Lightberry

Lightberry is a San Francisco robotics software company (Y Combinator Fall 2025) that builds conversational AI "personality" software for humanoid and quadruped robots. Its platform lets a robot listen, speak, see its surroundings, and act on natural-language instruction without the owner writing any code, emphasizing emotional intelligence, contextual awareness, and autonomy rather than a simple speech-to-text voice pipeline. Lightberry ships pre-installed on robots sold through its partnership with Unitree, and the company runs a manufacturer-compatibility program with robot makers including Fourier, Booster Robotics, and High Torque. Its published documentation today covers device setup (power-on, WiFi provisioning, starting a conversation) plus an MCP architecture note describing a Model Context Protocol server that routes tool calls to individual Lightberry devices, which each expose a REST endpoint. There is no public developer portal, API reference, SDK, or OpenAPI definition at this time.

Lightberry is profiled on the APIs.io network. Tagged areas include Company, Robotics, Artificial Intelligence, Conversational AI, and Voice.

Lightberry’s developer surface includes documentation, getting-started guide, support, YouTube channel, and 5 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

8.9/100 minimal ▬ flat Agent 4/100 human only Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

0 APIs 1 MCP Servers

Company Robotics Artificial Intelligence Conversational AI Voice Hardware MCP Agents Y Combinator

Kin Score

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

Composite quality — 8.9/100 · minimal

Contract Quality 0.0 / 21

Developer Ergonomics 4.5 / 17

Access Clarity 0.0 / 17

Operational Transparency 0.0 / 11

Contract Governance 0.0 / 10

Discoverability 4.7 / 9

Regulatory Posture 0.8 / 15

Agent readiness — 4/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 12 / 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.

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
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
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 0.0 — up to +20.0 composite points
Developer Ergonomics scores 26.2 — up to +14.8 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/lightberry/apis.yml: open an issue on github.com/api-evangelist/lightberry 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 →

Standards implemented 1

Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.

Model Context Protocol (MCP)

measured — observed by APIs.io (documented)

MCP Servers 1

Model Context Protocol servers that expose these APIs to AI agents.

Lightberry MCP Server

MCP SERVER

Security Posture 1

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

Lightberry Domain Security

TLSv1.3

SECURITY

Resources

Get Started 1

Portal, sign-up, and the first successful call

GettingStarted

GettingStarted

Documentation 1

Reference material describing how the API behaves

Documentation

Documentation

Agent Surfaces 2

MCP servers, agent skills, and machine-readable catalogs

MCPServer

MCPServer

LLMsTxt

LLMsTxt

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Learn 1

Tutorials, courses, talks, and written guidance

Youtube

Youtube

Operate 1

Status, limits, changes, and where to get help

Support

Support

Company 2

The organization behind the API

Website

Website

Twitter

Twitter

Source (apis.yml)

aid: lightberry
name: Lightberry
description: Lightberry is a San Francisco robotics software company (Y Combinator Fall 2025) that builds conversational AI
  "personality" software for humanoid and quadruped robots. Its platform lets a robot listen, speak, see its surroundings,
  and act on natural-language instruction without the owner writing any code, emphasizing emotional intelligence, contextual
  awareness, and autonomy rather than a simple speech-to-text voice pipeline. Lightberry ships pre-installed on robots sold
  through its partnership with Unitree, and the company runs a manufacturer-compatibility program with robot makers including
  Fourier, Booster Robotics, and High Torque. Its published documentation today covers device setup (power-on, WiFi provisioning,
  starting a conversation) plus an MCP architecture note describing a Model Context Protocol server that routes tool calls
  to individual Lightberry devices, which each expose a REST endpoint. There is no public developer portal, API reference,
  SDK, or OpenAPI definition at this time.
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:
  - security
  generated: '2026-11-06'
  method: derived
image: https://lightberry.com/assets/images/share.jpg
url: https://raw.githubusercontent.com/api-evangelist/lightberry/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- y-combinator
x-yc-batch: Fall 2025
x-founded: '2025'
x-location: San Francisco, CA
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-19'
tags:
- Company
- Robotics
- Artificial Intelligence
- Conversational AI
- Voice
- Hardware
- MCP
- Agents
- Y Combinator
tags_raw:
- Company
- Robotics
- Artificial Intelligence
- Conversational AI
- Voice
- Hardware
- Model Context Protocol
- Agents
- Y Combinator
apis: []
common:
- type: MCPServer
  url: mcp/lightberry-mcp.yml
- url: https://lightberry.com
  type: Website
- url: https://docs.lightberry.com
  type: Documentation
- url: https://docs.lightberry.com
  type: GettingStarted
- url: mailto:hello@lightberry.com
  type: Support
- url: https://x.com/lightberry
  type: Twitter
- url: https://youtube.com/@lightberryinc
  type: Youtube
- url: llms/lightberry-llms.txt
  type: LLMsTxt
- url: security/lightberry-domain-security.yml
  type: DomainSecurity
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

Lightberry API — frequently asked questions

Does Lightberry have an MCP server?

Yes. APIs.io lists 1 MCP server for Lightberry.

Is the Lightberry API ready for AI agents?

Lightberry has a Kin Score of 8.9 out of 100 (minimal) and an Agent Readiness score of 3.5 (human-only), scored by API Evangelist on 2026-11-06.

Work with this as data

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MCP server

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https://apis.io/mcp

Tools for providers

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get_provider_operationsEvery operation across all of their OpenAPIs — one call instead of parsing every spec.
get_provider_toolsEvery MCP tool they ship, with the operation each wraps.
get_provider_evidenceHow each part of their score was established. Free — the basis for a claim should not sit behind it.
get_provider_ratingPRO — composite, band, trend and facet scores.
apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
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find_cohortsEvery scored population of providers in the catalog.

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