giskard-oss vs langtest
LLM and language model testing tools compared
giskard-oss and langtest are Python toolkits for evaluating AI systems with repeatable tests. giskard-oss focuses on multi-turn agents, RAG, and adversarial security testing, while langtest covers a wider range of NLP and LLM quality tests and can support training-data augmentation for select models.
giskard-oss: Test and Red-Team LLM Agents
Giskard is a Python toolkit for evaluating agent behavior and probing AI systems for vulnerabilities. It suits teams building LLM agents or RAG applications that need repeatable checks, safety testing, and adversarial evaluation.
langtest: Test Language Models for Safety and Quality
LangTest is a Python library for generating and running tests that assess language-model quality, including robustness, bias, fairness, and accuracy. It helps NLP and AI teams identify issues and, for select models, augment training data based on results.
| giskard-oss | langtest | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Stars | 5.9k | 561 |
| Forks | 547 | 54 |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- giskard-oss tests callable agents, multi-turn conversations, and RAG answers; langtest tests NLP tasks such as named entity recognition, translation, and text classification, as well as LLM tasks.
- giskard-oss includes generated adversarial tests for issues such as prompt injection and harmful content; langtest generates tests for robustness, bias, fairness, representation, and accuracy.
- giskard-oss offers checks including groundedness and LLM-as-judge assessments; langtest provides more than 60 test types and includes benchmark datasets.
- giskard-oss v3 requires Python 3.12 or later and focuses on agent evaluation and security; langtest's listed use cases span NLP and LLM model assessment.
- Both projects use Python and Apache-2.0 licensing; giskard-oss has 5.9k stars and 547 forks, while langtest has 561 stars and 54 forks.
Choose giskard-oss if you…
- need to evaluate multi-turn agent behavior, traces, or RAG answer grounding.
- want adversarial tests for prompt injection, harmful content, or misinformation.
- need scenario-based checks for agent regressions as prompts, models, or workflows change.
Choose langtest if you…
- need to test NLP tasks such as named entity recognition, translation, or text classification.
- want to assess robustness, bias, fairness, or representation across generated tests and benchmark datasets.
- plan to use test results to guide training-data augmentation for a supported model.
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This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.