Giskard

Giskard is an open-source testing and evaluation framework for AI models and LLM applications, it provides automated vulnerability scanning, bias detection, and quality assessment for machine learning systems before deployment and throughout the production lifecycle. The LLM testing module generates adversarial test cases automatically based on your model's specific use case: prompt injections, hallucination-inducing inputs, off-topic responses, harmful content generation attempts, and performance on edge cases that standard test sets don't cover. This automated red-teaming approach surfaces failure modes without requiring manual test case creation for every possible input type. The ML model testing capabilities cover traditional machine learning systems as well: performance regression detection, data drift monitoring, and fairness evaluation across demographic subgroups.

Giskard integrates with the major ML frameworks (scikit-learn, PyTorch, Hugging Face) and LLM platforms (LangChain, LlamaIndex, OpenAI). The scan results produce a structured report with severity rankings for identified issues and suggested remediation approaches. The open-source library is free; the Giskard Hub provides a collaborative web interface for managing evaluations across teams and models. Giskard is commonly used by AI engineering teams with production ML systems and LLM applications who need systematic quality assurance workflows rather than ad hoc testing, particularly in regulated industries where AI output quality has compliance implications.