LAP

Lean API Platform

Agent-Native API specs. Verified, compressed, ready to install.

Website · Registry · Benchmarks · [Docs](docs/)

Request a Spec · Report a Bug · Request a Feature

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Without API documentation, LLM agents hallucinate endpoints, invent parameters, and guess auth flows -- scoring just 0.399 accuracy in blind tests.

LAP fixes this. One command gives your agent a verified, agent-native API spec -- jumping accuracy to 0.860. And because LAP specs are up to 10x smaller than raw OpenAPI, you also save 35% on cost and run 29% faster.

Not minification -- a purpose-built compiler with its own grammar.

Proven in 500 blind runs across 50 APIs

LAP Lean scored 0.851 (vs 0.825 raw) while using 35% less cost and 29% less time -- same accuracy, far fewer tokens.

> Full benchmark report (500 runs, 50 specs, 5 formats) · Benchmark methodology and data

Quick Start

# Set up LAP in your IDE
npx @lap-platform/lapsh init                    # Claude Code
npx @lap-platform/lapsh init --target cursor    # Cursor
npx @lap-platform/lapsh init --target codex     # Codex

# Search the registry for an API
npx @lap-platform/lapsh search payment

# Download a spec
npx @lap-platform/lapsh get stripe -o stripe.lap

# Install an API skill
npx @lap-platform/lapsh skill-install stripe

# Or compile your own spec
npx @lap-platform/lapsh compile api.yaml --lean

Use as an Agent Skill

Install the LAP skill so your agent can search, compile, and manage APIs automatically:

Claude Code:

npx @lap-platform/lapsh init

Cursor:

npx @lap-platform/lapsh init --target cursor

Codex (CLI & VS Code extension):

npx @lap-platform/lapsh init --target codex

Codex agents use curl for registry operations (search, get, check) instead of npx -- instant in the sandbox. Skills install to ~/.codex/skills/. Auto-update hooks are pre-configured for when Codex enables its hooks engine (codex_hooks feature flag is currently experimental).

OpenClaw: install from ClawHub or copy manually:

cp -r skills/lap ~/.openclaw/skills/lap

Once installed, agents auto-trigger the skill when working with APIs -- or invoke it directly with /lap. You can also install individual API skills for specific integrations:

npx @lap-platform/lapsh skill-install stripe
# Agent now knows the full Stripe API

> Want to get listed? Register as a verified publisher and share your specs and skills with the registry.

# Install globally (npm or pip)
npm install -g @lap-platform/lapsh
pip install lapsh

What You Get

• 📦 Registry — browse and install 1500+ pre-compiled specs at lap.sh
• 🗜️ 5.2× median compression on OpenAPI, up to 39.6× on large specs — 35% cheaper, 29% faster ([benchmarks](BENCHMARKS.md))
• 📐 Typed contracts — enum(a|b|c), str(uuid), int=10 prevent agent hallucination
• 🔌 6 input formats — OpenAPI, GraphQL, AsyncAPI, Protobuf, Postman, Smithy
• 🎯 Zero information loss — every endpoint, param, and type constraint preserved
• 🔁 Round-trip — convert back to OpenAPI with lapsh convert
• 🤖 Skill generation — lapsh skill creates agent-ready skills from any spec
• 🔗 Integrations — LangChain, Context Hub, Python/TypeScript SDKs

How It Works

Five compression stages, each targeting a different source of token waste:

| Stage | What it does | Savings | |-------|-------------|--------:| | Structural removal | Strip YAML scaffolding — paths:, requestBody:, schema: wrappers vanish | ~30% | | Directive grammar | @directives replace nested structures with flat, single-line declarations | ~25% | | Type compression | type: string, format: uuid → str(uuid) | ~10% | | Redundancy elimination | Shared fields extracted once via @common_fields and @type | ~20% | | Lean mode | Strip descriptions — LLMs infer meaning from well-named parameters | ~15% |

Benchmarks

1,500+ specs · 5,228 endpoints · 4.37M → 423K tokens

| Format | Specs | Median | Best | |--------|------:|-------:|-----:| | OpenAPI | 30 | 5.2× | 39.6× | | Postman | 36 | 4.1× | 24.9× | | Protobuf | 35 | 1.5× | 60.1× | | AsyncAPI | 31 | 1.4× | 39.1× | | GraphQL | 30 | 1.3× | 40.9× |

Verbose formats compress most — they carry the most structural overhead. Already-concise formats like GraphQL still benefit from type deduplication.

