Hugging Face Model Monitor - Hub SHA Watchlist

Re-check the Hugging Face Hub models you already care about. Get SHA, downloads, license and visibility changes — and skip the rows that did not move.

This Actor takes a caller-owned list of Hub model IDs, reads the public Hub API, and writes one structured row per model. Pass the previous run's snapshot back in and you get INITIAL, UNCHANGED or CHANGED on every successful check. No Hub token. No weight downloads. No Hub crawl.

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What this Actor does

You already know the model IDs (gpt2, owner/name). You want a scheduled check that tells you when the commit SHA, license, gated flag, download count or visibility moved — without paying a start fee every morning and without pulling gigabytes of weights.

That is the job: a watchlist monitor, not a Hub dump scraper.

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What you get

{
  "schemaVersion": 1,
  "id": "gpt2",
  "status": "ok",
  "modelId": "gpt2",
  "pipelineTag": "text-generation",
  "libraryName": "transformers",
  "downloads": 18423011,
  "likes": 2144,
  "sha": "607a30d783dfa663caf39e06633721c8d4cfcd7e",
  "lastModified": "2026-04-17T17:44:44.000Z",
  "gated": false,
  "disabled": false,
  "license": "mit",
  "author": "openai-community",
  "changeType": "INITIAL",
  "changedFields": [],
  "checkedAt": "2026-09-06T12:00:00.000Z",
  "sourceUrl": "https://huggingface.co/api/models/gpt2",
  "error": null
}

gpt2 is a public example identifier, not a customer or endorsement. Missing source strings are null — never "N/A".

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Main use cases

• Model-card drift. Catch SHA and license changes on models your product actually loads.
• Gated / disabled alerts. See when a watched model becomes gated or disabled.
• Download and like movement. Treat Hub counters as a change signal, not a ranking.
• Scheduled MLOps. Put the Actor on your own cadence and keep the snapshot in your workflow state.
• n8n / MCP / backend jobs. Start the run from a server that holds APIFY_TOKEN.

---

Quick start

{
  "models": ["gpt2"],
  "maxItems": 1
}

Run it, open the dataset, and save NEXT_SNAPSHOT from the default key-value store.

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Repeat workflow

1. Keep one Hub model watchlist per customer or project (1–50 IDs).
2. Start a bounded run. Schedule it yourself if you need a cadence.
3. Save the returned NEXT_SNAPSHOT JSON in your private state.
4. On the next run pass it as previousSnapshot and set onlyChanges: true.
5. Route changeType: CHANGED rows (SHA, visibility, downloads, license) for review.

No schedules are enabled by default. Snapshots use the run's default storage, never a shared named store. A repeated check without a supplied snapshot is a new initial check.

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Input

Field	Type	Default	What it does
models	array	—	1–50 Hub IDs (name or owner/name). Duplicates checked once. Letters, digits, ., _, - only.
maxItems	integer	50	1–50. Must cover all unique IDs; a smaller value is rejected rather than silently dropping IDs.
onlyChanges	boolean	false	Suppress unchanged successful records without charging them. The first observation is still emitted.
previousSnapshot	object	—	Prior NEXT_SNAPSHOT with schemaVersion: 1, kind: "hf-model", and a records map.

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Pricing

Event	Price	When it happens
Successful watchlist result	$0.003	One successful model check written to the dataset
Actor start	none	No custom start fee

You are not charged for: failed requests, parse failures, retries, duplicate IDs, invalid input, or unchanged records suppressed by onlyChanges. With onlyChanges: false, an unchanged successful refresh is delivered work and is charged. The run stops when eventChargeLimitReached is true.

Worked example. 50 models every weekday, onlyChanges: true after the first run, 4 changed rows/day → about (4 × $0.003) = $0.012 per run, roughly $0.26/month in Actor event charges if that pattern holds.

How this compares

Actor	Price per result	Per-run start	Weights downloaded
This Actor	$0.003	none	No
parseforge/huggingface-model-scraper (Store card, 2026-09-06)	$0.005	$0.005	Dump-oriented listing

Competitor list prices change. Check the current Store cards before relying on this table. This Actor is a caller-owned watchlist, not a Hub catalogue dump.

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Using the API

Keep APIFY_TOKEN on a protected backend. Set maxItems and maxTotalChargeUsd.

curl --fail -X POST "https://api.apify.com/v2/acts/technicaldost~huggingface-model-watchlist-monitor/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{"models":["gpt2"],"maxItems":1}'

JavaScript

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('technicaldost/huggingface-model-watchlist-monitor').call({
    models: ['gpt2'],
    maxItems: 1,
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();

Python

from apify_client import ApifyClient
import os

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("technicaldost/huggingface-model-watchlist-monitor").call(run_input={
    "models": ["gpt2"],
    "maxItems": 1,
})
for row in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(row["modelId"], row["sha"], row["changeType"])

n8n. HTTP Request → POST https://api.apify.com/v2/acts/technicaldost~huggingface-model-watchlist-monitor/runs with header Authorization: Bearer {{$credentials.apifyToken}}. Poll the run, then read the default dataset and NEXT_SNAPSHOT.

