Physical Intelligence

Physical Intelligence (often styled "Pi" or "π") is a San Francisco-based research company building general-purpose foundation models for robotics with the stated goal of producing learning algorithms that can control any robot to do any task. The company has published a continuing line of Vision-Language-Action (VLA) models: π0 (October 2024, first generalist multi-task multi-robot policy), π0-FAST (autoregressive variant via Real-time Action Chunking / FAST tokenization), π0.5 (April 2025, open-world generalization), π*0.6 (November 2025, reinforcement-learning from experience), and π0.7 (April 2026, steerable model with emergent capabilities). Physical Intelligence releases significant work as open source: the openpi repository (~12K stars) is the canonical home for π0 weights and code, with companion repos including real-time-chunking-kinetix, pi-data-sharing, aloha, augmax, and rlds_dataset_builder. The company does not yet offer a hosted commercial API; access to the platform is via open-weight models and research collaborations.

Physical Intelligence is profiled on the APIs.io network. Tagged areas include Robotics, Foundation Models, Vision Language Action, Embodied AI, and Reinforcement Learning.

Physical Intelligence’s developer surface includes engineering blog and 9 more developer resources.

🌐 Visit website 📡 Source on GitHub

3.1/100 minimal ▬ flat Agent 0/100 human only Full breakdown ↓
scored 2026-09-08 · rubric v0.20.0

0 APIs

Robotics Foundation Models Vision Language Action Embodied AI Reinforcement LearningImitation LearningOpen-Source Open Weightspi0openpiManipulationGeneralist Policy

Kin Score

Kin Score How this is scored →
scored 2026-09-08 · rubric v0.20.0

Composite quality — 3.1/100 · minimal

Contract Quality 0.0 / 21

Developer Ergonomics 0.4 / 17

Access Clarity 0.0 / 17

Operational Transparency 0.3 / 11

Contract Governance 0.0 / 10

Discoverability 4.3 / 9

Regulatory Posture 1.4 / 15

Agent readiness — 0/100 · 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 0 / 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

Regulatory Posture applies to this provider. Its tags matched the Insurance 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/.

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 github.com/api-evangelist/physical-intelligence: open an issue to ask a question, or submit a pull request to add artifacts. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Security Posture 1

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

Physical Intelligence Domain Security

TLSv1.3 · HSTS · DNSSEC · DMARC

SECURITY

Resources

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Company 6

The organization behind the API

Website

AlternateWebsite

Blog

Careers

Twitter

LinkedIn

Other 2

Properties that don't map to a standard resource type

Research

Research

OpenPi

OpenPi

Source (apis.yml)

aid: physical-intelligence
name: Physical Intelligence
description: 'Physical Intelligence (often styled "Pi" or "π") is a San Francisco-based research company building general-purpose
  foundation models for robotics with the stated goal of producing learning algorithms that can control any robot to do any
  task. The company has published a continuing line of Vision-Language-Action (VLA) models: π0 (October 2024, first generalist
  multi-task multi-robot policy), π0-FAST (autoregressive variant via Real-time Action Chunking / FAST tokenization), π0.5
  (April 2025, open-world generalization), π*0.6 (November 2025, reinforcement-learning from experience), and π0.7 (April
  2026, steerable model with emergent capabilities). Physical Intelligence releases significant work as open source: the openpi
  repository (~12K stars) is the canonical home for π0 weights and code, with companion repos including real-time-chunking-kinetix,
  pi-data-sharing, aloha, augmax, and rlds_dataset_builder. The company does not yet offer a hosted commercial API; access
  to the platform is via open-weight models and research collaborations.'
type: Index
deliveryModel:
  model: unknown
  open_source: false
  commercial: false
  callable_host: false
  label: Delivery model not determined — needs a product licence on record
  confidence: low
  source:
  - none
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source:
  - url: https://www.physicalintelligence.company
    status: 308
    note: declared website redirects to https://www.pi.website/ — a different registrable domain (physicalintelligence.company
      -> pi.website), possible rename or acquisition (probed 2026-09-03, roadmap#169)
  generated: '2026-07-22'
  method: derived
position: Producing
access: 3rd-Party
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/physical-intelligence.png
tags:
- Robotics
- Foundation Models
- Vision Language Action
- Embodied AI
- Reinforcement Learning
- Imitation Learning
- Open-Source
- Open Weights
- pi0
- openpi
- Manipulation
- Generalist Policy
tags_raw:
- Robotics
- Foundation Models
- Vision Language Action
- Embodied AI
- Reinforcement Learning
- Imitation Learning
- Open Source
- Open Weights
- pi0
- openpi
- Manipulation
- Generalist Policy
url: https://raw.githubusercontent.com/api-evangelist/physical-intelligence/refs/heads/main/apis.yml
created: '2026-05-23'
modified: '2026-05-23'
specificationVersion: '0.23'
apis: []
common:
- type: DomainSecurity
  url: security/physical-intelligence-domain-security.yml
- type: Website
  url: https://www.physicalintelligence.company
- type: AlternateWebsite
  url: https://www.pi.website
- type: Blog
  url: https://www.pi.website/blog
- type: Research
  url: https://www.pi.website/research
- type: GitHubOrganization
  url: https://github.com/Physical-Intelligence
- type: OpenPi
  url: https://github.com/Physical-Intelligence/openpi
- type: Careers
  url: https://www.pi.website/careers
- type: Twitter
  url: https://twitter.com/physical_int
- type: LinkedIn
  url: https://www.linkedin.com/company/physical-intelligence
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com

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