AI Testing Tools
AI testing is the practice of checking whether artificial intelligence systems behave reliably, safely, and as intended. Tests can measure accuracy and consistency, reveal bias or harmful outputs, probe how models respond to unusual inputs, and verify that an application handles errors and changing dependencies. For systems built with language models or machine learning, repeatable testing helps teams catch regressions and assess risks before deployment and as systems evolve.
Open source tools in this area include evaluation frameworks, robustness and safety checks, bias analysis, and mocks that simulate model APIs or other services. When choosing a tool, consider its maturity, license, maintenance activity, supported models and integrations, and the effort required to define meaningful tests. These tools are useful to developers, researchers, and teams responsible for building, deploying, or reviewing AI systems.
2 repositories · updated August 8, 2026
aimock: Comprehensive Mocking for AI Application Testing
aimock is a powerful tool designed for comprehensive testing of AI applications by mocking various AI APIs and services. It offers a unified solution to simulate interactions with LLM APIs, vector databases, and other AI infrastructure, ensuring deterministic and efficient testing workflows. With features like record and replay, chaos testing, and seamless framework integrations, aimock significantly simplifies the development and validation of robust AI systems.
LangTest: A Comprehensive Library for Safe & Effective Language Models
LangTest is an open-source Python library dedicated to ensuring the safety and effectiveness of language models. It offers a comprehensive framework for testing model quality, covering robustness, bias, fairness, and accuracy across various NLP tasks and LLM providers. With LangTest, developers can generate and execute over 60 distinct test types with just one line of code, promoting responsible AI development.