#56 · Primary category: MLOps & Evaluation

giskard-oss

agent-evaluation ai-red-team ai-security ai-testing fairness-ai llm llm-eval llm-evaluation llm-security llmops ml-testing ml-validation mlops rag-evaluation red-team-tools responsible-ai trustworthy-ai

🐢 Open-Source Evaluation & Testing library for LLM Agents

Project last updated:08/28/26

GitHub Stars

5.8K

Forks

527

Contributors

88

License

Apache-2.0

Why we included this project

For teams shipping LLM agents, RAG pipelines, or a single model endpoint, this is a practical way to check behavior before it reaches users. It pairs a checks package for writing evals with a scan layer that auto-generates adversarial test suites, covering prompt injection, jailbreaks, harmful content, and other OWASP LLM Top-10 threats. The eval side ranges from simple assertions up to LLM-as-judge groundedness and conformity checks that cope with non-deterministic output, while the scan helps you red-team known failure modes without hand-writing every attack. The v3 rewrite is modular and async-first, so you pull in only the pieces you need and can wrap any callable as the target under test. If you want evaluation and security testing in one pip-installable package instead of juggling separate tools, this is a solid, actively maintained place to start.

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