# Docker Hub Intelligence & Container Registry Matrix (`primesieve/docker-hub-intel`) Actor

Scrape Docker Hub search results, repositories, tags, architectures (amd64/arm64), pull metrics, star counts, and layer sizes without API keys or browser overhead.

- **URL**: https://apify.com/primesieve/docker-hub-intel.md
- **Developed by:** [Prime Sieve](https://apify.com/primesieve) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

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

## 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

## Docker Hub Intelligence & Container Registry Matrix — Prime Sieve

**Scrape Docker Hub search results, repositories, tags, OS architectures (amd64/arm64), pull metrics, star counts, and layer sizes without API keys or browser overhead.**

Built by **Prime Sieve**. Powered by direct, zero-proxy API queries against the public Docker Hub registry endpoints. Ideal for DevOps engineers, platform teams, AI infra architects, and security researchers looking to audit base images, track deployment trends, and monitor container adoption.

***

### 🚀 What This Actor Does

1. **Keyword Search & Discovery**: Search through millions of public Docker Hub containers (e.g. `vllm`, `ollama`, `postgres`, `ai-agent`, `qdrant`).
2. **Direct Repository Auditing**: Provide an explicit list of repository names (`ollama/ollama`, `vllm/vllm-openai`, `library/python`) to extract deep metadata in bulk.
3. **Multi-Arch & Tag Breakdown**: Extract recent image tags with detailed OS / CPU architectures (`linux/amd64`, `linux/arm64`), compressed layer sizes (in MB), digests, and push timestamps.
4. **Metrics & Popularity Signals**: Capture real-time lifetime pull counts, star counts, official badge status, and registration dates.
5. **Zero Proxy Overhead**: Fast, lightweight, and completely fetch-clean with zero browser memory bloat.

***

### 💰 Pricing & Economics

- **Pay-Per-Result (PPE)**: **$1.00 per 1,000 repository results** ($0.001 per result).
- No expensive proxy charges, no browser overhead fees.

#### Price & Feature Comparison

| Feature | Prime Sieve (This Actor) | Generic Docker Scrapers |
| :--- | :---: | :---: |
| **Pricing** | **$1.00 / 1k results** | $3.00+ / 1k results or Flat |
| **Zero Proxy Overhead** | ✅ 100% Native API | ❌ Slow Headless Browsers |
| **Architecture Extraction (`arm64`/`amd64`)** | ✅ Built-in | ❌ Missing |
| **Tag Image Sizes (MB)** | ✅ Calculated per tag | ❌ Missing |
| **Direct Repo List Mode** | ✅ Supported | ❌ Search Only |
| **Dataset Table Views** | ✅ Clean Overview Table | ❌ Raw Dump |

***

### 📥 Input Parameters

| Parameter | Type | Default | Description |
| :--- | :---: | :---: | :--- |
| `search` | String | `"vllm"` | Search keyword for finding Docker images (e.g., `ollama`, `langchain`, `redis`). |
| `repositories` | Array | `[]` | Explicit list of repos (e.g. `["ollama/ollama", "vllm/vllm-openai", "python"]`). Overrides search. |
| `maxResults` | Number | `20` | Maximum number of repositories to process (1 - 500). |
| `includeTags` | Boolean | `true` | Fetch recent image tags, OS/architectures, digests, and compressed sizes. |
| `maxTagsPerRepo` | Number | `10` | Number of recent tags to extract per repository (1 - 100). |
| `includeFullReadme` | Boolean | `false` | Include the full README / setup instructions in markdown. |

