as scraping a single page or crawling an entire site. Key Features:
  • Instant Scalability - Spin up hundreds of browser sessions in seconds without infrastructure headaches
  • Simple Integration - Works seamlessly with popular tools like Puppeteer and Playwright
  • Powerful APIs - Easy to use APIs for scraping/crawling any site, and much more
  • Bypass Anti-Bot Measures - Built-in stealth mode, ad blocking, automatic CAPTCHA solving, and rotating proxies
This guide provides a quick overview for getting started with HyperbrowserLoader document loader. For more information about Hyperbrowser, please visit the Hyperbrowser website or if you want to check out the docs, you can visit the Hyperbrowser docs.

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Overview

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Integration details

ClassPackageLocalSerializableJS support
HyperbrowserLoaderlangchain-hyperbrowser❌❌❌

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Loader features

SourceDocument Lazy LoadingNative Async Support
HyperbrowserLoader✅✅

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Setup

To access Hyperbrowser document loader you’ll need to install the langchain-hyperbrowser integration package, and create a Hyperbrowser account and get an API key.

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Credentials

Head to Hyperbrowser to sign up and generate an API key. Once you’ve done this set the HYPERBROWSER_API_KEY environment variable:

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Installation

Install langchain-hyperbrowser. 
pip install -qU langchain-hyperbrowser

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Initialization

Now we can instantiate our model object and load documents: 
from langchain_hyperbrowser import HyperbrowserLoader

loader = HyperbrowserLoader(
    urls="https://example.com",
    api_key="YOUR_API_KEY",
)

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Load

docs = loader.load()
docs[0]

Document(metadata={'title': 'Example Domain', 'viewport': 'width=device-width, initial-scale=1', 'sourceURL': 'https://example.com'}, page_content='Example Domain\n\n# Example Domain\n\nThis domain is for use in illustrative examples in documents. You may use this\ndomain in literature without prior coordination or asking for permission.\n\n[More information...](https://www.iana.org/domains/example)')

print(docs[0].metadata)

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Lazy load

page = []
for doc in loader.lazy_load():
    page.append(doc)
    if len(page) >= 10:
        # do some paged operation, e.g.
        # index.upsert(page)

        page = []

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Advanced usage

You can specify the operation to be performed by the loader. The default operation is scrape. For scrape, you can provide a single URL or a list of URLs to be scraped. For crawl, you can only provide a single URL. The crawl operation will crawl the provided page and subpages and return a document for each page. 
loader = HyperbrowserLoader(
    urls="https://hyperbrowser.ai", api_key="YOUR_API_KEY", operation="crawl"
)
Optional params for the loader can also be provided in the params argument. For more information on the supported params, visit docs.hyperbrowser.ai/reference/sdks/python/scrape#start-scrape-job-and-wait or docs.hyperbrowser.ai/reference/sdks/python/crawl#start-crawl-job-and-wait. 
loader = HyperbrowserLoader(
    urls="https://example.com",
    api_key="YOUR_API_KEY",
    operation="scrape",
    params={"scrape_options": {"include_tags": ["h1", "h2", "p"]}},
)

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API reference

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