TensorFlow website screenshot

TensorFlow

TensorFlow is an end-to-end open source machine learning platform developed by Google. It provides a comprehensive ecosystem of tools, libraries, and community resources for building and deploying ML-powered applications, including model training, serving, mobile/edge deployment, and a hub of pre-trained models. TensorFlow Serving exposes REST and gRPC APIs for production model inference.

TensorFlow publishes 2 APIs on the APIs.io network: Inference API and Models API. Tagged areas include AI, Deep Learning, JavaScript, Machine Learning, and Model Serving.

The TensorFlow catalog on APIs.io includes 1 JSON-LD context and 2 Spectral governance rulesets.

TensorFlow’s developer surface includes authentication, sandbox, changelog, CLI, engineering blog, YouTube channel, Stack Overflow tag, and 27 more developer resources.

48.0/100 developing ▬ flat Agent 41/100 agent ready Full breakdown ↓
scored 2026-07-28 · rubric v0.6
AccessFreeOpen⚡ Free to try
7 APIs 1 MCP Servers
AIDeep LearningJavaScriptMachine LearningModel ServingNeural NetworksOpen SourcePython

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-07-28 · rubric v0.6
Composite quality — 48.0/100 · developing
Contract Quality 14.2 / 25
Developer Ergonomics 5.7 / 20
Commercial Clarity 5.8 / 20
Operational Transparency 5.5 / 13
Governance 9.6 / 12
Discoverability 7.2 / 10
Agent readiness — 41/100 · agent ready
Machine-Readable Contract 18 / 18
Agentic Access Contract 10 / 10
MCP Server 12 / 12
Machine-Readable Auth 10 / 10
Idempotency 0 / 9
Stable Error Semantics 8 / 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
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. This rating is computed from github.com/api-evangelist/tensorflow: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

APIs 7

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

TensorFlow Core API

The foundational Python and C++ API for building and training machine learning models using TensorFlow.

TensorFlow.js API

A JavaScript library for training and deploying ML models in the browser and on Node.js.

TensorFlow Lite API

Lightweight solution for ML inference on mobile and embedded devices, optimized for on-device model execution.

TensorFlow Hub API

A library and repository of reusable pre-trained machine learning modules, enabling transfer learning across text, image, video, and audio domains.

TensorBoard API

TensorFlow's visualization toolkit for experiment tracking, model debugging, and performance profiling via an embedded web server with REST endpoints.

TensorFlow Inference API

Model inference operations including classify, regress, and predict

TensorFlow Models API

Model status and metadata operations

Scroll for all 7

Open Collections 1

Open, tool-agnostic API collections (OpenAPI-derived and Bruno).

Arazzo Workflows 7

Multi-step API workflows described with the Arazzo specification.

TensorFlow Serving Preflight and Classify

Confirm a model is loaded and its signature is known before running classification inference.

ARAZZO

TensorFlow Serving Route Inference by Version Label

Resolve a version label such as stable or canary to a concrete version, then run inference pinned to it.

ARAZZO

TensorFlow Serving Pinned Reproducible Example Scoring

Pin a model version and score the same tf.Example inputs through both its classify and regress signatures.

ARAZZO

TensorFlow Serving Preflight and Predict

Confirm a model is loaded and its signature is known before running prediction inference.

ARAZZO

TensorFlow Serving Preflight and Regress

Confirm a model is loaded and its signature is known before running regression inference.

ARAZZO

TensorFlow Serving Gate a Rollout on Version Readiness

Poll a newly exported model version until it reports AVAILABLE, then smoke test it before traffic is shifted.

ARAZZO

TensorFlow Serving Compare a Candidate Version Against the Default

Score the same instances against a pinned candidate version and the default version to measure rollout drift.

ARAZZO

Scroll for all 7

MCP Servers 1

Model Context Protocol servers that expose these APIs to AI agents.

tensorflow-mcp.yml

MCP SERVER

Pricing Plans 1

Published pricing tiers and plan structures.

Rate Limits 1

Documented rate limits and quota policies.

Tensorflow Rate Limits

1 limits

RATE LIMITS

FinOps 1

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

Semantic Vocabularies 1

JSON-LD contexts and semantic vocabularies used across these APIs.

Tensorflow Context

4 classes · 15 properties

JSON-LD

Spectral Rules 2

Spectral governance rulesets for linting and validating these APIs.

TensorFlow API Rules

5 rules · 4 warnings 1 info

SPECTRAL

TensorFlow API Rules

13 rules · 5 errors 7 warnings

SPECTRAL

JSON Schema 3

Standalone JSON Schema definitions for this provider's data models.

TensorFlow Serving Prediction Request

3 properties

JSON SCHEMA

JSON Structure 1

JSON Structure definitions describing this provider's data shapes.

Examples 2

Example request and response payloads for these APIs.

