The testing
framework for AI models

Eliminate risks of biases, performance issues & security holes in AI models. In <10 lines of code.

From tabular models to LLMs
Listed by Gartner
AI Trust, Risk and Security
# Get started
pip install giskard -U
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Giskard - Open-source testing framework for LLMs & ML models | Product Hunt

Trusted by future-driven AI teams


AI pipelines are broken

AI/ML teams spend weeks manually creating test cases, writing reports, and enduring endless review meetings.
MLOps tools don’t cover the full range of AI risks: robustness, fairness, efficiency, security, etc.
AI Testing practices are siloed and inconsistent across projects & teams.
Non compliance to the EU AI regulation can cost up to 3% of your global revenue.

Enter Giskard: Fast AI Testing at scale

Stop wasting time on manual testing and writing evaluation reports.
Automatically detect errors, biases and security holes in your ML models.
Unify AI Testing practices: use standard methodologies for optimal model deployment.
Ensure compliance with AI regulations using our holistic AI Quality framework.
ML Testing library

Open-source & easy to integrate

In a few lines of code, identify vulnerabilities that may affect the performance, fairness & security of your model. 

Directly in your Python notebook or Integrated Development Environment (IDE).

import giskard
qa_chain = RetrievalQA.from_llm(...)
model = giskard.Model(
name="My QA bot",
description="An AI assistant that...",
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AI Quality Hub

Enable collaborative AI Quality Assurance

Entreprise-ready quality assurance platform to test, debug & explain your AI models collaboratively.
Try our latest beta release!

Monitor your LLM-based applications

Diagnose critical AI Safety risks in real-time, such as hallucinations, incorrect responses and toxicity in your LLM outputs. Works with any LLM API.
Try LLMon

Who is it for?

Data scientists
ML Engineers
AI Governance specialists
You want to work with the best Open-source tools
You work on business-critical AI applications
You spend a lot of time evaluating & testing models
You prioritize performance, security, safety & compliance in AI models
You care about Responsible AI principles: fairness, transparency, governance

“Giskard really speeds up input gatherings and collaboration between data scientists and business stakeholders!”

Head of Data
Emeric Trossat

"Giskard really speeds up input gatherings and collaboration between data scientists and business stakeholders!"

Head of Data
Emeric Trossat

"Giskard has become a strong partner in our purpose for ethical AI. It delivers the right tools for releasing fair and trustworthy models."

Head of Data Science
Arnault Gombert

"Giskard enables to integrate Altaroad business experts' knowledge into our ML models and test them."


"Giskard allows us to easily identify biases in our models and gives us actionable ways to deliver robust models to our customers."

Chief Science Officer
Maximilien Baudry

Join the community

Welcome to an inclusive community focused on AI Quality! Join us to share best practices, create new tests, and shape the future of AI safety standards together.


All those interested in AI Quality are welcome here!

All resources

Thought leadership articles about AI Quality: Performance, Robustness, Ethics, Risk & Governance

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Giskard's LLM Red Teaming

LLM Red Teaming: Detect safety & security breaches in your LLM apps

Introducing our LLM Red Teaming service, designed to enhance the safety and security of your LLM applications. Discover how our team of ML Researchers uses red teaming techniques to identify and address LLM vulnerabilities. Our new service focuses on mitigating risks like misinformation and data leaks by developing comprehensive threat models.

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Data Drift Monitoring with Giskard

Data Drift Monitoring with Giskard

Learn how to effectively monitor and manage data drift in machine learning models to maintain accuracy and reliability. This article provides a concise overview of the types of data drift, detection techniques, and strategies for maintaining model performance amidst changing data. It provides data scientists with practical insights into setting up, monitoring, and adjusting models to address data drift, emphasising the importance of ongoing model evaluation and adaptation.

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Classification of AI systems under the EU AI Act

EU AI ACT: 8 Takeaways from the Council's Final Approval

The Council of the EU has recently voted unanimously on the final version of the European AI Act. It’s a significant step forward in its efforts to legislate the first AI law in the world. The Act establishes a regulatory framework for the safe use and development of AI, categorizing AI systems according to their associated risk. In the coming months, the text will enter the last stage of the legislative process, where the European Parliament will have a final vote on the AI Act.

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