PyTorch

APIs and resources for PyTorch, an open source machine learning framework for tensor computation and deep learning developed by Meta.

PyTorch is listed with 5 APIs on the APIs.io network. Tagged areas include Artificial Intelligence, Deep Learning, Machine Learning, Neural Networks, and Open Source.

PyTorch’s developer surface includes engineering blog, getting-started guide, and 10 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

13.1/100 emerging ▬ flat Agent 1/100 human only Front door AI 5/6 prominent Heartbeat 100/100 alive Full breakdown ↓
scored 2026-11-13 · rubric v0.28.0 · Show this score on your site →

AccessFreemium

5 APIs

Artificial Intelligence Deep Learning Machine Learning Neural Networks Open Source Python

Kin Score

Kin Score How this is scored →
scored 2026-11-13 · rubric v0.28.0

Composite quality — 13.1/100 · emerging

Contract Quality 0.0 / 21

Developer Ergonomics 6.1 / 17

Access Clarity 1.1 / 17

Operational Transparency 1.3 / 11

Contract Governance 0.0 / 10

Discoverability 4.6 / 9

Regulatory Posture 0.0 / 15

Agent readiness — 1/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 0 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 7 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

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Agent readiness — checks not met, largest first (exact, from this profile):

Machine-Readable Contract +18 pts — api_specs includes an OpenAPI document Ask about this →
MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship Ask about this →
Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author Ask about this →
Machine-Readable Auth +10 pts — a served openid-configuration/oauth-authorization-server carrying `issuer` (served), else the securitySchemes class in the provider OpenAPIs — bound (mutualTLS, DPoP, Signature) > negotiable (oauth2 authorizationCode/clientCredentials, openIdConnect) > bearer (http bearer, apiKey) Ask about this →
Idempotency +9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented) Ask about this →
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes Ask about this →

12 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Contract Quality scores 0.0 — up to +25.0 composite points Ask about this →
Access Clarity scores 6.4 — up to +18.7 composite points Ask about this →
Developer Ergonomics scores 35.7 — up to +12.9 composite points Ask about this →

Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.

Not sure how this applies to your stack? Ask about any one item. Kin Lane reads every question, looks at your profile and your situation, and replies with what a fix would take — scoped to that one area, priced up front, yours whether you ship it or not. Ask a question →

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from the provider profile at artifacts.apis.io/pytorch/apis.yml: open an issue on github.com/api-evangelist/pytorch to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Markets this provider is in provisional

The Kin Score Market layer reads a provider inside its market: its position, and how deep its integrations go. Depth is only comparable within a position. Provisional and unweighted: not part of the Kin Score or agent readiness.

Market	Position	Depth	Registered MCP server	Declares compatibility
MLOps and AI/ML Infrastructure (LF AI & Data, MLPerf, Kubeflow)	standard	58.1	-	1
Agent Skills (SKILL.md packages)	standard	58.1	-	1
OpenSSF Best Practices Badge Holders	standard	58.1	-	1

Registered MCP server and declares compatibility are report only — not weighted. Undeclared means we found nothing we can read, so there is no number: we never infer integrations.

APIs 5

Individual APIs this provider publishes, each with its own machine-readable definition.

PyTorch Core API

Core PyTorch library for tensor computation and deep learning.

TorchVision API

Computer vision library for PyTorch with datasets, models, and transforms.

TorchText API

Natural language processing library for PyTorch.

TorchAudio API

Audio processing library for PyTorch.

PyTorch Hub API

Pre-trained model repository and discovery API.

PyTorch API pricing

Not yet checked against PyTorch's own pricing page. Compiled by API Evangelist on 2026-05-04; confirm before you rely on a figure. A free plan or tier is listed.

Plan	Price
Free freemium	Free
Professional paid	Free
Enterprise enterprise	—

Plan details, limits and sources →

Rate limits

Free Tier Default: 10 requests per minute
Free Tier Monthly Quota: 1000 requests per month
Professional Tier Default: 100 requests per minute

Compiled by API Evangelist, not yet checked against PyTorch's documentation. All 5 limits →

Pricing Plans 1

Published pricing tiers and plan structures.

PyTorch Plans Pricing

3 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

PyTorch Rate Limits

5 limits

RATE LIMITS

FinOps 1

Cost, billing, and metering signals for API financial operations.

PyTorch Finops

FINOPS

Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals.

