browse-latest-models▌
huggingface.co/browse-latest-models-brgzzx · updated May 21, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
List the most recently created models on Hugging Face with full metadata (id, author, createdAt, tags, pipeline_tag, library, downloads, likes, gated flag, canonical URL). Filter by pipeline task, library, author/org, or free-text search. Read-only.
| name | browse-latest-models |
| title | Hugging Face Latest Models |
| description | >- List the most recently created models on Hugging Face with full metadata (id, author, createdAt, tags, pipeline_tag, library, downloads, likes, gated flag, canonical URL). Filter by pipeline task, library, author/org, or free-text search. Read-only. |
| website | huggingface.co |
| category | ml-platforms |
| tags | - huggingface - models - ml - api - read-only |
| source | 'browserbase: agent-runtime 2026-05-20' |
| updated | '2026-05-20' |
| recommended_method | api |
| alternative_methods | - method: browser rationale: >- Browser path at https://huggingface.co/models?sort=created works without any anti-bot stealth — but the listing is fully JS-rendered and costs ~50× the time/$ vs. a single HTTP call to /api/models. Use only if the JSON API is unreachable from your egress (no such block observed as of 2026-05-20). - method: cli rationale: >- The official Python client `from huggingface_hub import HfApi; HfApi().list_models(sort='createdAt', direction=-1, limit=50)` wraps this exact endpoint with typed results, retries, and auth. Skill consumers running in a Python-capable sandbox should prefer it for ergonomics. |
| verified | false |
| proxies | false |
Hugging Face — Browse Latest Models
Purpose
Return the most recently created models on Hugging Face — for each model: id (owner/name), author, createdAt, lastModified, downloads, likes, tags, pipeline_tag, library_name, gated flag, and canonical model URL. Optionally narrow by pipeline task (e.g. text-generation, text-to-image), library (transformers, diffusers, gguf, ...), author/org, or a free-text search. Read-only — never creates, edits, or downloads model artifacts.
When to Use
- "What are the newest models on Hugging Face right now?"
- Hourly / daily polling for newly-uploaded models matching a task or library.
- Watching a specific org (e.g.
meta-llama,google,stabilityai) for new releases. - Discovering new fine-tunes of a base model (combine with
search=<base>orfilter=<task>). - Any flow that would otherwise scrape
huggingface.co/models?sort=created— the JSON API is faster, cheaper, paginated cleanly, and returns richer per-model metadata.
Workflow
Hugging Face exposes a fully public, unauthenticated JSON API at https://huggingface.co/api/models. No cookies, no anti-bot, no residential proxy required. Rate limit is 500 requests / 5-minute fixed window on the api scope (advertised via Ratelimit-Policy and Ratelimit response headers). robots.txt is Allow: / for all user-agents. Lead with the API; the browser path works as a fallback but is ~50× slower because the listing page is fully JS-rendered.
-
Fetch the most recent models (default — no filters):
GET https://huggingface.co/api/models?sort=createdAt&direction=-1&limit=50Returns a JSON array of model objects.
direction=-1is descending (newest first); pair withsort=createdAtfor upload time.limitis per-page; observed max is 1000 per request — paginate via theLinkheader for more. -
Optional query parameters (combine freely):
Param Effect Example sortSort field. Valid: createdAt,lastModified,downloads,likes,trendingScoresort=createdAtdirection-1desc,1ascdirection=-1limitPage size (≤ 1000) limit=50filterPipeline tag filter — text-generation,text-to-image,text-to-video,image-text-to-text,automatic-speech-recognition,feature-extraction,robotics,any-to-any, etc.filter=text-generationlibraryLibrary filter — transformers,diffusers,gguf,mlx,sentence-transformers,transformers.js,pytorch,tf,jax,onnx,safetensorslibrary=diffusersauthorRestrict to one user/org namespace author=meta-llamasearchFree-text substring match on model id search=llama-3fullIf true, includeauthor,sha,gated,lastModified,siblings[](file manifest)full=trueconfigIf true, includeconfig.jsoncontents (architectures, model_type, tokenizer_config) inlineconfig=truecardDataIf true, includecardData(model-card frontmatter: license, language, datasets, base_model)cardData=trueUnrecognized params are silently dropped. Combine filters and
searchfor narrow queries, e.g.?filter=text-to-image&library=diffusers&search=flux&sort=createdAt&direction=-1. -
Parse each result object. Every item is a flat JSON object (named fields — not positional). Default-mode fields:
id—"owner/name"(e.g."meta-llama/Llama-3.2-1B") or a single-segment legacy id ("bert-base-uncased"). This is alsomodelId(duplicated)._id— MongoDB ObjectId (12-byte hex). Its first 4 bytes encode the upload timestamp; this is whatcursor=paginates against. Don't treat this as the model identifier — useid.createdAt— ISO-8601 UTC timestamp of initial upload.tags[]— string array. Includes raw labels ("transformers","safetensors","qwen2"), pipeline-tag duplicates ("text-generation"), language codes ("en","fr"), license tags ("license:apache-2.0"),"base_model:<id>","endpoints_compatible", and a trailing"region:us"deployment-region tag.pipeline_tag— canonical task (e.g."text-generation","text-to-image"). Absent when the uploader didn't tag the model — many fresh uploads have nopipeline_taguntil the README is committed. Don't assume it's always present.library_name— canonical library (e.g."transformers","diffusers"). Also frequently absent on bare uploads.downloads,likes— integers; both0for fresh uploads (uploads are rate-counted with delay).private— alwaysfalsefor results returned by this endpoint (private models are filtered server-side).
With
full=true, additionally:author,gated(false/"manual"/"auto"),lastModified,sha(repo commit SHA),siblings[](array of{rfilename}entries — the file manifest). -
Construct the canonical model URL:
https://huggingface.co/{id}idis used verbatim, slashes included ("meta-llama/Llama-Prompt-Guard-2-86M"→https://huggingface.co/meta-llama/Llama-Prompt-Guard-2-86M). No URL-encoding needed for the slash. The page exists for every model in the response. -
Paginate (only if you need >
limitresults). The response includes aLinkheader:Link: <https://huggingface.co/api/models?sort=createdAt&direction=-1&limit=50&cursor=eyJfaWQiOnsiJGx0IjoiNmEwZTMxNDFmMjdlNGU0NGU5OTlhMjhhIn19>; rel="next"Parse the URL between
<and>and follow it — thecursoris opaque (base64-encoded{_id: {$lt: <ObjectId>}}mongo predicate); do not decode/construct it yourself. There is noprevlink. Stop when theLinkheader is absent or the response array is empty. -
Honor the rate limit. Each response carries:
Ratelimit-Policy: "fixed window";"api";q=500;w=300 Ratelimit: "api";r=<remaining>;t=<seconds-to-window-reset>500 requests per 300-second fixed window. Stay well below it (e.g. ≤ 1 req/s sustained) and back off when
Ratelimit: r=...drops below ~50. There is no documented per-IP block on overage — the server returns429 Too Many Requestsand you waittseconds.
Browser fallback
Use only if the JSON API is unreachable from your egress (it shouldn't be — no anti-bot, no geo restrictions observed).
https://huggingface.co/models?sort=created&pipeline_tag=<task>&library=<lib>&search=<q>&p=<page>
sort=created(note: the browser URL usescreated, the API usescreatedAt— these are not interchangeable across surfaces).p=Npaginates (0-indexed, 30 results per page).- The listing is fully JS-rendered;
browse get markdown bodyparses cleanly. Each card text is<id> [task • params •] Updated <relative-time> agofollowed by the canonical URL in the surrounding anchor<a href="/{id}">.browse snapshotreturns workable refs for clicking through to a model page, but for bulk extraction prefer markdown parsing.
Site-Specific Gotchas
sort=createdAtvssort=created: the API endpoint usescreatedAt(camelCase). The browser URL usescreated(no suffix). They are not aliased — passingsort=createdto/api/modelsis silently ignored and the API falls back to its default sort (which is not createdAt — it'slastModifieddesc, so you'll get stale results that look "recent" but aren't). Always usesort=createdAt&direction=-1on the API.createdAt≠lastModified:createdAtis when the repo was first pushed.lastModifiedis the most recent commit (README edit, weight reupload, etc.). For "newest models" usecreatedAt. For "recently updated models" uselastModified. The two diverge by hours/days for active repos.pipeline_tagandlibrary_nameare often absent on fresh uploads. Many models surface insort=createdAt&direction=-1with no README and no auto-detected pipeline. Treat both as optional fields and key offtags[]if you must classify._idis not the model identifier —idis. The_idfield is the internal MongoDB ObjectId; it changes if the repo is recreated. The user-facing identifier isid(also exposed asmodelId). Useidfor canonical URLs and downstream/api/models/{id}lookups.- No
total_countis returned. Unlike Craigslist'stotalResultCount, the HF models endpoint doesn't include a total. The total models count (~2.9M as of 2026-05) is only available from the browser listing page header text. If you need a count, scrape it fromhttps://huggingface.co/modelsand parse the number under the# Modelsh1. - Cursor pagination is opaque and one-way. The
Link: rel="next"header carries a base64'd mongo predicate. There is norel="prev"— you can only walk forward. If you need to resume from a known model, supplycursor=<base64 of {"_id":{"$lt":"<the model's _id>"}}>— the predicate is straightforward to construct if you have a prior_id, but the safer pattern is to walk from the start and stop whencreatedAt < <cutoff>. limitceiling is 1000. Passinglimit=10000clamps silently to 1000.- Gated and private models.
