# Github Models Catalog Scraper (`bigibyte/github-models-catalog-scraper`) Actor

Collect models available on GitHub Models from the public catalog. Filter by publisher, set row caps, and export clean JSON, CSV, or Excel datasets.

- **URL**: https://apify.com/bigibyte/github-models-catalog-scraper.md
- **Developed by:** [BigiByte](https://apify.com/bigibyte) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.99 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## GitHub Models Catalog Scraper

![GitHub Models Catalog Scraper cover](https://images.bigibyte.com/cdn-cgi/image/width=1260,height=540,fit=cover,format=webp/apify-actors-covers/github-models-catalog-scraper-cover.png)

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

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

```

### Output example

```json
{
  "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?](https://blog.apify.com/is-web-scraping-legal/).

# Actor input Schema

## `publisher` (type: `string`):

Optional publisher filter (case-insensitive substring). Leave empty to keep all publishers.

## `maxItems` (type: `integer`):

Stop after this many successful rows. Hard max is 1000.

## `requestDelaySecs` (type: `number`):

Seconds to wait between requests.

## `userAgent` (type: `string`):

User-Agent header sent with each request.

## `timeoutSecs` (type: `integer`):

Overall run budget in seconds.

## `requestTimeoutSecs` (type: `integer`):

Per-request timeout in seconds.

## `proxyConfiguration` (type: `object`):

Optional proxy. Leave off unless you need it.

## Actor input object example

```json
{
  "publisher": "",
  "maxItems": 100,
  "requestDelaySecs": 1,
  "userAgent": "ActorResearch/1.0",
  "timeoutSecs": 300,
  "requestTimeoutSecs": 30,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `overview` (type: `string`):

Dataset rows in the overview table.

## `dataset` (type: `string`):

Full dataset for this run.

## `items` (type: `string`):

Dataset items as JSON.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("bigibyte/github-models-catalog-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("bigibyte/github-models-catalog-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call bigibyte/github-models-catalog-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,bigibyte/github-models-catalog-scraper"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/NGew3eGEX0vOHhM3e/builds/fDblSm6kmNecE7TAZ/openapi.json