PyTorch Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Get Started 1

Portal, sign-up, and the first successful call

GettingStarted

GettingStarted

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Operate 3

Status, limits, changes, and where to get help

RateLimits

RateLimits

Community

Community

Forums

Forums

Commercial 2

Pricing, plans, and the legal terms of use

Plans

Plans

FinOps

FinOps

Company 3

The organization behind the API

LinkedIn

Blog

Blog

Website

Website

Other 1

Properties that don't map to a standard resource type

Ecosystem

Ecosystem

Source (apis.yml)

aid: pytorch
name: PyTorch
description: APIs and resources for PyTorch, an open source machine learning framework for tensor computation and deep learning
  developed by Meta.
type: Index
deliveryModel:
  model: unknown
  license_evidence:
    repo: pytorch/pytorch
    spdx: NOASSERTION
    stars: 103293
  open_source: unknown
  commercial: false
  callable_host: false
  label: Delivery model not determined — needs a product licence on record
  confidence: low
  source:
  - repository-unlicensed
  generated: '2026-09-25'
  method: derived
accessModel:
  pricing: freemium
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Freemium
  confidence: low
  source:
  - plans
  - security
  generated: '2026-11-13'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/pytorch.png
tags:
- Artificial Intelligence
- Deep Learning
- Machine Learning
- Neural Networks
- Open Source
- Python
tags_raw:
- Artificial Intelligence
- Deep Learning
- Machine Learning
- Neural Networks
- Open Source
- Python
- Machine-Learning
- Open-Source
url: https://raw.githubusercontent.com/api-evangelist/pytorch/refs/heads/main/apis.yml
created: '2024-01-01'
modified: '2026-04-28'
specificationVersion: '0.23'
apis:
- aid: pytorch:pytorch-core-api
  name: PyTorch Core API
  description: Core PyTorch library for tensor computation and deep learning.
  humanURL: https://pytorch.org/docs/stable/index.html
  tags:
  - Deep Learning
  - Machine Learning
  - Tensors
  tags_raw:
  - Deep Learning
  - Machine Learning
  - Tensors
  - Machine-Learning
  properties:
  - type: Documentation
    url: https://pytorch.org/docs/stable/index.html
  - type: GettingStarted
    url: https://pytorch.org/get-started/locally/
  - type: APIReference
    url: https://pytorch.org/docs/stable/torch.html
  - type: GitHub
    url: https://github.com/pytorch/pytorch
- aid: pytorch:torchvision
  name: TorchVision API
  description: Computer vision library for PyTorch with datasets, models, and transforms.
  humanURL: https://pytorch.org/vision/stable/index.html
  tags:
  - Computer Vision
  - Image Processing
  - Models
  tags_raw:
  - Computer Vision
  - Image Processing
  - Models
  - Computer-Vision
  properties:
  - type: Documentation
    url: https://pytorch.org/vision/stable/index.html
  - type: GitHub
    url: https://github.com/pytorch/vision
  - type: Models
    url: https://pytorch.org/vision/stable/models.html
- aid: pytorch:torchtext
  name: TorchText API
  description: Natural language processing library for PyTorch.
  humanURL: https://pytorch.org/text/stable/index.html
  tags:
  - Natural Language Processing
  - Text Processing
  tags_raw:
  - NLP
  - Text Processing
  properties:
  - type: Documentation
    url: https://pytorch.org/text/stable/index.html
  - type: GitHub
    url: https://github.com/pytorch/text
- aid: pytorch:torchaudio
  name: TorchAudio API
  description: Audio processing library for PyTorch.
  humanURL: https://pytorch.org/audio/stable/index.html
  tags:
  - Audio
  - Speech Recognition
  properties:
  - type: Documentation
    url: https://pytorch.org/audio/stable/index.html
  - type: GitHub
    url: https://github.com/pytorch/audio
  - type: Tutorials
    url: https://pytorch.org/audio/stable/tutorials.html
- aid: pytorch:pytorch-hub
  name: PyTorch Hub API
  description: Pre-trained model repository and discovery API.
  humanURL: https://pytorch.org/hub/
  tags:
  - Pre-Trained Models
  - Model Hub
  - Transfer Learning
  properties:
  - type: Documentation
    url: https://pytorch.org/docs/stable/hub.html
  - type: Models
    url: https://pytorch.org/hub/research-models
common:
- type: Plans
  url: plans/pytorch-plans-pricing.yml
- type: RateLimits
  url: rate-limits/pytorch-rate-limits.yml
- type: FinOps
  url: finops/pytorch-finops.yml
- type: DomainSecurity
  url: security/pytorch-domain-security.yml
- type: LinkedIn
  url: https://www.linkedin.com/company/pytorch
- type: Blog
  url: https://pytorch.org/blog/
- type: Community
  url: https://pytorch.org/community
- type: Forums
  url: https://discuss.pytorch.org/
- type: GitHubOrganization
  url: https://github.com/pytorch
- type: GettingStarted
  url: https://pytorch.org/get-started/pytorch-2.0/
- type: Website
  url: https://pytorch.org
- type: Ecosystem
  url: https://pytorch.org/ecosystem
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com

PyTorch API — frequently asked questions

Does PyTorch have an API?

Yes. APIs.io lists 5 APIs from PyTorch, including PyTorch Core API, TorchVision API and TorchText API, and 2 more.

How much does the PyTorch API cost?

APIs.io lists 3 pricing plans for PyTorch, with each plan's price, limits and inclusions under Pricing on this page.

Is the PyTorch API ready for AI agents?

PyTorch has a Kin Score of 13.1 out of 100 (emerging) and an Agent Readiness score of 0.6 (human-only), scored by API Evangelist on 2026-11-13.

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