Github Models Catalog Scraper

Explore and extract model availability directly from the GitHub Models catalog in a clean, structured dataset format. Whether you need a quick overview of what models are available, want to filter out specific vendors for your evaluation planning, or need to keep a running inventory of model versions and output capabilities, this tool handles the heavy lifting so you can focus on building.

What it does

This actor fetches the public catalog in a single pass, maps each available model into a flat, organized row, and respects your item limit. If you want to narrow down your list by a particular publisher, the tool filters the results seamlessly after fetching the data.

Modes

• Catalog requests the public model listings and keeps up to your specified maximum item limit.

Input example

{
  "publisher": "OpenAI",
  "maxItems": 50,
  "requestDelaySecs": 1,
  "userAgent": "ActorResearch/1.0",
  "timeoutSecs": 300,
  "requestTimeoutSecs": 30,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}

Output example

{
  "modelId": "openai/gpt-4.1",
  "modelName": "GPT-4.1",
  "registry": "azure-openai",
  "publisher": "OpenAI",
  "summary": "Flagship OpenAI model for general chat and coding.",
  "rateLimitTier": "high",
  "htmlUrl": "https://github.com/marketplace/models/azure-openai/gpt-4.1",
  "modelVersion": "2025-04-14",
  "capabilities": ["chat", "tools"],
  "maxInputTokens": 1048576,
  "maxOutputTokens": 32768,
  "tags": ["openai"],
  "supportedInputModalities": ["text"],
  "supportedOutputModalities": ["text"],
  "scrapedAt": "2026-10-26T00:00:00+00:00"
}

The overview table highlights key properties including model identifier, readable name, vendor publisher, registry, summary description, model version, and source reference.

Use cases

• Model inventory maintain an up-to-date spreadsheet of all available catalog models and publishers.
• Publisher filter pull models from a single vendor to plan evaluations or benchmark tests.
• Capability audit inspect supported input/output modalities, token limits, and tags without manually reading through web documentation.
• Scheduled refresh run the actor on recurring Apify schedules to capture new model drops automatically.

Pricing and usage limits

This actor is lightweight and efficient, typically consuming minimal compute units per run depending on the catalog size and item caps. There are no hidden fees or extra requirements just configure your row limits and export your data directly.

FAQ

Who is this for?

Developers, researchers, and technical analysts who want structured GitHub Models catalog data neatly organized in a spreadsheet or dataset format.

What can I export?

You can easily export your collected data in JSON, CSV, Excel, and other standard Apify dataset formats.

Can I run it on a schedule?

Yes. You can configure Apify schedules to refresh your dataset automatically at whatever interval fits your workflow.

What happens when a request encounters an issue?

The run continues smoothly, logging an error row if needed rather than crashing midway through your collection.

Related tools

If you plan to map or join these rows, a geocoding pass or a quick CSV cleanup are the usual next steps. BigiByte also builds scrapers for other open-data portals when you need them as a separate job. Need another open dataset, a scheduled monitor, or a custom pipeline? BigiByte builds custom scrapers and APIs: https://bigibyte.com

Trademark Notice

GitHub Models and related names, logos, and trademarks belong to their respective owners. Their use in this documentation is solely to identify the source of the data and does not imply affiliation, sponsorship, endorsement, or partnership.

Disclaimer

This Actor is an independent tool and is not affiliated with, endorsed by, sponsored by, or otherwise officially associated with GitHub Models or its owners. All data is collected from publicly available sources. Users are responsible for complying with the source's terms of use, rate limits, and attribution or licensing requirements.

Is web scraping legal?

This actor extracts publicly accessible data without bypassing login screens, paywalls, or technical security layers. While collecting public web data is widely permitted, compliance depends on how data is gathered and used:

• Personal data: When collecting identifiers like names, emails, or public handles, ensure compliance with applicable data protection regulations such as GDPR or CCPA.
• Copyrighted content: Public text, descriptions, and metadata are often protected by intellectual property rights. Avoid directly duplicating or re-distributing full creative works without authorization.
• Platform terms & rate limits: Practice responsible data extraction by avoiding aggressive traffic spikes, adhering to sensible delays, and respecting published platform policies.
• Disclaimer: This summary serves informational purposes only and does not constitute formal legal counsel. Seek professional legal advice for specific compliance queries.

For a detailed guide, see Apify's legal overview: Is web scraping legal?.