Giskard
Eliminate risks of biases, performance issues & security holes in AI models. In <10 lines of code.
Giskard is an open-source AI quality management system that helps address challenges in AI testing, such as dealing with domain-specific edge cases and the need for extensive documentation. It offers tools for scanning, testing, debugging, and monitoring AI models, enhancing test coverage and automating test execution. Giskard includes a Python library, a Quality Assurance Hub, and an LLM Monitoring platform, aiding in efficient test-writing and quality reporting.
Resources
What is Giskard?
Giskard is an open-source Python library for testing, evaluating, and red-teaming AI systems. It automatically detects vulnerabilities like hallucinations, prompt injections, bias, and harmful content in LLM applications, RAG pipelines, and traditional ML models. It works as a black-box testing tool, only needing API-level access to the model under test.
Key Features
The LLM Scan automatically identifies vulnerabilities including hallucinations, prompt injection susceptibility, harmful content generation, and discriminatory outputs. RAGET (RAG Evaluation Toolkit) generates test datasets and evaluates individual RAG components: generators, retrievers, rewriters, and knowledge bases. A red teaming engine continuously generates attack scenarios as new threats emerge.
Built-in evaluation checks cover correctness, groundedness, conformity, string matching, and semantic similarity. Giskard uses LiteLLM under the hood, so it works with any LLM provider: OpenAI, Anthropic, Azure, Mistral, Ollama, and custom endpoints. Framework integrations include LangChain, MLflow, and Hugging Face.
How It Works
Install via pip (pip install "giskard[llm]" -U), wrap your model, and run scans or evaluations programmatically. Giskard integrates into CI/CD pipelines so scans run on every commit. The workflow is: wrap model, run scan, review results, fix issues.
Open Source vs Enterprise
The open-source library (Apache 2.0) is free for individual use with basic security and failure detection. Giskard Hub is the enterprise platform adding team collaboration, scheduled evaluation runs, CI/CD integration, historical tracking, role-based access, and compliance features (SOC 2, HIPAA, GDPR). Enterprise pricing is custom.
Who Should Use Giskard?
Giskard is useful for ML engineers building LLM applications who need systematic testing before deployment, AI teams in regulated industries (banking, insurance, healthcare) where compliance requires documented evaluation, and organizations deploying RAG applications who need to evaluate retrieval and generation quality separately.
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