MCP / agents. Call the Actor asynchronously from a server tool. Do not put the token in the browser or in a public agent transcript.

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Limitations

• Public Hub metadata only. Private repositories are written as errors, not guessed.
• Weights, datasets, Spaces and files are out of scope.
• Hub availability and freshness govern results. This is not a ranking, safety, or license-compliance opinion.
• One request at a time, 20 seconds per request, 1.5 MB per response, no automatic retries.
• At most 1,000 snapshot records are retained; SUMMARY.snapshotPruned reports eviction.

Not affiliated with or endorsed by Hugging Face.

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Related Actors

• Software End-of-Life Monitor — support-window changes on products you already run.
• crt.sh Certificate Monitor — new public TLS certificates for hostnames you already own.

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Changelog

See CHANGELOG.md. Release 1.0.0 (2026-09-06): caller-owned Hub watchlists, SHA/visibility deltas, isolated snapshots, $0.003 per successful result, no start fee.

Output example

Each result is one dataset item. A real item from a test run (long fields shortened):

{
  "schemaVersion": 1,
  "id": "gpt2",
  "status": "ok",
  "modelId": "openai-community/gpt2",
  "pipelineTag": "text-generation",
  "libraryName": "transformers",
  "downloads": 18446999,
  "likes": 3883,
  "sha": "607a30d783dfa663caf39e06633721c8d4cfcd7e",
  "lastModified": "2024-02-19T10:57:45.000Z",
  "gated": false,
  "disabled": false
}

Use Hugging Face Model Monitor with AI agents (MCP)

Give Claude, ChatGPT, Cursor, VS Code or any MCP client this Actor as a tool through the hosted Apify MCP server. The agent sees the input fields with descriptions and example values, runs the Actor, and reads the results.

MCP server URL (this Actor only):

https://mcp.apify.com/?tools=technicaldost/huggingface-model-watchlist-monitor

Claude Code:

$claude mcp add --transport http apify "https://mcp.apify.com/?tools=technicaldost/huggingface-model-watchlist-monitor" --header "Authorization: Bearer YOUR_APIFY_TOKEN"

Cursor, VS Code, Claude Desktop and other clients (mcp.json or the client's MCP settings):

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=technicaldost/huggingface-model-watchlist-monitor",
      "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" }
    }
  }
}

Clients that support sign-in (such as Claude.ai connectors) can use the URL without a token and log in to Apify instead. Get your token in Apify Console → Settings → API & Integrations, or generate a ready config for your client at mcp.apify.com. In the agent the tool is named technicaldost--huggingface-model-watchlist-monitor; ask it, for example, to "run Hugging Face Model Monitor with {"models": ["gpt2"]}".

AI agents without an Apify account can also run and pay for this Actor per request with x402 agentic payments.

Run Hugging Face Model Monitor via API

Run the Actor and get its results in one call (replace YOUR_APIFY_TOKEN):

curl -X POST "https://api.apify.com/v2/acts/technicaldost~huggingface-model-watchlist-monitor/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"models": ["gpt2"]}'

Python (pip install apify-client):

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("technicaldost/huggingface-model-watchlist-monitor").call(run_input={"models": ["gpt2"]})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

JavaScript / TypeScript (npm install apify-client):

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('technicaldost/huggingface-model-watchlist-monitor').call({"models": ["gpt2"]});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Long runs: start them with POST /v2/acts/technicaldost~huggingface-model-watchlist-monitor/runs and fetch the dataset when the run finishes, or receive a webhook.

FAQ

Can I use Hugging Face Model Monitor with AI agents like Claude or ChatGPT?

Yes. Connect the Apify MCP server with ?tools=technicaldost/huggingface-model-watchlist-monitor (see above) and the agent can run the Actor and read its results on its own.

Can I call Hugging Face Model Monitor from my own code?

Yes. Use the REST API, or the official Python and JavaScript clients shown above. Results are available as JSON, CSV, Excel, XML or HTML.

How do I run it on a schedule?

Save your input as a task in Apify Console and add a schedule (for example, daily). Use webhooks or integrations to send each run's results onward.

Is it legal to use this Actor?

It works with publicly available data. Make sure your use complies with the source website's terms and with data protection laws such as GDPR, especially for personal data.

Something doesn't work or I need a feature. What should I do?

Open an issue in the Issues tab on this Actor's page with an example input, and we'll look into it.