Homebrew Formulae Scraper - Package Metadata

Scrape Homebrew formula metadata with formula name, description, homepage, license, stable version, dependencies and install analytics. Export results to JSON, CSV, Excel, webhooks, Make, Zapier, n8n, Google Sheets or your own API.

This actor is part of a portfolio of lightweight public-data scrapers designed for reliable scheduled runs. It uses the public Homebrew Formulae endpoint directly, so it does not need a browser, residential proxy, login session or API key. That makes it inexpensive to run and suitable for Apify Store daily auto-tests.

What does this actor do?

The actor searches Homebrew Formulae for a keyword such as python and normalizes the returned records into a flat dataset. The exact fields depend on the registry, but the output is designed to preserve the commercially useful metadata: names, descriptions, owners/authors, popularity counters, download counts, versions, URLs, tags, licenses and source-specific identifiers.

Input

{
  "query": "python",
  "maxResults": 25
}

Use broad terms for market mapping and exact terms for monitoring. For example, developer-tool companies can search for a framework or technology category, agencies can monitor plugins and themes around a vertical, and researchers can watch package ecosystems over time.

Output

Each output item includes a stable package or record identifier, the source name and the search query. A typical record looks like this:

{
  "package_id": "example",
  "name": "example",
  "description": "Example description",
  "source": "Homebrew Formulae",
  "search": "python"
}

The actor keeps the output intentionally flat so it works well in spreadsheets, BI tools and enrichment pipelines.

Use cases

Developer-relations teams can identify projects and ecosystems that match their product. SaaS companies can find integration, migration and partnership opportunities. Security teams can monitor widely used packages or plugins. Market researchers can track popularity, downloads and version activity over time. Agencies can build lead lists from technology adoption signals.

Commercial value

Public registry metadata is a buying-intent signal. A project using a package, plugin, formula, module or theme often has an active maintainer or company behind it. Combine this output with website/contact enrichment to turn package metadata into sales research, partner discovery, competitive tracking or ecosystem intelligence.

Scheduling

Create a saved task for each keyword and run it weekly. Over time, you can compare new results, download counters, versions and metadata changes. Webhooks can send new records to your CRM, spreadsheet or database.

Reliability

The actor uses direct HTTP requests with retries and small defaults. It avoids fragile browser automation and anti-bot-heavy sites. If the source API returns a temporary error, the actor retries before finishing.

Pricing

This actor uses pay-per-result pricing. You pay only for records written to the default dataset, plus a small actor start event.

FAQ

Does it need an API key?

No. It uses public endpoints from Homebrew Formulae.

Can I run it daily?

Yes. The actor is built for scheduled monitoring and small reliable runs.

Can I enrich the output?

Yes. Combine URLs, names and package IDs with website contact extraction, GitHub enrichment or company enrichment actors.

Is this useful for lead generation?

Yes, especially for developer tools, agencies, security products, hosting providers, integration vendors and technical consultants.

You might also like

• PyPI Scraper
• Docker Hub Scraper
• RemoteOK Jobs Scraper
• Website Contact Extractor

Keywords

Homebrew Formulae scraper, metadata scraper, developer tools data, package registry scraper, technology intelligence, open source intelligence, lead generation data, Apify scraper.

Data quality and field coverage

The actor preserves source-specific metadata instead of forcing every source into a thin generic schema. This is important because commercial value often lives in the details: download counters, active installs, ratings, verified flags, repository URLs, author names, version numbers, dependency lists, license fields and homepage links. When a source does not provide a field, the actor leaves it empty rather than inventing data.

For analytics workflows, keep the source and search fields in your exports. They make it easy to combine multiple runs, compare several keywords and trace each row back to its origin. For CRM workflows, use the package name, homepage, repository or author fields as enrichment starting points.

Recommended workflow

Start with a broad search to understand the market, then create separate saved tasks for high-value subcategories. For example, a security vendor might track authentication, secrets, OAuth, SSO and vulnerability terms separately. A hosting provider might track database, queue, cache, deployment and observability terms. A developer-relations team might track framework names, competitor names and integration keywords.

After the first export, deduplicate records by package ID and enrich only the rows that matter. This keeps downstream costs low and makes the dataset more useful for sales, partnerships, research and content planning.

Why run this on Apify?

Apify gives you scheduling, API access, datasets, webhooks, integrations and pay-per-result billing in one place. You can run the actor manually, trigger it from your backend, schedule it weekly, send results to a webhook or connect it to no-code tools. That makes this actor useful as both a quick data export and a small building block inside a larger data pipeline.

Maintenance approach

This actor intentionally avoids scraping brittle private pages. It targets public endpoints that are stable, lightweight and fast. That makes it more reliable than a browser-based scraper and keeps run costs predictable. If the source changes its response shape, the actor can usually be updated with a small parser change rather than a full anti-bot rebuild.