# Trending AI Agents & LLMs (`brunofin/trending-ai-agents`) Actor

Fetch the latest trending AI/LLM models, datasets, and Spaces from Hugging Face via the official keyless API: downloads, likes, pipeline tags, parameter counts, and more. Ideal for AI/LLM market intelligence, agent research, and content pipelines.

- **URL**: https://apify.com/brunofin/trending-ai-agents.md
- **Developed by:** [Bruno Finger](https://apify.com/brunofin) (community)
- **Categories:** AI, Developer tools
- **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/platform/actors/running/actors-in-store#pay-per-usage

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.
Actors are written with capital "A".

## 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.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Trending AI Agents & LLMs

Fetches the latest trending AI/LLM models, datasets, and Spaces from Hugging Face via the official keyless API (`https://huggingface.co/api/trending`). No scraping, no API key, no auth.

**Why this is not a duplicate of `github-trending-scraper`:** that actor scrapes the *github.com/trending* HTML page (GitHub repositories, by language/date). This actor reads Hugging Face's official trending endpoint — a completely different platform, data model (models/datasets/Spaces vs repos), and delivery mechanism (JSON API vs HTML scrape).

### Input

```json
{
  "repoTypes": ["model", "dataset", "space"],
  "limit": 10,
  "includeAuthorData": true,
  "includeInferenceProviders": true
}
```

- `repoTypes` — array of `model`, `dataset`, `space` (default all three).
- `limit` — max trending items per repo type (API exposes up to 10 per type; default 10).
- `includeAuthorData` — include author profile (name, fullname, account type, follower count).
- `includeInferenceProviders` — for models, include third-party inference providers hosting the model.

### Output

One dataset row per trending item. Common fields: `repoType`, `id`, `url`, `author`, `likes`, `downloads`, `lastModified`, `gated`, `private`. Type-specific extras:

- **model** — `pipelineTag`, `numParameters`, `availableInferenceProviders[]` (provider, providerId, task, modelStatus)
- **dataset** — `numRows`, `modalities[]`, `isBenchmark`, `isTraces`
- **space** — `title`, `emoji`, `runtime`, `tags[]`, `aiCategory`, `aiShortDescription`

Failed type fetches produce explicit error rows (`{"repoType": ..., "id": null, "error": "..."}`).

### Pricing

PAY\_PER\_EVENT — `$0.001` per `item-scraped` event (one per dataset row), `apifyMarginPercentage: 0.2`.

### Local run

```bash
cd trending-ai-agents
python3 -m venv .venv
.venv/bin/pip install -r requirements.txt
echo '{"repoTypes": ["model", "dataset", "space"], "limit": 3}' > INPUT.json
APIFY_PYTHON_COMMAND=.venv/bin/python apify run --input-file INPUT.json
```

### Publish

Requires an authenticated Apify CLI (`apify login` with `APIFY_TOKEN`):

```bash
apify push --force
```

Then set PAY\_PER\_EVENT pricing via the Apify API (actor ID comes from the push output):

```bash
curl -X PUT "https://api.apify.com/v2/acts/{ACTOR_ID}?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "pricingModel": "PAY_PER_EVENT",
    "pricingPerEvent": {
      "actorChargeEvents": {
        "item-scraped": {"eventPriceUsd": 0.001}
      }
    },
    "apifyMarginPercentage": 0.2,
    "isPublic": true,
    "title": "Trending AI Agents & LLMs",
    "categories": ["AI", "DEVELOPER_TOOLS"]
  }'
```

Verify monetization with a platform run: `POST /v2/acts/{ACTOR_ID}/runs` with the raw input object as body, then confirm `chargedEventCounts.item-scraped` on the completed run.

### Related actors

- [GitHub Trending Scraper](https://apify.com/brunofin/github-trending-scraper) — trending GitHub repos by language and time range
- [Hacker News Story Stats](https://apify.com/brunofin/hacker-news-story-stats) — HN front-page and search story stats

# Actor input Schema

## `repoTypes` (type: `array`):

Which Hugging Face repo types to fetch: model, dataset, space. Defaults to all three.

## `limit` (type: `integer`):

Maximum number of trending items to return per repo type (the API exposes up to 10 per type).

## `includeAuthorData` (type: `boolean`):

Include author profile info (full name, account type, follower count) for each item.

## `includeInferenceProviders` (type: `boolean`):

For models, include the list of third-party inference providers hosting the model.

## Actor input object example

```json
{
  "repoTypes": [
    "model",
    "dataset",
    "space"
  ],
  "limit": 10,
  "includeAuthorData": true,
  "includeInferenceProviders": true
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# 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 = {
    "repoTypes": [
        "model",
        "dataset",
        "space"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("brunofin/trending-ai-agents").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 = { "repoTypes": [
        "model",
        "dataset",
        "space",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("brunofin/trending-ai-agents").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 '{
  "repoTypes": [
    "model",
    "dataset",
    "space"
  ]
}' |
apify call brunofin/trending-ai-agents --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,brunofin/trending-ai-agents"
        }
    }
}

```

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/h7gBrzLsbcC3fUUQy/builds/qsmFE7zkVFdkeoJcX/openapi.json