LAP: 1,500+ API Specs Compiled for LLM Consumption to Reduce Claude Hallucinations

✍️ OpenClawRadar📅 Published: March 22, 2026🔗 Source
LAP: 1,500+ API Specs Compiled for LLM Consumption to Reduce Claude Hallucinations
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What LAP Does

LAP addresses the problem where AI coding agents like Claude hallucinate API endpoints when given vague instructions like "use the Stripe API to create a charge." Instead of guessing or relying on stale training data, LAP provides compiled API specifications specifically structured for LLM consumption.

The core issue is that standard OpenAPI specs are built for humans, not agents. For example, Stripe's OpenAPI spec contains 1.2 million tokens of what the source describes as "noise." LAP compiles these specs 10x smaller while maintaining verified endpoints, correct parameters, and actual authentication requirements.

Technical Implementation

LAP was built primarily with Claude's assistance:

  • Claude Code wrote approximately 99.9% of the Python compiler, the TypeScript port, and the benchmark harness
  • The registry pipeline (processing 1,500+ specs) was built iteratively with Claude handling parsing, validation, and edge case handling
  • The lean output format was co-designed with Claude, optimized for what actually helps agents make correct API calls
  • The compilation process is deterministic with no AI in the compilation loop
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Workflow Integration

LAP provides several commands for integration:

  • lap init sets up LAP skills and hooks into automatic update checking
  • lap check tells you when installed specs are outdated
  • lap diff shows exactly what changed in updated specs

In practice, you can tell Claude: "Integrate Discord into the project, use LAP to fetch the spec" → Claude will invoke the LAP skill, install the right API-skill, and start coding with verified endpoints instead of guessing.

Performance Benefits

According to the source, LAP delivers:

  • 35% cheaper runs
  • 29% faster responses
  • The primary benefit: agents stop making up endpoints

Getting Started

LAP is open source with PRs, features, and spec requests welcome:

  • Initialize with: npx @lap-platform/lapsh init
  • GitHub: https://github.com/Lap-Platform/LAP
  • Registry (1,500+ APIs): https://registry.lap.sh

📖 Read the full source: r/ClaudeAI

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👀 See Also

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