Security Posture 2

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

Tensorflow Authentication

0 schemes

SECURITY

Tensorflow Domain Security

TLSv1.3 · HSTS

SECURITY

Agentic Access 1

Recommended x-agentic-access execution contracts for AI agents.

Tensorflow Agentic Access

11 operations · 6 acting

11 operations · 6 acting

AGENTIC

Resources

Get Started 1

Portal, sign-up, and the first successful call

Documentation 1

Reference material describing how the API behaves

Agent Surfaces 3

MCP servers, agent skills, and machine-readable catalogs

Design & Contract 13

Pagination, idempotency, versioning, errors, and events

Scroll for all 13

Build 3

SDKs, sample code, and the tooling you integrate with

Access & Security 2

Authentication, authorization, and security posture

Learn 1

Tutorials, courses, talks, and written guidance

Operate 3

Status, limits, changes, and where to get help

Commercial 1

Pricing, plans, and the legal terms of use

Company 3

The organization behind the API

Other 3

Properties that don't map to a standard resource type

Source (apis.yml)

apis.yml Raw ↑
aid: tensorflow
name: TensorFlow
description: TensorFlow is an end-to-end open source machine learning platform developed by Google. It provides a comprehensive
  ecosystem of tools, libraries, and community resources for building and deploying ML-powered applications, including model
  training, serving, mobile/edge deployment, and a hub of pre-trained models. TensorFlow Serving exposes REST and gRPC APIs
  for production model inference.
type: Index
accessModel:
  pricing: free
  onboarding: open
  trial: false
  try_now: true
  public: true
  label: Free · Open access
  confidence: high
  source:
  - plans
  - authentication
  generated: '2026-07-22'
  method: derived
image: https://www.tensorflow.org/images/tf_logo_social.png
tags:
- AI
- Deep Learning
- JavaScript
- Machine Learning
- Model Serving
- Neural Networks
- Open Source
- Python
url: https://raw.githubusercontent.com/api-evangelist/tensorflow/refs/heads/main/apis.yml
created: '2024-01-15'
modified: '2026-06-20'
specificationVersion: '0.19'
apis:
- aid: tensorflow:tensorflow-core
  name: TensorFlow Core API
  description: The foundational Python and C++ API for building and training machine learning models using TensorFlow.
  humanURL: https://www.tensorflow.org/api_docs/python/tf
  baseURL: https://www.tensorflow.org/api_docs
  tags:
  - Core API
  - Machine Learning
  - Python
  properties:
  - type: Documentation
    url: https://www.tensorflow.org/api_docs/python/tf
  - type: Tutorial
    url: https://www.tensorflow.org/tutorials
  - type: GitHub
    url: https://github.com/tensorflow/tensorflow
  - type: Guide
    url: https://www.tensorflow.org/guide
- aid: tensorflow:tensorflow-js
  name: TensorFlow.js API
  description: A JavaScript library for training and deploying ML models in the browser and on Node.js.
  humanURL: https://js.tensorflow.org/
  baseURL: https://cdn.jsdelivr.net/npm/@tensorflow/tfjs
  tags:
  - Browser
  - JavaScript
  - Node.js
  properties:
  - type: Documentation
    url: https://js.tensorflow.org/api/latest/
  - type: Tutorial
    url: https://js.tensorflow.org/tutorials/
  - type: GitHub
    url: https://github.com/tensorflow/tfjs
  - type: NPM
    url: https://www.npmjs.com/package/@tensorflow/tfjs
- aid: tensorflow:tensorflow-lite
  name: TensorFlow Lite API
  description: Lightweight solution for ML inference on mobile and embedded devices, optimized for on-device model execution.
  humanURL: https://www.tensorflow.org/lite
  baseURL: https://www.tensorflow.org/lite/api_docs
  tags:
  - Edge Computing
  - Embedded
  - Mobile
  - On-Device AI
  properties:
  - type: Documentation
    url: https://www.tensorflow.org/lite/api_docs
  - type: Guide
    url: https://www.tensorflow.org/lite/guide
  - type: Examples
    url: https://www.tensorflow.org/lite/examples
  - type: GitHub
    url: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite
- aid: tensorflow:tensorflow-hub
  name: TensorFlow Hub API
  description: A library and repository of reusable pre-trained machine learning modules, enabling transfer learning across
    text, image, video, and audio domains.
  humanURL: https://tfhub.dev/
  baseURL: https://tfhub.dev/
  tags:
  - Model Repository
  - Pre-Trained Models
  - Transfer Learning
  properties:
  - type: Documentation
    url: https://www.tensorflow.org/hub/api_docs/python/hub
  - type: Models
