APIEval-20

What it does

APIEval-20 is a black-box benchmark for API testing agents. Each agent gets only a JSON schema and one sample payload, then generates a test suite. We run those tests against live reference APIs with planted bugs and score bug detection, API coverage, and efficiency. Unlike LLM-as-judge evals, scoring is fully objective: a bug is either caught or it isn’t. Tasks span auth, errors, pagination, schemas, and multi-step flows. Open on Hugging Face.

A task benchmark for evaluating AI agents on real-world API testing — 20 scenarios across 7 domains, measuring bug-finding capability from schema and payload alone.

Testing APIs thoroughly is one of the most critical, yet consistently underserved, activities in software engineering. Despite a rich ecosystem of API testing tools — Postman, RestAssured, Schemathesis, Dredd, and others — we found ourselves asking a deceptively simple question: Given only the schema and an example payload of an API request — no source code, no documentation, no prior knowledge — how well can an AI agent generate a test suite that actually finds bugs? We searched for an existing benchmark that captured this black-box scenario and came up empty. Every evaluation we found either required access to the implementation, relied on rich API documentation, or measured properties…from resources.kusho.ai

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