Hugging Face Models Scraper

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Operated by Sourcelane✓ verified

A utility for searching the Hugging Face Hub to collect structured metadata about machine learning models by keyword, task, or library. The connector queries the Hub and returns per-model metadata including model identifiers and author information, direct model URLs, pipeline task tags (e.g., text-generation, text-to-image), library names (transformers, diffusers, sentence-transformers, peft, timm, etc.), all Hub tags (license, language, base model, results, framework), recent download counts, likes counts, trending score, gated/private flags, and creation/last-modified timestamps for freshness and popularity analysis.

Verified Sep 28, 3:55 AM

$0.003

per model

Up to 100 per call. Only pay for results returned; failed calls are refunded.

What people use it for

ML / model market research: measure popularity and adoption of open-source models across tasks and libraries.
Competitor tracking: monitor download and like trends for models in targeted categories (LLMs, image generation, embeddings).
Build a model catalog: compile up-to-date Hub metadata for internal catalogs, comparison pages, or documentation.
Lead generation: discover authors and organizations publishing models in a specific task or library.
License audit: surface license-related tags across a curated list of models for compliance and licensing analysis.

Trust & reliability

7d uptime trend

Window	Uptime	Success rate	p50	p95
24h	—	—	—	—
7d	—	—	—	—
30d	—	—	—	—

Calling contract

Call it through Sourcelane's gateway or MCP server. We run the connector, apply your agent's spend guardrails, and bill only the results returned.

Call it via Sourcelane

curl -X POST https://api.usesourcelane.com/v1/call \
  -H "Authorization: Bearer sl_live_your_agent_key" \
  -H "Content-Type: application/json" \
  -d '{"listing":"huggingface-models-scraper","params":{"query":"llama","maxItems":5},"maxResults":10}'

Request params

Field	Type	Required	Description
`query`	string	optional	Free-text search across model IDs and descriptions. Leave empty to list every model in the Hub (filtered by task/library if set).
`task`	string	optional	Restrict results to a Hugging Face pipeline task tag.
`library`	string	optional	Restrict to models that declare a specific library - e.g. transformers, diffusers, sentence-transformers, peft, timm. Leave empty for any library.
`sort`	string	optional	Field to sort the results by.
`direction`	string	optional	Descending (-1) returns the most downloads/likes/etc. first; ascending (1) returns the least.
`maxItems`	integer	optional	Total number of models to fetch across paginated calls. Hugging Face returns 100 per page; this connector follows the Link header automatically. Capped at 100 per call.

Each result contains

Field	Type	Description
`id`	string	Id
`author`	string	Author
`modelName`	string	Model Name
`huggingfaceUrl`	string	Huggingface Url
`pipelineTag`	string	Pipeline Tag
`libraryName`	string	Library Name
`tags`	array	Tags
`downloads`	integer	Downloads
`likes`	integer	Likes
`gated`	string	Gated
`private`	boolean	Private
`createdAt`	string	Created At
`lastModified`	string	—
`trendingScore`	null	—
`scrapedAt`	string	—

Use Hugging Face Models Scraper from Claude, ChatGPT or Cursor

Pick your tool and connect in under a minute. Then just ask — for example: “For these 50 domains, find contact emails and what e-commerce platform they run on.”

Connect Claude

Web, desktop and mobile. Paste one URL.

1Copy your personal connector URL
2In Claude open Settings → Connectors → Add custom connector
3Paste the URL and click Add — done

Manual setup (config files, REST, Python) +

Claude Code

Adds the Sourcelane MCP server with your key as a header.

terminal

claude mcp add --transport http sourcelane https://api.usesourcelane.com/mcp \
  --header "Authorization: Bearer sl_live_your_agent_key"

Claude Desktop (config file)

Alternative to the connector URL: add to claude_desktop_config.json, then restart Claude.

claude_desktop_config.json

{
  "mcpServers": {
    "sourcelane": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://api.usesourcelane.com/mcp",
        "--header",
        "Authorization:${AUTH_HEADER}"
      ],
      "env": {
        "AUTH_HEADER": "Bearer sl_live_your_agent_key"
      }
    }
  }
}

Cursor & Windsurf

Add to ~/.cursor/mcp.json (or Windsurf's mcp_config.json).

mcp.json

{
  "mcpServers": {
    "sourcelane": {
      "url": "https://api.usesourcelane.com/mcp",
      "headers": {
        "Authorization": "Bearer sl_live_your_agent_key"
      }
    }
  }
}

VS Code

Save as .vscode/mcp.json. VS Code prompts for your key once.

.vscode/mcp.json

{
  "servers": {
    "sourcelane": {
      "type": "http",
      "url": "https://api.usesourcelane.com/mcp",
      "headers": {
        "Authorization": "Bearer ${input:sourcelane-key}"
      }
    }
  },
  "inputs": [
    {
      "type": "promptString",
      "id": "sourcelane-key",
      "description": "Sourcelane agent key",
      "password": true
    }
  ]
}

ChatGPT Custom GPT (Actions)

Create a GPT → Actions → Import from URL, then Authentication: API Key, Bearer.

OpenAPI schema URL

https://usesourcelane.com/openapi.json

REST

One POST. Pass maxResults to cap cost.

curl

curl -X POST https://api.usesourcelane.com/v1/call \
  -H "Authorization: Bearer sl_live_your_agent_key" \
  -H "Content-Type: application/json" \
  -d '{"listing":"huggingface-models-scraper","params":{"query":"llama","maxItems":5},"maxResults":10}'

Python, LangChain, CrewAI, OpenAI Agents SDK…

Wrap the REST call as a tool, or point an MCP client at the endpoint.

python

import requests

res = requests.post(
    "https://api.usesourcelane.com/v1/call",
    headers={"Authorization": "Bearer sl_live_your_agent_key"},
    json={
        "listing": "huggingface-models-scraper",
        "params": {"query":"llama","maxItems":5},
        "maxResults": 10,
    },
    timeout=300,
)
body = res.json()
print(body["receipt"]["chargedMicros"], "micro-USD for", body["receipt"]["results"], "results")
print(body["data"])

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