Experiential Labs

Experiential Labs is an applied AI research laboratory (Y Combinator Summer 2026, San Francisco) building world models that let AI agents simulate reality with high fidelity. Founded by Kion Fallah (former staff research scientist at Waabi) and Silen Naihin (AutoGPT and agent-benchmark contributor, co-founder of Stackwise), the company adapts techniques from autonomous-vehicle development to reconstruct environments at high accuracy and pursue recursive self-improvement and continual learning across domains. Its public work is open source on GitHub, including the world-model-harness (world-model-as-a-harness for simulating AI agent environments), OpenCLaaS (Continual Learning as a Service), and autoresearch-distillation. The company does not yet publish a public API, developer portal, or SDKs.

Experiential Labs is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Machine Learning, World Models, and AI Agents.

Experiential Labs’ developer surface includes pricing, engineering blog, and 4 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

11.6/100 emerging ▬ flat Agent 0/100 human only Front door AI 5/6 prominent Heartbeat 100/100 alive Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

AccessFreemium

0 APIs

Company Artificial Intelligence Machine Learning World Models AI Agents Simulation Continual Learning Infrastructure Research

Kin Score

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

Composite quality — 11.6/100 · emerging

Contract Quality 0.0 / 21

Developer Ergonomics 0.4 / 17

Access Clarity 6.3 / 17

Operational Transparency 0.3 / 11

Contract Governance 0.0 / 10

Discoverability 4.1 / 9

Regulatory Posture 0.8 / 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

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
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)
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes

13 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
Developer Ergonomics scores 2.4 — up to +19.5 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/experiential-labs/apis.yml: open an issue on github.com/api-evangelist/experiential-labs 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 →

Experiential Labs API pricing

Prices published by Experiential Labs, read 2026-10-07 from www.experientiallabs.ai/pricing.

Plan	Price
Free	$0 per month Includes: 500 credits a month once you verify a card (a one-time $1 charge, added to your balance)
Pro	$20 per month Includes: 2,000 credits / mo (slider up to 1,000,000 credits / mo); Tier 2 limits
Enterprise	Custom scoped with your team Includes: Committed credits at the lowest rate, SSO, private networking, SOC 1

Plan details, limits and sources →

Pricing Plans 1

Published pricing tiers and plan structures.

Experiential Labs Plans Pricing

3 plans

PLANS

Security Posture 1

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

Experiential Labs Domain Security

TLSv1.3 · HSTS · 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

Commercial 2

Pricing, plans, and the legal terms of use

Pricing

Pricing

Plans

Plans

Company 2

The organization behind the API

Blog

Blog

Website

Website

Source (apis.yml)

aid: experiential-labs
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: freemium
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Freemium
  confidence: low
  source:
  - plans
  - security
  generated: '2026-11-06'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/experiential-labs.png
name: Experiential Labs
description: Experiential Labs is an applied AI research laboratory (Y Combinator Summer 2026, San Francisco) building world
  models that let AI agents simulate reality with high fidelity. Founded by Kion Fallah (former staff research scientist at
  Waabi) and Silen Naihin (AutoGPT and agent-benchmark contributor, co-founder of Stackwise), the company adapts techniques
  from autonomous-vehicle development to reconstruct environments at high accuracy and pursue recursive self-improvement and
  continual learning across domains. Its public work is open source on GitHub, including the world-model-harness (world-model-as-a-harness
  for simulating AI agent environments), OpenCLaaS (Continual Learning as a Service), and autoresearch-distillation. The company
  does not yet publish a public API, developer portal, or SDKs.
url: https://raw.githubusercontent.com/api-evangelist/experiential-labs/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- y-combinator
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-10-06'
tags:
- Company
- Artificial Intelligence
- Machine Learning
- World Models
- AI Agents
- Simulation
- Continual Learning
- Infrastructure
- Research
tags_raw:
- Company
- Artificial Intelligence
- Machine Learning
- World Models
- AI Agents
- Simulation
- Continual Learning
- Infrastructure
- Research
- Machine-Learning
apis: []
common:
- type: Pricing
  url: https://www.experientiallabs.ai/pricing
- type: Plans
  url: plans/experiential-labs-plans-pricing.yml
- url: https://www.experientiallabs.ai/blog
  type: Blog
- type: DomainSecurity
  url: security/experiential-labs-domain-security.yml
- type: Website
  url: https://experientiallabs.ai
- type: GitHubOrganization
  url: https://github.com/experientiallabs
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

Experiential Labs API — frequently asked questions

How much does the Experiential Labs API cost?

Experiential Labs publishes its prices; its Free plan is $0 per month as of 2026-10-07. APIs.io lists 3 pricing plans for Experiential Labs, with each plan's price, limits and inclusions under Pricing on this page.

Is the Experiential Labs API ready for AI agents?

Experiential Labs has a Kin Score of 11.6 out of 100 (emerging) and an Agent Readiness score of 0.0 (human-only), scored by API Evangelist on 2026-11-06.

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