The Ecosystem

LAP is more than a compiler:

| Component | What | Command | |-----------|------|---------| | Init | Set up LAP in your IDE | lapsh init --target claude | | Search | Find APIs in the registry | lapsh search payment | | Get | Download a spec by name | lapsh get stripe | | Skill Install | Install an API skill | lapsh skill-install stripe --target claude | | Skill Uninstall | Remove an installed skill | lapsh skill-uninstall stripe | | Uninstall | Fully remove LAP from your IDE | lapsh uninstall --target claude | | Check | Check installed skills for updates | lapsh check [--target claude\|cursor\|codex] | | Diff | Compare installed skill vs registry | lapsh diff stripe | | Pin / Unpin | Skip or resume update checks | lapsh pin stripe | | Compiler | Any spec → .lap | lapsh compile api.yaml | | Skill Generator | Create agent-ready skills from any spec | lapsh skill api.yaml --install | | API Differ | Detect breaking API changes | lapsh diff old.lap new.lap | | Round-trip | Convert LAP back to OpenAPI | lapsh convert api.lap -f openapi | | Publish | Share specs to the registry | lapsh publish api.yaml --provider acme |

> Claude Code, Cursor & Codex: The lap skill is included -- run lapsh init and your agent can search, install, and manage API skills directly. Claude Code and Cursor support auto-update checks via SessionStart hooks. Codex hooks are pre-configured and will activate when the codex_hooks feature becomes stable.

Supported Formats

lapsh compile  api.yaml           # OpenAPI 3.x / Swagger
lapsh compile  schema.graphql     # GraphQL SDL
lapsh compile  events.yaml        # AsyncAPI
lapsh compile  service.proto      # Protobuf / gRPC
lapsh compile  collection.json    # Postman v2.1
lapsh compile  model.smithy       # AWS Smithy

Format is auto-detected. Override with -f openapi|graphql|asyncapi|protobuf|postman|smithy.

Top Compressions

Integrations

# LangChain
from lap.middleware import LAPDocLoader
docs = LAPDocLoader("stripe.lap").load()

LangChain, Context Hub, and Python/TypeScript SDKs. See [integration docs](docs/guide-integrate.md).

FAQ

Why do agents hallucinate API calls?

Because they have no way to find the spec, and even if they could, it's a million tokens of YAML written for humans. Agents without specs score 0.399 accuracy -- wrong 60% of the time. They hallucinate endpoint paths, send invalid types, and miss auth. Give them a LAP spec and accuracy jumps to 0.860. The spec doesn't make the agent smarter. It makes guessing unnecessary.

How is this different from OpenAPI?

LAP doesn't replace OpenAPI — it compiles FROM it. Like TypeScript → JavaScript: you keep your OpenAPI specs, your existing tooling, everything. LAP adds a compilation step for the LLM runtime.

How is this different from MCP?

MCP defines how agents discover and invoke tools (the plumbing). LAP compresses the documentation those tools expose (the payload). They're complementary — LAP can compress MCP tool schemas.

Why not just minify the JSON?

Minification removes whitespace — that's ~10% savings. LAP performs semantic compression: flattening nested structures, deduplicating schemas, compressing type declarations, and stripping structural overhead. That's 5-40× savings. Different class of tool.

What about prompt caching?

Use both. Compress with LAP first, then cache the compressed version. LAP reduces the first-call cost and frees context window space. Caching reduces repeated-call cost. They stack.

Will LLMs understand this format?

Yes. LAP uses conventions LLMs already know — @directive syntax, {name: type} notation, HTTP methods and paths. In blind tests, agents produce identical correct output from LAP and raw OpenAPI. The typed contracts actually reduce hallucination.

What if token costs keep dropping?

Cost is the least important argument. The core value is typed contracts: enum(succeeded|pending|failed) prevents hallucinated values regardless of token price. Plus: formal grammar (parseable by code, not just LLMs), schema diffing, and faster inference from fewer input tokens.

Contributing

See [CONTRIBUTING.md](CONTRIBUTING.md). CI runs on every push and PR -- Python 3.11/3.12 and Node 18/20.

Python (18 test files, 1,083 tests):

| Suite | What it covers | |-------|----------------| | Compilers | OpenAPI, GraphQL, AsyncAPI, Protobuf, Postman, Smithy | | Round-trip | Compile → parse → re-emit across 190+ specs | | Skill & Tool | Skill compiler, tool format, MCP manifest, skill updates | | Agent | Agent implementation verification (enum, nested, array handling) | | Differ | Breaking change detection, compatibility checking | | CLI | Auth, search, version, integration (subprocess) | | Quality | Regression tests for compiler bug fixes |

TypeScript SDK (14 test files -- full compiler parity):

| Suite | What it covers | |-------|----------------| | Compilers | OpenAPI, GraphQL, AsyncAPI, Protobuf, Postman, Smithy, AWS SDK | | Parser & Serializer | LAP text round-trip in TypeScript | | Skills | Skill compilation, LLM integration | | CLI & Auth | CLI commands, credential management | | Search | Registry search helpers |

git clone https://github.com/Lap-Platform/lap.git
cd lap

# Python tests
pip install -e ".[dev]"
pytest

# TypeScript SDK tests
cd sdks/typescript
npm ci && npm test

License

[Apache 2.0](LICENSE) — See [NOTICE](NOTICE) for attribution.

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lap.sh · Built by the LAP team

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Lap-Platform
• Source: Lap-Platform/LAP
• License: Apache-2.0
• Homepage: https://lap.sh/

Install and usage instructions live in the source repository linked above.