Open the source and read safety notes before installing.
Citation facts
Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.
- Canonical URL
- https://heyclau.de/entry/tools/giskard
- Source URLs
- https://docs.giskard.ai, https://github.com/Giskard-AI/giskard-oss, https://www.giskard.ai
- Brand
- Giskard
- Brand domain
- giskard.ai
- Brand asset source
- brandfetch
- Author
- Giskard
- Claim status
- unclaimed
- Last verified
- 2026-04-27
Schema details
- Install type
- copy
- Troubleshooting
- No
- Scope
- Source repo
- Website
- https://www.giskard.ai
- Pricing
- freemium
- Disclosure
- editorial
- Application category
- SecurityApplication
- Operating system
- Web, Self-hosted
Full copyable content
## Editorial notes
Giskard fits teams that want testing and monitoring workflows for LLM and machine learning system quality.
## Disclosure
Editorial listing. No paid placement or affiliate link is used.About this resource
Editorial notes
Giskard fits teams that want testing and monitoring workflows for LLM and machine learning system quality.
Disclosure
Editorial listing. No paid placement or affiliate link is used.
Source citations
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[](https://heyclau.de/entry/tools/giskard)How it compares
Giskard side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.
| Field | AI testing platform for evaluating, scanning, and monitoring machine learning and LLM application quality. Open dossier | Open-source Python framework for unit-testing LLM applications, agents, RAG pipelines, metrics, regression suites, and traces. Open dossier | Open-source LLM vulnerability scanner for probing model behavior, prompt attack surfaces, and safety failures. Open dossier | Open-source framework from OpenAI for evaluating LLM and agent behavior with reusable eval definitions, grading logic, datasets, and regression workflows. Open dossier |
|---|---|---|---|---|
| Trust | ||||
| Install risk | Review first | Review first | Review first | Review first |
| Notes | Safety · Privacy · | Safety ✓ Privacy ✓ | Safety · Privacy · | Safety ✓ Privacy ✓ |
| Brand | — | — | ||
| Category | tools | tools | tools | tools |
| Source | source-backed | source-backed | source-backed | source-backed |
| Author | Giskard | Confident AI | NVIDIA | OpenAI |
| Added | 2026-04-27 | 2026-06-03 | 2026-04-27 | 2026-06-05 |
| Platforms | CLI | CLI | CLI | CLI |
| Source repo | — | — | — | — |
| Safety notes | — missing | ✓DeepEval metrics should be treated as regression and review signals, not proof that an LLM application is safe, correct, or production-ready. LLM-as-a-judge metrics can call configured model providers, consume quota, hit rate limits, and produce judge-model errors that need separate handling. Evaluation thresholds should be calibrated on real examples before they block deployments or trigger automated rollback, ranking, billing, or moderation decisions. Tracing instrumentation can wrap live application code, agents, retrievers, tools, and model calls; keep eval and production environments clearly separated. | — missing | ✓Eval scores are regression and quality signals, not proof that a model or agent is safe, fair, or production-ready. Run adversarial, prompt-injection, or tool-use evals against isolated environments and reviewed credentials. Large eval runs can issue many model calls; set budgets, rate limits, and stop conditions before running them. |
| Privacy notes | — missing | ✓Test cases, traces, spans, prompts, actual outputs, expected outputs, retrieval context, tool arguments, metadata, and evaluation results may contain sensitive user or business data. LLM-based metrics can send evaluation payloads to the configured model provider unless a reviewed local model path is used. DeepEval documentation says evaluations run locally by default, while Confident AI login and cloud reporting are optional paths for centralized results. The official data privacy docs say DeepEval collects basic PostHog telemetry by default, including event names, metric names, notebook usage, an anonymous UUID, and public IP, with `DEEPEVAL_TELEMETRY_OPT_OUT=1` available for opt-out. | — missing | ✓Prompts, model outputs, labels, traces, retrieved documents, and grader notes can contain user, customer, or proprietary data. Completion functions may send eval payloads to the configured model provider unless a reviewed local model path is used. Store eval datasets and results according to the same retention and redaction rules used for production AI data. |
| Prerequisites | — none listed |
| — none listed |
|
| Install | — | — | — | |
| Config | — | — | — | — |
| Citations | ||||
| Claim | Unclaimed | Unclaimed | Unclaimed | Unclaimed |
Related resources
DeepEval
Python LLM evaluation tests, metrics, regression checks, and tracing.
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