Hugging Face Spaces Scraper

Hugging Face Spaces Scraper

Scrape Hugging Face Spaces by keyword, author, or SDK, up to a million per run. Each Space comes with its title, description, likes, SDK, author, and last modified date. No API key or login. Export to CSV, JSON, Excel, or XML.

Hugging Face's official API needs a token, rate-limits you, and returns nested JSON that is painful to flatten. This reads the public Spaces search endpoint directly, filtered by keyword, author, or SDK, and returns each match in one fixed schema. It is the fastest way to monitor new ML apps, track competitors, or build a dataset of deployed demos.

Who uses it	What they scrape Hugging Face Spaces for
AI product managers	Track which ML apps competitors and the community are shipping this week
Market researchers	Build a list of deployed demos for a technology landscape report
Recruiters	Find authors who publish working ML apps in a specific framework
Data scientists	Collect a training corpus of app titles and descriptions for trend analysis
Investors	Monitor new Spaces from target organizations to spot early signals

What it does

This Actor collects Hugging Face Spaces by search query, author, or SDK, and returns each one as a flat row.

• 🔍 Keyword search: free-text search across Space titles and descriptions, trimmed before it hits the API.
• 👤 Author filter: restrict results to one Hugging Face user or organization, like huggingface or openai-community.
• 🧩 SDK filter: return only Gradio, Streamlit, Docker, or static HTML Spaces.
• 📊 Sort control: order by likes, trending, created date, or last modified, ascending or descending.
• 📦 Flat output: every Space is one row with title, description, likes, SDK, author, and last modified date.

Results export to CSV, JSON, Excel, or XML, or straight from the API.

What you can do with Hugging Face Spaces data

📈 Track new ML apps.

A product manager runs the Actor daily with sort by created date and a keyword like 'chatbot' to see what the community shipped overnight.

🧑‍💻 Monitor a competitor organization.

An analyst sets the author filter to a company like openai-community and collects every Space they publish, with likes and last modified date.

🗂️ Build a technology landscape.

A market researcher runs the Actor for each SDK and keyword in their scope, then merges the CSV exports into one master list of deployed demos.

🎯 Source AI talent.

A recruiter filters by Gradio and a niche keyword, then reaches out to the authors of the most-liked Spaces.

📊 Feed a trend model.

A data scientist collects a year of Space titles and descriptions to train a topic model on what the Hugging Face community builds.

Why choose this scraper

	What you get
No API key	The public search endpoint needs no token, so you can start a run in seconds.
Flat schema	Every Space is one row, no nested JSON to parse before you can use it.
Up to 1M rows	Collect a full category or author portfolio in a single run.
Sort and filter	Likes, trending, created date, last modified, author, and SDK are all supported.

How it compares

This Actor focuses on Hugging Face Spaces only, with a flat row per Space and no API key, while the competitors below cover broader AI tool data or the full Hub API.

Feature	ParseForge	AI Tools & Models Intelligence	Hugging Face Hub API
Search Spaces by keyword	Yes	Yes	Yes
Filter by author or organization	Yes	Not listed	Not listed
Filter by SDK (Gradio, Streamlit, Docker, static)	Yes	Not listed	Not listed
Sort by likes, trending, created, or modified	Yes	Not listed	Not listed
Flat row per Space (no nested JSON)	Yes	Not listed	Not listed
No API key required	Yes	Not listed	Not listed

Configure the run

Drive the Actor from a search query, an author, and an SDK, alone or together, and filters run as each Space is read so only matches reach your dataset. The Input tab lists every parameter.

A first run with the defaults:

{
 "maxItems": 10
}

A larger pull:

{
 "maxItems": 200
}

Pricing

Pay-per-result: $0.0085 per result collected. You pay only for the results written to your dataset.

Results collected	Approximate cost
100 results	$0.85
1,000 results	$8.50
10,000 results	$85.00

New Apify accounts start with $5 in free credit.

Free users

Free-plan runs return up to 10 results as a preview. Upgrade your Apify plan to collect up to 1,000,000 results per run.

Run it

1. Create a free Apify account with $5 in credit.
2. Open the Hugging Face Spaces Scraper.
3. Set your inputs and any filters, then click Start.
4. Export the results as CSV, Excel, JSON, or XML from the Dataset tab.

Run it programmatically through the Apify API (run-sync-get-dataset-items) or the ApifyClient for JavaScript and Python.

Use with AI agents (MCP)

Give an AI agent live access to Hugging Face Spaces through the Model Context Protocol. Add the Actor to Claude, Cursor, or any MCP client:

$claude mcp add --transport http apify "https://mcp.apify.com?tools=parseforge/huggingface-spaces-scraper"

Then prompt it in plain language to run the scraper and read back the results.

Troubleshooting

Why am I getting no results?

Check your search query and filters. If you set an author or SDK that does not match any Spaces, the result set will be empty. Try removing filters one at a time.

Why did the run stop before reaching my maximum?

The API may have fewer matching Spaces than your maximum. The Actor returns all matches it finds, up to the limit you set.

Why are some fields empty in the output?

Not every Space has a description or a last modified date. Empty fields are normal and reflect the data on Hugging Face.

Can I get the Space's source code or files?

No. This Actor returns metadata only. To download files, use the Hugging Face Hub API or the repository's git endpoint.

Why is the sort order different from what I expected?

The API applies the sort before pagination. If you combine sort with a large maximum, the order is consistent with the API's ranking, not a post-hoc sort of all results.

FAQ

Question	Answer
Do I need a Hugging Face API token?	No. This Actor uses the public Spaces search endpoint, which requires no authentication. You can run it immediately.
What data does each Space row contain?	Each row includes the Space title, description, likes, SDK, author, and last modified date. The exact fields are shown in the sample output.
Can I filter by author or organization?	Yes. Set the author field to a Hugging Face username or organization, like huggingface or openai-community, and only their Spaces will be returned.
Can I filter by SDK?	Yes. Choose Gradio, Streamlit, Docker, or static HTML to return only Spaces built with that framework.
How many Spaces can I collect in one run?	Up to 1,000,000. Set the maximum Spaces field to the number you need, and the Actor will stop when it reaches that count.
What sort orders are supported?	You can sort by likes, trending, created date, or last modified, in ascending or descending order.
Can I search by keyword?	Yes. The search field does free-text matching across Space titles and descriptions, and the input is trimmed before it is sent to the API.
What export formats are available?	CSV, JSON, Excel, and XML. Choose the format in the run settings or download from the dataset tab.
Is this the same as the Hugging Face Hub API?	This Actor focuses on Spaces only and returns a flat row per Space. The Hub API also covers models and datasets, but this is simpler for Spaces-only work.
Does this Actor scrape the Space page itself?	No. It uses the public search API, so it is fast and does not load the Space UI. You get the metadata, not the app's internal files.

Related actors

Browse the full ParseForge collection for more scrapers.

🆘 Need help? Email parseforge@protonmail.com with your run ID, your input, and what you expected.

⚠️ Disclaimer. This Actor is unofficial and is not affiliated with, endorsed by, or sponsored by Hugging Face, Inc. It collects only publicly available data. You are responsible for using the collected data in compliance with the source's terms of service and applicable data-protection laws, including GDPR, CCPA, and PIPL. Do not use it to collect personal data unlawfully.