private: truerepos are never returned by this endpoint regardless of auth.gated: "manual"/"auto"repos are returned (visible in the listing) but their model page may require accepting terms before download — the listing itself is public. Surfacegatedto callers whenfull=trueso they know to expect a terms-gate on click-through. - Adult / NSFW models surface in
sort=createdAt. The default firehose includes user-uploaded LoRAs and image models with explicit names/content. Callers that render results to end users should filter ontags[]for"not-for-all-audiences"/"nsfw"or apply name-based filtering — this is not auto-redacted by the API. tags[]is a multi-namespace bag, not normalized. Same value can appear as a raw label and as a namespaced tag (e.g."text-generation"and"pipeline_tag:text-generation"rarely both appear, but"safetensors"may appear both as a raw tag and as"library:safetensors"-equivalent). Don't expect uniqueness or a stable schema across categories.- Rate-limit policy header is the source of truth. The 500-per-5-minute number above is observed on the
apiscope as of 2026-05-20. ReadRatelimit-Policyon each response in case HF changes it — don't hardcode the window. - No residential proxy required, no stealth required.
browse cloud fetch <url>(the bare HTTP path) works fine — no need to spin up a stealth session unless you're also doing browser interactions in the same flow. This is a cost win: a singlefetchcall costs ~$0 vs. ~$0.01–0.05 for a full session. huggingface_hubPython SDK is the official client (from huggingface_hub import HfApi; HfApi().list_models(sort="createdAt", direction=-1, limit=50)). It wraps this exact endpoint with auth/retry/typing. Skill consumers who can run Python should prefer it for ergonomic typed results; agents driving from a sandbox without Python should use the raw HTTP path above.
Expected Output
{
"query": {
"sort": "createdAt",
"direction": -1,
"limit": 50,
"filter": "text-generation",
"library": null,
"author": null,
"search": null
},
"count": 50,
"next_cursor_url": "https://huggingface.co/api/models?sort=createdAt&direction=-1&limit=50&filter=text-generation&cursor=eyJfaWQiOnsiJGx0IjoiNmEwZTJlZmM2Mjc4ZDhiMmU2MjNlMTk0In19",
"models": [
{
"id": "sstoica12/UAS_qwen7b_medmcqa_100_alpaca_400_proximity_0_8_diversity_0_19999999999999996",
"author": "sstoica12",
"created_at": "2026-05-20T22:00:28.000Z",
"last_modified": "2026-05-20T22:03:07.000Z",
"pipeline_tag": "text-generation",
"library_name": "transformers",
"tags": [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
],
"downloads": 0,
"likes": 0,
"gated": false,
"private": false,
"sha": "1993fd1a13a3aebc3cfb2db24c7c8f32f79b52ed",
"url": "https://huggingface.co/sstoica12/UAS_qwen7b_medmcqa_100_alpaca_400_proximity_0_8_diversity_0_19999999999999996"
},
{
"id": "longtermrisk/Olmo-3-7B-Instruct-replaydistillsftjob-306b1e549725-replay_distillation-a0.3-b0.1-s3407",
"author": "longtermrisk",
"created_at": "2026-05-20T22:10:38.000Z",
"last_modified": "2026-05-20T22:10:44.000Z",
"pipeline_tag": null,
"library_name": "transformers",
"tags": ["transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us"],
"downloads": 0,
"likes": 0,
"gated": false,
"private": false,
"url": "https://huggingface.co/longtermrisk/Olmo-3-7B-Instruct-replaydistillsftjob-306b1e549725-replay_distillation-a0.3-b0.1-s3407"
}
]
}
Three shapes the caller should be prepared for:
// 1. Normal — array of model objects, plus next cursor URL.
{ "count": 50, "next_cursor_url": "...", "models": [...] }
// 2. End of pagination — empty array, no Link header.
{ "count": 0, "next_cursor_url": null, "models": [] }
// 3. Rate-limited — server returns 429, no JSON body, with Retry-After / Ratelimit headers indicating wait time.
{ "error": "rate_limited", "retry_after_seconds": 187, "ratelimit_remaining": 0 }
How to use browse-latest-models on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add browse-latest-models
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches browse-latest-models from GitHub repository huggingface.co/browse-latest-models-brgzzx and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate browse-latest-models. Access the skill through slash commands (e.g., /browse-latest-models) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★53 reviews- ★★★★★Fatima Nasser· Dec 28, 2024
Solid pick for teams standardizing on skills: browse-latest-models is focused, and the summary matches what you get after install.
- ★★★★★Liam Tandon· Dec 16, 2024
browse-latest-models is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Sakura Srinivasan· Dec 12, 2024
Solid pick for teams standardizing on skills: browse-latest-models is focused, and the summary matches what you get after install.
- ★★★★★Chaitanya Patil· Dec 8, 2024
I recommend browse-latest-models for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Piyush G· Nov 27, 2024
Solid pick for teams standardizing on skills: browse-latest-models is focused, and the summary matches what you get after install.
- ★★★★★Hana Gupta· Nov 19, 2024
I recommend browse-latest-models for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Liam Wang· Nov 7, 2024
Keeps context tight: browse-latest-models is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Kwame Haddad· Oct 26, 2024
I recommend browse-latest-models for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Hiroshi Liu· Oct 22, 2024
Keeps context tight: browse-latest-models is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Shikha Mishra· Oct 18, 2024
browse-latest-models is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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