Causal

Causal Labs builds a Large Physics foundation Model (LPM) to learn causality through physics and weather, aiming for general causal intelligence. The company focuses on understanding how actions influence the world, advancing AI by integrating causal reasoning. It offers APIs for causal inference, simulation, and data-driven insights, supporting developers and researchers in building causally-aware applications.

Causal is listed with 1 API on the APIs.io network. Tagged areas include AI, Causal-Intelligence, Machine-Learning, Physics, and Weather.

Causal’s developer surface includes engineering blog and 5 more developer resources.

🌐 Visit website📡 Profile data on GitHub
7.4/100 minimal Agent 0/100 human only Full breakdown ↓
scored 2026-10-31 · rubric v0.25.0 · Show this score on your site →
1 API
AICausal-IntelligenceMachine-LearningPhysicsWeather

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-10-31 · rubric v0.25.0
Composite quality — 7.4/100 · minimal
Contract Quality 0.0 / 21
Developer Ergonomics 0.4 / 17
Access Clarity 1.8 / 17
Operational Transparency 0.0 / 11
Contract Governance 0.0 / 10
Discoverability 4.9 / 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
Pagination 5 / 5
Distinct Operations 3 / 3
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/.
Show this score on your site. Kin Score badge preview for Causal It updates itself when the score changes. Paste this where you link to your API docs: 
<a href="https://apis.io/providers/getcausal-ai/"><img src="https://apis.io/badge/getcausal-ai/card.svg" alt="Causal on APIs.io — Kin Score by API Evangelist"></a>
Other styles, a README pill and a shields.io endpoint are on the badge page.
Raise your score.
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
  • 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)
  • 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)
  • A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0
13 more unmet checks in the Agent Readiness table above.
Kin Score — facets with the most composite points still available (weight × headroom):
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/getcausal-ai/apis.yml: open an issue on github.com/api-evangelist/getcausal-ai 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 →

APIs 1

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

Causal API

Causal Labs provides APIs for causal inference, simulation, and data-driven insights as described on their website.

Security Posture 1

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

Getcausal Ai Domain Security

TLSv1.3 · HSTS

SECURITY

Resources

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity
DomainSecurity

Commercial 1

Pricing, plans, and the legal terms of use

PrivacyPolicy
PrivacyPolicy

Company 2

The organization behind the API

Blog
Blog
 Website
Website

Other 2

Properties that don't map to a standard resource type

Hosts
Hosts
 Vendors
Vendors

Source (apis.yml)

apis.yml Raw ↑ 
aid: getcausal-ai
name: Causal
image: https://cdn.prod.website-files.com/6977a9c6055cd296356ce1d0/69786945ff11eafe3793296d_44720ecd47dcbec811e851d0560d3a99_Open%20Graph.png
description: Causal Labs builds a Large Physics foundation Model (LPM) to learn causality through physics and weather, aiming
  for general causal intelligence. The company focuses on understanding how actions influence the world, advancing AI by integrating
  causal reasoning. It offers APIs for causal inference, simulation, and data-driven insights, supporting developers and researchers
  in building causally-aware applications.
url: https://raw.githubusercontent.com/api-evangelist/getcausal-ai/refs/heads/main/apis.yml
x-type: company
x-source: harvest:vc-portfolio
x-tier: profiled
x-tier-reason: enrichment pass 2026-10-26 (modular-v1; was harvest)
specificationVersion: '0.20'
created: '2026-10-25'
modified: '2026-10-25'
tags:
- AI
- Causal-Intelligence
- Machine-Learning
- Physics
- Weather
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Hosts
  url: hosts/getcausal-ai-hosts.yml
- type: Vendors
  url: vendors/getcausal-ai-vendors.yml
- type: PrivacyPolicy
  url: https://www.causallabs.ai/privacy-policy
- type: Blog
  url: https://blog.getcausal.ai/
- type: DomainSecurity
  url: security/getcausal-ai-domain-security.yml
- type: Website
  url: https://getcausal.ai/
apis:
- name: Causal API
  description: Causal Labs provides APIs for causal inference, simulation, and data-driven insights as described on their
    website.
  humanURL: https://www.causallabs.ai/
  aid: getcausal-ai:causal-api
x-backed-by:
- kindred-ventures
x-coverage:
  state: unreadable
  reason: js-rendered-docs
  detail: Main site renders via JavaScript and provides no machine‑readable OpenAPI spec.
  evidence:
  - url: https://www.causallabs.ai/
    status: 200
  checked: '2026-10-26'
x-enrichment:
  date: '2026-10-26'
  status: enriched
  artifacts_added: 7
  pass: modular-v1
  engine: identity=local,contract=local,surfaces=local,docs=local,regulatory=local

Causal API — frequently asked questions

Does Causal have an API?

Yes. APIs.io lists 1 API from Causal, including Causal API.

Is the Causal API ready for AI agents?

Causal has a Kin Score of 7.4 out of 100 (minimal) and an Agent Readiness score of 0.0 (human-only), scored by API Evangelist on 2026-10-31.

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