Kalpa Labs
Kalpa Labs is a San Francisco audio-research lab (Y Combinator Fall 2025) building generalist speech models that unify text-to-speech, multi-speaker conversation, voice cloning, and speech-in / speech-out reasoning behind one API — steerable with natural instructions and in-context learning the way a large language model is. Their Kalpa Speech API exposes stable public model ids over a clean REST interface: POST /v1/tts turns text into 24 kHz WAV audio, POST /v1/converse completes the open turn of a conversation (authored speech or contextual TTS), and a stateful WebSocket streams multi-speaker sessions. The developer surface ships a committed OpenAPI 3.1 contract, an AsyncAPI 3.0 WebSocket protocol, docs with a markdown twin per page, an llms.txt, and a browser Studio playground. Founded by Prashant Shishodia and Gautam Jha.
Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.
API Evangelist profiles Kalpa Labs the way a machine reads it — 7 machine-readable artifacts across 3 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Kalpa Labs scores 40.3/100 (thin), with a separate agent-readiness read of 58/100 (agent ready). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Kalpa Labs
Each block below is one kind of artifact we hold for Kalpa Labs. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 3
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Kalpa Labs Meta API
Health checks and capability discovery.
Kalpa Labs Speech API
Text-to-speech and conversational generation.
Kalpa Labs Usage API
Per-key usage and metering.
MCP Servers 1
Model Context Protocol servers expose these APIs directly to AI agents. We profile them because agent-native access is the fastest-growing way this provider's capabilities actually get used.
Model Context Protocol servers that expose these APIs to AI agents.
kalpa-labs-mcp.yml
MCP SERVERRate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Kalpa Labs Rate Limits
RATE LIMITSSecurity Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Resources
Every other property we hold for Kalpa Labs — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 3
Portal, sign-up, and the first successful call
Documentation 3
Reference material describing how the API behaves
Agent Surfaces 3
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 5
Pagination, idempotency, versioning, errors, and events
Build 3
SDKs, sample code, and the tooling you integrate with
Access & Security 2
Authentication, authorization, and security posture
Operate 2
Status, limits, changes, and where to get help
Company 1
The organization behind the API
Other 1
Properties that don't map to a standard resource type
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