Telekinesis.ai

Telekinesis is a Physical AI company building a unified Python SDK and large-scale "skill library" for industrial robotics and computer vision. Its composable skills span perception (2D/3D detection, segmentation, 6D pose estimation, point clouds), planning, control, reinforcement learning, and Vision-Language-Model agents that orchestrate complete robotics systems. Developers install the official telekinesis-ai SDK from PyPI, authenticate with an API key from platform.telekinesis.ai, and run skills either cloud-hosted or on-premise. The stack includes BabyROS (a lightweight ROS-style pub/sub middleware on Zenoh), a Data Engine for synthetic dataset generation, and NVIDIA Isaac Sim integration for simulation-to-real transfer. Telekinesis targets high-mix manufacturing use cases such as assembly, machine tending, bin picking, CNC loading, and inspection, and is a Techstars portfolio company.

Telekinesis.ai is profiled on the APIs.io network. Tagged areas include Company, Robotics, Computer Vision, Physical AI, and Industrial Automation.

Telekinesis.ai’s developer surface includes documentation, API reference, getting-started guide, signup flow, support, authentication, and 9 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

22.9/100 emerging ▬ flat Agent 3/100 human only Front door AI 6/6 dominant Heartbeat 100/100 alive saas Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

0 APIs

Company Robotics Computer Vision Physical AI Industrial Automation Manufacturing Automation6D Pose EstimationMotion Planning Reinforcement Learning Python SDKEmbodied IntelligenceAgents

Kin Score

Kin Score How this is scored →
scored 2026-11-06 · rubric v0.27.0

Composite quality — 22.9/100 · emerging

Contract Quality 0.0 / 21

Developer Ergonomics 10.5 / 17

Access Clarity 3.6 / 17

Operational Transparency 0.3 / 11

Contract Governance 0.0 / 10

Discoverability 4.7 / 9

Regulatory Posture 2.7 / 15

Agent readiness — 3/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 10 / 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

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
MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship
Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author
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)
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0

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
Access Clarity scores 21.1 — up to +15.8 composite points
Operational Transparency scores 2.6 — up to +12.7 composite points

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.

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/telekinesisai/apis.yml: open an issue on github.com/api-evangelist/telekinesisai 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
ONVIF Video Analytics (video surveillance, edge cameras, computer vision)	distribution	9.6 (thin)	-	-

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.

Security Posture 2

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

Telekinesisai Authentication

apiKey · 1 scheme

SECURITY

Telekinesisai Domain Security

TLSv1.3 · DMARC

SECURITY

Resources

Get Started 3

Portal, sign-up, and the first successful call

DeveloperPortal

DeveloperPortal

GettingStarted

GettingStarted

SignUp

SignUp

Documentation 2

Reference material describing how the API behaves

Documentation

Documentation

APIReference

APIReference

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

Build 3

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Packages

Packages

SDKs

SDKs

Access & Security 2

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Authentication

Authentication

Operate 1

Status, limits, changes, and where to get help

Support

Support

Commercial 2

Pricing, plans, and the legal terms of use

TermsOfService

TermsOfService

PrivacyPolicy

PrivacyPolicy

Company 1

The organization behind the API

Website

Website

Source (apis.yml)

aid: telekinesisai
deliveryModel:
  model: saas
  open_source: false
  commercial: true
  callable_host: false
  label: Hosted service · you call their endpoint
  confidence: medium
  source:
  - pricing
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source:
  - authentication
  - security
  generated: '2026-11-06'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/telekinesisai.png
name: Telekinesis.ai
description: Telekinesis is a Physical AI company building a unified Python SDK and large-scale "skill library" for industrial
  robotics and computer vision. Its composable skills span perception (2D/3D detection, segmentation, 6D pose estimation,
  point clouds), planning, control, reinforcement learning, and Vision-Language-Model agents that orchestrate complete robotics
  systems. Developers install the official telekinesis-ai SDK from PyPI, authenticate with an API key from platform.telekinesis.ai,
  and run skills either cloud-hosted or on-premise. The stack includes BabyROS (a lightweight ROS-style pub/sub middleware
  on Zenoh), a Data Engine for synthetic dataset generation, and NVIDIA Isaac Sim integration for simulation-to-real transfer.
  Telekinesis targets high-mix manufacturing use cases such as assembly, machine tending, bin picking, CNC loading, and inspection,
  and is a Techstars portfolio company.
url: https://raw.githubusercontent.com/api-evangelist/telekinesisai/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- techstars
x-tier: stub
x-tier-reason: portfolio-lead
x-docs-platform: vitepress
x-docs-platform-confidence: medium
x-docs-platform-checked: 2026-10-17
x-docs-hosting: aws-cloudfront
x-enrichment:
  date: '2026-07-21'
  status: enriched
  artifacts_added: 5
  pass: local-v1
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-21'
tags:
- Company
- Robotics
- Computer Vision
- Physical AI
- Industrial Automation
- Manufacturing Automation
- 6D Pose Estimation
- Motion Planning
- Reinforcement Learning
- Python SDK
- Embodied Intelligence
- Agents
tags_raw:
- Company
- Robotics
- Computer Vision
- Physical AI
- Industrial Automation
- Manufacturing Automation
- 6D Pose Estimation
- Motion Planning
- Reinforcement Learning
- Python SDK
- Embodied Intelligence
- Agents
- Computer-Vision
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: DomainSecurity
  url: security/telekinesisai-domain-security.yml
- type: Website
  url: https://telekinesis.ai/
- type: DeveloperPortal
  url: https://platform.telekinesis.ai/
- type: Documentation
  url: https://docs.telekinesis.ai/
- type: APIReference
  url: https://docs.telekinesis.ai/skills/overview.html
- type: GettingStarted
  url: https://docs.telekinesis.ai/getting-started/quickstart
- type: SignUp
  url: https://platform.telekinesis.ai/api-keys
- type: GitHubOrganization
  url: https://github.com/telekinesis-ai
- type: Support
  url: https://telekinesis.ai/contact/
- type: TermsOfService
  url: https://docs.telekinesis.ai/legal/terms.html
- type: PrivacyPolicy
  url: https://docs.telekinesis.ai/legal/privacy.html
- type: Packages
  url: packages/telekinesisai-packages.yml
- type: SDKs
  url: packages/telekinesisai-packages.yml
- type: Authentication
  url: authentication/telekinesisai-authentication.yml
- type: LLMsTxt
  url: llms/telekinesisai-llms.txt

Telekinesis.ai API — frequently asked questions

How do I authenticate with the Telekinesis.ai API?

Telekinesis.ai uses bearer-token authentication (apiKey · 1 scheme). The details are profiled on APIs.io as Telekinesisai Authentication.

Is the Telekinesis.ai API ready for AI agents?

Telekinesis.ai has a Kin Score of 22.9 out of 100 (emerging) and an Agent Readiness score of 2.5 (human-only), scored by API Evangelist on 2026-11-06.

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