🐢 Open-Source Evaluation & Testing library for LLM Agents
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Updated
Nov 18, 2025 - Python
🐢 Open-Source Evaluation & Testing library for LLM Agents
The Python Risk Identification Tool for generative AI (PyRIT) is an open source framework built to empower security professionals and engineers to proactively identify risks in generative AI systems.
A Python package to assess and improve fairness of machine learning models.
moDel Agnostic Language for Exploration and eXplanation (JMLR 2018; JMLR 2021)
Deliver safe & effective language models
A toolkit that streamlines and automates the generation of model cards
FIBO is a SOTA, first open-source, JSON-native text-to-image model built for controllable, predictable, and legally safe image generation.
LangFair is a Python library for conducting use-case level LLM bias and fairness assessments
Open-source testing platform & SDK for LLM and agentic applications. Define what your app should and shouldn't do in plain language, and Rhesis generates hundreds of test scenarios, runs them, and shows you where it breaks before production. Built for cross-functional teams to collaborate.
[ICCV 2023 Oral, Best Paper Finalist] ITI-GEN: Inclusive Text-to-Image Generation
PyTorch package to train and audit ML models for Individual Fairness
Oracle Guardian AI Open Source Project is a library consisting of tools to assess fairness/bias and privacy of machine learning models and data sets.
AWS Certified AI Practitioner (AIF-C01) exam preparation
Credo AI Lens is a comprehensive assessment framework for AI systems. Lens standardizes model and data assessment, and acts as a central gateway to assessments created in the open source community.
Official code of "StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis" (CVPR 2022)
🤖🛡️🔍🔒🔑 Tiny package designed to support red teams and penetration testers in exploiting large language model AI solutions.
Official code of "Discover and Mitigate Unknown Biases with Debiasing Alternate Networks" (ECCV 2022)
Open-source toolkit to help companies implement responsible AI workflows.
Metadata encoding and extraction for AI-generated content
Package for evaluating the performance of methods which aim to increase fairness, accountability and/or transparency
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