***

### 📤 Sample Output

```json
{
  "repoName": "ollama/ollama",
  "namespace": "ollama",
  "name": "ollama",
  "description": "The easiest way to get up and running with large language models.",
  "starCount": 1613,
  "pullCount": 176266101,
  "isOfficial": false,
  "isAutomated": false,
  "isPrivate": false,
  "repositoryType": "image",
  "lastUpdated": "2026-09-17T23:04:44.564114Z",
  "dateRegistered": "2023-06-29T23:27:34.326426Z",
  "supportedArchitectures": [
    "amd64",
    "arm64"
  ],
  "totalTagsAvailable": 10,
  "tags": [
    {
      "tag": "latest",
      "lastUpdated": "2026-09-17T23:04:42.129381Z",
      "fullSizeMB": 3538.5,
      "architectures": [
        "amd64",
        "arm64"
      ],
      "images": [
        {
          "architecture": "amd64",
          "os": "linux",
          "sizeBytes": 3710334567,
          "sizeMB": 3538.5,
          "digest": "sha256:c715bebf769913db6c82d96f8a8dfee989c4bdfe19fa7d700e3d41ee0ceb5461",
          "lastPushed": "2026-09-17T21:40:51.077443938Z"
        }
      ]
    }
  ],
  "url": "https://hub.docker.com/r/ollama/ollama",
  "scrapedAt": "2026-09-18T10:15:00.000Z"
}
```

***

### 🛠️ Use Cases

- **AI Infrastructure & Model Serving Tracking**: Track container adoption for LLM inference engines like vLLM, Ollama, TGI, and LocalAI.
- **Supply Chain & Multi-Arch Verification**: Ensure your containerized dependencies publish native `linux/arm64` images for Apple Silicon / AWS Graviton instances.
- **Image Bloat & Storage Optimization**: Monitor image tag sizes over time to detect unexpected layer bloat in CI/CD pipelines.
- **Market Intelligence & Tech Stack Trends**: Track developer adoption across open-source databases, message brokers, and AI agent frameworks.

***

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# Actor input Schema

## `search` (type: `string`):

Keyword to search Docker Hub container repositories (e.g. 'vllm', 'ollama', 'postgres', 'ai-agent'). Ignored if direct Repositories list is provided.

## `repositories` (type: `array`):

List of specific repositories to scrape in 'namespace/name' or 'name' format (e.g. \['ollama/ollama', 'vllm/vllm-openai', 'python']). Overrides search keyword.

## `maxResults` (type: `integer`):

Maximum number of repositories to process.

## `includeTags` (type: `boolean`):

Fetch container image tags, OS/architectures (amd64, arm64), digests, and compressed sizes.

## `maxTagsPerRepo` (type: `integer`):

Maximum number of recent image tags to fetch per repository.

## `includeFullReadme` (type: `boolean`):

Include the full markdown description / docker-compose setup instructions.

## Actor input object example

```json
{
  "search": "vllm",
  "maxResults": 20,
  "includeTags": true,
  "maxTagsPerRepo": 10,
  "includeFullReadme": false
}
```

# Actor output Schema

## `repoName` (type: `string`):

Full repository identifier (e.g. ollama/ollama, vllm/vllm-openai)

## `description` (type: `string`):

Short repository description

## `starCount` (type: `string`):

Total Docker Hub star count

## `pullCount` (type: `string`):

Lifetime total image pull count

## `isOfficial` (type: `string`):

Whether this repository is a Docker Official Image

## `supportedArchitectures` (type: `string`):

List of supported architectures (amd64, arm64, etc.)

## `lastUpdated` (type: `string`):

Timestamp of most recent push or update

## `url` (type: `string`):

Direct link to Docker Hub repository

# 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 = {
    "search": "vllm"
};

// Run the Actor and wait for it to finish
const run = await client.actor("primesieve/docker-hub-intel").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 = { "search": "vllm" }

# Run the Actor and wait for it to finish
run = client.actor("primesieve/docker-hub-intel").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 '{
  "search": "vllm"
}' |
apify call primesieve/docker-hub-intel --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,primesieve/docker-hub-intel"
        }
    }
}
```

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/epAuPqEiyULOsFEH7/builds/Efz20biSblJfytjAA/openapi.json