Hugging Face Hub API

Name: Hugging Face Hub API
Brand: Hugging Face Hub API
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferencebearer235 EndpointsREST

For Agents

Browse and manage Hugging Face Hub repos, datasets, Spaces, collections, jobs, and inference endpoints, with full discussion and webhook support.

Connect the Hugging Face Hub API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Hugging Face Hub API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Install Jentic One Beta (Self-Hosted)

Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with Hugging Face Hub API.

List, search, and inspect models, datasets, and Spaces on the Hugging Face Hub

Create, rename, move, and delete repositories of any type on the Hub

Open, comment on, and merge discussions and pull requests against a repo

Run and schedule jobs against Hub-hosted compute and read their logs

GET STARTED

Start building with Hugging Face Hub API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI specification for the Hugging Face Hub API, keeping it validated and agent-ready. The Hugging Face Hub API is the management surface for everything on huggingface.co outside of model inference — repositories for models, datasets, and Spaces, plus collections, papers, organizations, jobs, inference endpoints, and user settings. Through it an integration can list and search the Hub, create or update repos, manage discussions and pull requests, run scheduled jobs, and configure notifications and webhooks. It is the API that wraps what the official huggingface_hub Python client exposes, in HTTP form.

Use Cases

Patterns agents use Hugging Face Hub API for, with concrete tasks.

★ Programmatic Hub repo management

Create, rename, and delete model and dataset repositories on the Hugging Face Hub from CI without going through the web UI. The API supports private repos, organization ownership, and revision-level operations so a release pipeline can push a new model artifact, tag the revision, and update the model card in one run.

Call POST /api/repos/create with name, type='model', and private=true, then patch the model card via the repo's resolve endpoint.

Hub search and discovery for agents

Power agent-facing tooling that needs to find the right model, dataset, or Space on the Hugging Face Hub. The search and listing endpoints return paginated results filterable by tag, library, language, license, and downloads so an agent can shortlist candidates and inspect each repo's metadata before pulling the artifact.

Call GET /api/models with filter=text-classification and library=transformers, then GET /api/models/{id} for each shortlisted repo to read its model card metadata.

Discussion and pull request automation

Triage discussions and pull requests across many Hub repositories from a single bot — surface stale issues, auto-comment on license-flagged uploads, or merge approved community contributions into model cards. The discussions endpoints expose the full thread including reactions and revision diffs so the bot can act on real context.

List discussions on a repo, post a comment via the discussions API on each open PR older than 14 days, then merge the ones with maintainer approval.

Scheduled jobs on Hub compute

Run training, evaluation, or dataset preparation jobs against Hugging Face Hub compute on a schedule and stream the logs back into an observability tool. Scheduled job configuration, run listing, and per-run log retrieval are all exposed through the API so the orchestration layer does not need to scrape the UI.

POST /api/scheduled-jobs with the job spec, then poll GET /api/jobs/{id}/logs to stream output back into the orchestrator.

Agent-driven Hub workflows through Jentic

An AI agent that builds a curated dataset of evaluation models uses Jentic to find the Hugging Face search and repo-create operations, loads their schemas, and runs them with a single Hugging Face token held in the Jentic vault. The agent never holds the raw token in its prompt context.

Through Jentic, search 'find Hugging Face models for text classification', load GET /api/models, execute it with filter='text-classification', then iterate to load each repo's metadata.

Key Endpoints

235 endpoints — jentic publishes the only available openapi specification for the hugging face hub api, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

GET

/api/models

List and search models

GET

/api/datasets

List and search datasets

GET

/api/spaces

List and search Spaces

POST

/api/repos/create

Create a new repository of any type

DELETE

/api/repos/delete

Delete an existing repository

GET

/api/settings/webhooks

List configured webhooks

POST

/api/settings/webhooks

Create a new webhook

GET

/api/notifications

Read the notification feed

GET

/api/models

List and search models

GET

/api/datasets

List and search datasets

GET

/api/spaces

List and search Spaces

POST

/api/repos/create

Create a new repository of any type

DELETE

/api/repos/delete

Delete an existing repository

Why Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Hugging Face user access tokens are stored encrypted in the Jentic MAXsystem vault. Agents call Hub operations through scoped execution handles — the raw Bearer token never enters the agent's prompt context.

Intent-based discovery

Agents search by intent like 'find Hugging Face models' or 'create a Hugging Face repo' and Jentic returns the matching Hub operation with its full input schema, including filters and pagination cursors.

Time to first call

Direct integration: 2-4 days to discover the right Hub endpoints from docs, build pagination, and handle revision-aware writes. Through Jentic: under 1 hour — search, load, execute against the validated spec.

FAQs

Specific to using Hugging Face Hub API through Jentic.

Why is there no official OpenAPI spec for Hugging Face Hub API?

Hugging Face does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Hugging Face Hub API via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Hugging Face Hub API use?

The Hub API uses Hugging Face user access tokens passed as a Bearer header. Through Jentic the token sits in the encrypted MAXsystem vault and is injected at execution time so the agent never sees the raw token.

Can I create a private model repository through this API?

Yes. POST /api/repos/create accepts type='model' along with private=true and an optional organization owner, so the new repo is created with restricted visibility from the start.

What are the rate limits for the Hugging Face Hub API?

Hugging Face applies per-token rate limits that scale with the user's tier; Pro and enterprise tokens get substantially higher limits than free accounts. Authenticated calls always have higher quotas than unauthenticated ones, so always include the Bearer token.

How do I search for models through Jentic?

Search Jentic for 'find Hugging Face models', load the schema for GET /api/models, and execute it with filters like library, tag, or language. Jentic returns the parsed search results ready for the agent to iterate over.

Does this API run model inference?

No — model inference goes through the separate Hugging Face Inference API and Inference Endpoints. This Hub API manages the repositories, jobs, discussions, and account settings around models rather than running them.

Can I configure webhooks for repo events?

Yes. POST /api/settings/webhooks creates a webhook that fires on configured repo events, and the per-webhook endpoints let you pause, replay, or delete the webhook from code.

Hugging Face Hub API

Connect the Hugging Face Hub API to your agent

Capabilities

Start building with Hugging Face Hub API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs