Giskard
Metrics as of , from the GitHub or GitLab API of each repository. Refreshed monthly.
What Giskard is
Giskard is a Python library that tests and evaluates agentic systems such as LLMs, black-box agents and multi-step pipelines. Version 3 is split into focused packages. The giskard-checks package defines scenarios built from interactions and checks. Its built-in evals include string matching, regex, semantic similarity and LLM-as-judge checks. Scenarios can cover multi-turn conversations and RAG groundedness. The giskard-scan package generates adversarial test suites from a plain-language description of an agent. Its probes cover prompt injection, jailbreaks, harmful content and misinformation. The legacy scan for tabular ML models remains available only in version 2. The library requires Python 3.12 or later.
Written from the project's README, read .
- Category
- AI and LLM evaluation
- License
- Apache-2.0
- Language
- Python
- Changelog
- Releases on GitHub
Repository metrics
Status
activeLast commit within 90 days of the fetch date.Computed from the last commit date and the archive flag on the fetch date. See the status rules.
Alternatives
Listed AI and LLM evaluation tools, same primary language first, then by GitHub stars. Each line gives one fact from the tool's documentation where it differs from Giskard's, with its source.
OpenAI Evals: Model providers: Models on the OpenAI API; completion functions in evals/registry/completion_fns or any CompletionFn implementation. source: Docs: How to run evals
DeepEval: Model providers: OpenAI, Azure OpenAI, Ollama, Anthropic, Gemini, LiteLLM; custom models through DeepEvalBaseLLM. source: Docs: Introduction to LLM Metrics
Ragas: Model providers: OpenAI, Anthropic, Google directly; Azure OpenAI, AWS Bedrock, Google Vertex AI and others through LiteLLM. source: Docs: Customise models
Language Model Evaluation Harness: Model providers: Hugging Face transformers, vLLM, SGLang, GGUF via llama.cpp, NeMo, Megatron-LM; OpenAI, Anthropic, LiteLLM, local API servers. source: README
garak: Model providers: Hugging Face, Replicate, OpenAI, AWS Bedrock, LiteLLM, Cohere, Groq, NIM, ggml/GGUF, REST endpoints. source: README
TruLens: Model providers: OpenAI, Azure OpenAI, LiteLLM, Google Gemini, AWS Bedrock, Snowflake Cortex, HuggingFace, LangChain models, OrcaRouter. source: README
HELM: Model providers: Models from various providers through one interface, such as OpenAI, Anthropic Claude, Google Gemini. source: README
Inspect: Model providers: OpenAI, Anthropic, Google, Grok, Mistral, DeepSeek; AWS Bedrock, Azure AI; Groq, Together AI; local models. source: Docs: Model Providers
How to install
pip install giskardQuestions
Is Giskard open source?
Yes. Giskard is released under Apache-2.0, an OSI-approved license, as reported by the GitHub API on 2026-09-22.
Is Giskard maintained?
On 2026-09-22, the last commit to the default branch was on 2026-09-21, so the listed status is active. Rule: Last commit within 90 days of the fetch date.
How many GitHub stars does Giskard have?
5,834 stars on 2026-09-22, from the GitHub API. The number is refreshed at each monthly update.
What language is Giskard written in?
The repository's primary language, as reported by the GitHub API, is Python.
How do I install Giskard?
The README gives this command: pip install giskard
Sources
- GitHub REST API: repository, read
- GitHub REST API: commits, read
- GitHub REST API: latest release, read
- GitHub REST API: contributors, read
- README, read