Alternatives to Fairlearn

Giskard is an open-source testing framework for evaluating AI model quality, detecting bias, data drift, and security vulnerabilities. It provides automated test generation for LLMs and tabular models, scanning for issues like hallucination, prompt injection susceptibility, stereotypical outputs, and data leakage. Integrates with CI/CD pipelines for continuous model validation before deployment.

PyRIT (Python Risk Identification Toolkit) is Microsoft's open-source framework for automated red teaming of generative AI systems. It enables security researchers to probe LLMs for jailbreaks, prompt injection, content safety bypasses, and harmful output generation using multi-turn attack strategies, scoring engines, and orchestrated adversarial workflows. Supports multiple target models and integrates with Azure AI services.

Guardrails AI is an open-source Python and JavaScript framework for validating and structuring LLM outputs using composable Guards built from a Hub of pre-built validators. It handles structured data extraction with Pydantic models, content safety checks including toxicity, PII detection, competitor mentions, and bias filtering, plus automatic re-prompting when validation fails. The Guardrails Hub offers dozens of validators from regex matching to hallucination detection via LLM judges.

NeMo Guardrails is NVIDIA's open-source toolkit for adding programmable safety rails to LLM applications. It supports five guardrail types — input, dialog, retrieval, execution, and output rails — covering content safety, jailbreak detection, topic control, PII masking, hallucination detection, and fact-checking. The toolkit uses Colang, a domain-specific language for defining conversational constraints, and integrates with OpenAI, Azure, Anthropic, HuggingFace, and LangChain/LangGraph.