    url: https://tfhub.dev/
  - type: GitHub
    url: https://github.com/tensorflow/hub
- aid: tensorflow:tensorboard
  name: TensorBoard API
  description: TensorFlow's visualization toolkit for experiment tracking, model debugging, and performance profiling via
    an embedded web server with REST endpoints.
  humanURL: https://www.tensorflow.org/tensorboard
  tags:
  - Model Debugging
  - Monitoring
  - Visualization
  properties:
  - type: Documentation
    url: https://www.tensorflow.org/tensorboard/get_started
  - type: GitHub
    url: https://github.com/tensorflow/tensorboard
- aid: tensorflow:tensorflow-inference-api
  name: TensorFlow Inference API
  description: Model inference operations including classify, regress, and predict
  humanURL: https://www.tensorflow.org/tfx/serving/api_rest
  baseURL: http://host:8501
  tags:
  - Inference
  properties:
  - type: OpenAPI
    url: openapi/tensorflow-inference-api-openapi.yml
  - type: Documentation
    url: https://www.tensorflow.org/tfx/serving/api_rest
  - type: GitHub
    url: https://github.com/tensorflow/serving
- aid: tensorflow:tensorflow-models-api
  name: TensorFlow Models API
  description: Model status and metadata operations
  humanURL: https://www.tensorflow.org/tfx/serving/api_rest
  baseURL: http://host:8501
  tags:
  - Models
  properties:
  - type: OpenAPI
    url: openapi/tensorflow-models-api-openapi.yml
  - type: Documentation
    url: https://www.tensorflow.org/tfx/serving/api_rest
  - type: GitHub
    url: https://github.com/tensorflow/serving
maintainers:
- FN: Google Brain Team
  email: tensorflow@googlegroups.com
  url: https://www.tensorflow.org/
include:
- name: TensorFlow Community
  url: https://www.tensorflow.org/community
common:
- type: AgenticAccess
  url: agentic-access/tensorflow-agentic-access.yml
- type: DomainSecurity
  url: security/tensorflow-domain-security.yml
- type: Packages
  url: packages/tensorflow-packages.yml
- type: MCPServer
  url: mcp/tensorflow-mcp.yml
- type: LLMsTxt
  url: llms/tensorflow-llms.txt
- type: Overlay
  url: overlays/tensorflow-serving-overlay.yaml
- type: Protobuf
  url: grpc/tensorflow-prediction-service.proto
- type: Protobuf
  url: grpc/tensorflow-model-service.proto
- type: Conformance
  url: conformance/tensorflow-conformance.yml
- type: ErrorCatalog
  url: errors/tensorflow-problem-types.yml
- type: Lifecycle
  url: lifecycle/tensorflow-lifecycle.yml
- type: Authentication
  url: authentication/tensorflow-authentication.yml
- type: Sandbox
  url: sandbox/tensorflow-sandbox.yml
- type: Conventions
  url: conventions/tensorflow-conventions.yml
- type: ChangeLog
  url: changelog/tensorflow-changelog.yml
- type: CLI
  url: cli/tensorflow-cli.yml
- type: DataModel
  url: data-model/tensorflow-data-model.yml
- type: Arazzo
  url: arazzo/tensorflow-predict-preflight-workflow.yml
  name: TensorFlow Preflight a Model and Run Prediction
- type: Arazzo
  url: arazzo/tensorflow-classify-preflight-workflow.yml
  name: TensorFlow Preflight a Model and Run Classification
- type: Arazzo
  url: arazzo/tensorflow-regress-preflight-workflow.yml
  name: TensorFlow Preflight a Model and Run Regression
- type: Arazzo
  url: arazzo/tensorflow-version-canary-compare-workflow.yml
  name: TensorFlow Compare a Candidate Version Against the Default
- type: Arazzo
  url: arazzo/tensorflow-label-routed-inference-workflow.yml
  name: TensorFlow Route Inference by Version Label
- type: Arazzo
  url: arazzo/tensorflow-pinned-example-scoring-workflow.yml
  name: TensorFlow Pinned Reproducible Example Scoring
- type: Arazzo
  url: arazzo/tensorflow-rollout-readiness-workflow.yml
  name: TensorFlow Gate a Rollout on Version Readiness
- type: LinkedIn
  url: https://www.linkedin.com/showcase/tensorflowdev
- type: Blog
  url: https://blog.tensorflow.org/
- type: GitHubOrg
  url: https://github.com/tensorflow
- type: Twitter
  url: https://twitter.com/tensorflow
- type: YouTube
  url: https://www.youtube.com/tensorflow
- type: License
  url: https://github.com/tensorflow/tensorflow/blob/master/LICENSE
- type: Forums
  url: https://discuss.tensorflow.org/
- type: StackOverflow
  url: https://stackoverflow.com/questions/tagged/tensorflow
- type: OpenAPI
  url: https://raw.githubusercontent.com/api-evangelist/tensorflow/refs/heads/main/openapi/tensorflow-serving-openapi.yml
- type: Vocabulary
  url: https://raw.githubusercontent.com/api-evangelist/tensorflow/refs/heads/main/vocabulary/tensorflow-vocabulary.yml