Deterministic Context Batching Cuts LLM Token Usage by 81.7% for OpenAPI Testing

A developer built an agentic API testing prototype that reads OpenAPI specifications, generates test plans, executes them via a headless Dart runner, and reasons about failures. The core challenge was that large OpenAPI specs, often containing hundreds of endpoints and nested schemas, were too token-heavy to send repeatedly to a language model. To solve this, the developer created a deterministic Dart algorithm called OpenAPI Context Batching, which splits endpoints by root domain and recursively resolves only the schema references relevant to a given request. This approach reduced approximate token usage from around 50,000 to 9,200 tokens across a five-interaction workflow, an 81.7% reduction. The project was originally developed as part of a GSoC 2026 proposal for foss42, and though the developer was not selected, the working prototype was completed independently.