# LAPIS

**Canonical:** https://apis.io/providers/lapis/  
**Website:** https://cr0hn.github.io/LAPIS/  
**APIs profiled:** 1

LAPIS (Lightweight API Specification for Intelligent Systems) is a compact, LLM-native API description format authored by Daniel Garcia (cr0hn). It is designed as the format you convert your OpenAPI specifications to when the consumer is a Large Language Model rather than a code generator or human reader. By replacing JSON/YAML structural overhead with a function-signature syntax, indentation-based sections, and centralized definitions for errors, webhooks, rate limits, and workflows, a typical LAPIS document carries the same semantic information as its OpenAPI source while consuming roughly 70-80 percent fewer tokens. LAPIS is not a runtime format and does not replace MCP, function calling, or OpenAPI itself - it is an intermediate representation optimized for AI agents that need to reason about an API inside a constrained context window.

## Kin Score — 30.7 / 100 (thin)

Scored 2026-08-21 under rubric 0.12.0. Trend: flat (+0.0 from 30.7).

| Facet | Score |
|---|---|
| Discoverability | 59.3 |
| Contract Quality | 16.9 |
| Governance | 25.0 |
| Contract Governance | 25.0 |
| Operational Transparency | 26.3 |
| Developer Ergonomics | 52.4 |
| Commercial Clarity | 18.4 |
| Access Clarity | 18.4 |

## Agent readiness — 1.5 (human-only)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| Reversibility Documented | no |
| MCP Server | no |
| Auth Clarity | no |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | documented |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |

## Access

Unknown — onboarding: unknown, pricing: unknown, trial: no (confidence: low).

## APIs (1)

- **LAPIS Specification** — The LAPIS specification defines a token-minimal, LLM-native format for describing HTTP APIs. A LAPIS document is organized into up to seven indentation-based sections - [meta], ...

## Use cases (8)

- **Reducing LLM Context Cost** — Engineering teams whose AI features pass an OpenAPI specification into prompts on every call convert the spec to LAPIS once and pass the smaller LAPIS document instead, reducing...
- **Powering AI Coding Assistants** — AI coding assistants that need to reason about a third-party API (generating client code, debugging a failing call, suggesting an endpoint) consume LAPIS as the API context laye...
- **Multi-Step API Agent Planning** — AI agents executing multi-step API workflows (create customer, create invoice, send invoice, await payment webhook) load a LAPIS document with a populated [flows] section so the...
- **Webhook-Aware Integrations** — Integration platforms that build webhook receivers use the [webhooks] section's trigger conditions (!) and headers (@header:X-Event-ID) to generate signature verification and ev...
- **Plan-Aware Rate Limit Enforcement** — Client SDKs and gateway integrations consume the [limits] section to configure backoff, request-throttling, and quota tracking per plan tier (free, pro, enterprise) and per scop...
- **Specification Linting and Validation** — Tooling vendors and platform teams enforce LAPIS-conformant documents by validating against the EBNF grammar in spec section 16, catching missing required sections, invalid type...
- **Cross-Provider API Comparison** — Because LAPIS strips presentation overhead and centralizes errors, limits, and flows, two LAPIS documents from different providers can be diffed and compared more directly than ...
- **Onboarding Documentation for Internal APIs** — Platform teams generate LAPIS from internal OpenAPI sources to provide on-call engineers and product stakeholders a quickly readable, function-signature view of the company's se...

## Tags

Specification, LLM, AI Agents, OpenAPI, Token Optimization, Standards

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/lapis/). Scores are computed from the provider's own public artifacts under a published rubric.