Mirai

Mirai (trymirai.com) is an on-device AI infrastructure company building uzu, a high-performance, Rust-based inference engine that runs large language, text-to-speech, and classification models directly on consumer hardware — Apple Silicon, iOS, macOS, and beyond — with zero inference cost and full data privacy. The uzu SDK exposes a single high-level API across Python, Swift, TypeScript, and Rust, adding speculative decoding, structured output, and built-in performance metrics, plus an optional cloud path for hosted models. Founded by the co-creators of Reface and Prisma and backed by a $10M seed round led by Uncork Capital.

Mirai is listed with 1 API on the APIs.io network. Tagged areas include Company, Artificial Intelligence, On-Device AI, Inference, and LLM.

Mirai’s developer surface includes documentation, getting-started guide, signup flow, engineering blog, support, and 9 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

19.9/100 emerging ▬ flat Agent 0/100 human only Front door AI 5/6 prominent Heartbeat 100/100 alive open core · MIT Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

1 API

Company Artificial Intelligence On-Device AI Inference LLM SDK Machine Learning Edge AI Apple Silicon Text-to-Speech

Kin Score

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

Composite quality — 19.9/100 · emerging

Contract Quality 0.0 / 19

Developer Ergonomics 5.4 / 15

Access Clarity 3.2 / 15

Operational Transparency 1.8 / 10

Contract Governance 0.0 / 9

Discoverability 4.8 / 8

Regulatory Posture 1.9 / 15

Open Source Surface 2.5 / 10

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 AI & Biometrics 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.

Open Source Surface applies to this provider. This product is open source and we read its repository directly, so Open Source Surface carries 10 points of the composite. It is scored from what the repository actually publishes — a security policy, a contribution guide, a release history, a code of conduct — read live from the provider rather than inferred from our own catalog pointers. This facet adds; nothing was taken away to make room for it. An open-source project is not excused from the commercial facets, because exemption would strip it of the points it does earn. If we have the wrong repository, or this product is not open source, say so on your provider repo and we will drop the facet rather than have you publish against 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 75 points between them, because both conditional facets apply and carry 25 points together. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 75% 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
Access Clarity scores 21.1 — up to +15.8 composite points
Developer Ergonomics scores 35.7 — up to +12.9 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/mirai/apis.yml: open an issue on github.com/api-evangelist/mirai 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
Apple Accessory & Device Ecosystem (MFi, Matter, Apple partners)	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.

APIs 1

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

Mirai uzu Inference SDK

uzu is Mirai's first-party, high-performance on-device inference engine. A single high-level API — shared across Python, Swift, TypeScript, and Rust — downloads and runs LLM, te...

Security Posture 1

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

Mirai Domain Security

TLSv1.2 · HSTS · DMARC

SECURITY

Resources

Get Started 3

Portal, sign-up, and the first successful call

DeveloperPortal

DeveloperPortal

GettingStarted

GettingStarted

SignUp

SignUp

Documentation 1

Reference material describing how the API behaves

Documentation

Documentation

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Operate 3

Status, limits, changes, and where to get help

IssueTracker

IssueTracker

Releases

Releases

Support

Support

Commercial 3

Pricing, plans, and the legal terms of use

License

License

TermsOfService

TermsOfService

PrivacyPolicy

PrivacyPolicy

Company 2

The organization behind the API

Website

Website

Blog

Blog

Source (apis.yml)

aid: mirai
name: Mirai
description: Mirai (trymirai.com) is an on-device AI infrastructure company building uzu, a high-performance, Rust-based inference
  engine that runs large language, text-to-speech, and classification models directly on consumer hardware — Apple Silicon,
  iOS, macOS, and beyond — with zero inference cost and full data privacy. The uzu SDK exposes a single high-level API across
  Python, Swift, TypeScript, and Rust, adding speculative decoding, structured output, and built-in performance metrics, plus
  an optional cloud path for hosted models. Founded by the co-creators of Reface and Prisma and backed by a $10M seed round
  led by Uncork Capital.
url: https://raw.githubusercontent.com/api-evangelist/mirai/refs/heads/main/apis.yml
deliveryModel:
  model: open-core
  license: MIT
  open_source: true
  commercial: true
  callable_host: false
  label: Open core · an OSS project plus a commercial hosted product
  confidence: high
  source:
  - license
  - pricing
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source:
  - security
  generated: '2026-11-06'
  method: derived
image: https://trymirai.com/images/og/default.jpg
x-type: company
x-source: vc-portfolio
x-backed-by:
- uncork-capital
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-20'
x-docs-platform: mintlify
x-docs-platform-confidence: medium
x-docs-platform-checked: 2026-10-04
x-docs-hosting: vercel
tags:
- Company
- Artificial Intelligence
- On-Device AI
- Inference
- LLM
- SDK
- Machine Learning
- Edge AI
- Apple Silicon
- Text-to-Speech
tags_raw:
- Company
- Artificial Intelligence
- On-Device AI
- Inference
- LLM
- SDK
- Machine Learning
- Edge AI
- Apple Silicon
- Text to Speech
- Machine-Learning
apis:
- name: Mirai uzu Inference SDK
  description: uzu is Mirai's first-party, high-performance on-device inference engine. A single high-level API — shared across
    Python, Swift, TypeScript, and Rust — downloads and runs LLM, text-to-speech, and classification models locally, with
    speculative decoding, structured output, and an optional cloud path.
  humanURL: https://docs.trymirai.com
  tags:
  - On-Device AI
  - Inference
  - SDK
  properties:
  - type: Documentation
    url: https://docs.trymirai.com
  - type: GettingStarted
    url: https://docs.trymirai.com/quick-start/chat
  - type: Packages
    url: packages/mirai-packages.yml
  - type: SDKs
    url: packages/mirai-packages.yml
  - type: LLMsTxt
    url: llms/mirai-llms.txt
  - type: SourceCode
    url: https://github.com/trymirai/uzu
common:
- type: IssueTracker
  url: https://github.com/trymirai/uzu/issues
- type: Releases
  url: https://github.com/trymirai/uzu/releases
- type: License
  name: MIT
  url: https://github.com/trymirai/uzu/blob/main/LICENSE
- type: DomainSecurity
  url: security/mirai-domain-security.yml
- type: Website
  url: https://trymirai.com
- type: DeveloperPortal
  url: https://platform.trymirai.com
- type: Documentation
  url: https://docs.trymirai.com
- type: GettingStarted
  url: https://docs.trymirai.com/quick-start/chat
- type: SignUp
  url: https://platform.trymirai.com
- type: GitHubOrganization
  url: https://github.com/trymirai
- type: Blog
  url: https://trymirai.com/blog
- type: Support
  url: https://discord.com/invite/trymirai
- type: TermsOfService
  url: https://trymirai.com/terms-of-use
- type: PrivacyPolicy
  url: https://trymirai.com/privacy-policy
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-07-20'
  status: enriched
  artifacts_added: 3
  pass: local-v1

Mirai API — frequently asked questions

Does Mirai have an API?

Yes. APIs.io lists 1 API from Mirai, including Mirai uzu Inference SDK.

Is the Mirai API ready for AI agents?

Mirai has a Kin Score of 19.9 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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