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Giskard

AI testing platform for evaluating, scanning, and monitoring machine learning and LLM application quality.

by Giskard·added 2026-04-27·
CLI
HarnessCLI
Review first — review before installing

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

Decision playbook

Review trust signals before you adopt

Signals are present but mixed. Use the checklist below to confirm the source and operational safety for your environment.

Required checks are still incomplete. Finish source and safety verification before adopting this resource.

Compare context
Selected

0

Current score

58

Baseline

—

Delta

No baseline selected

No major trust-signal divergence detected in the current selection.

Source and provenance checks

Complete

Confirm ownership and provenance before trusting install instructions.

  • Source link availableRequired

    Open the canonical repository and verify ownership.

    Done

  • Source provenance statusRequired

    Marked as source-backed.

    Done

  • Metadata reviewed

    Registry metadata indicates a reviewed listing.

    Done

Safety and privacy checks

Required checks missing

Validate risk disclosures before installation or API wiring.

  • Safety notes presentRequired

    No safety notes listed.

    Pending

  • Privacy notes presentRequired

    No privacy notes listed.

    Pending

  • Trust level risk gateRequired

    Trust level does not block evaluation.

    Done

Package and install checks

Needs review

Check package metadata and artifact integrity signals.

  • Install payload available

    Install or copy payload is available for review.

    Done

  • Package verification flag

    No package verification flag provided.

    Pending

  • Checksum metadata

    No checksum provided for downloaded artifact.

    Pending

Compare-driven decision checks

Needs review

Use compare context to validate trade-offs before adoption.

  • Compare tray has multiple entries

    Add at least one more entry to compare trust differences.

    Pending

  • Baseline comparison available

    No baseline peer selected yet.

    Pending

  • Diverging trust signals identified

    No major trust-signal divergence found.

    Pending

Setup at a glance

Copy & paste

Copy-ready — paste the snippet to get started.

Install command

Not provided

Config snippet

Not provided

Copy snippet

Provided

Prerequisites

None

Platforms

1 listed

Install type

Copy & paste

Adoption plan

Balanced adoption plan

Current risk score 44/100. Use staged verification before broader rollout.

Risk 44
Adoption blockers
  • Safety notes are missing.
  • Privacy notes are missing.

Pre-adoption checks

Validate source and review signals before any execution.

  • Confirm source provenanceRequired

    Source URL/provenance metadata is present.

    Done

  • Confirm metadata review state

    Listing has review metadata.

    Done

  • Verify install payload

    Install/config payload exists and can be inspected.

    Done

Security checks

Confirm safety, privacy, and package integrity signals.

  • Review safety notesRequired

    Safety notes missing; review source code paths before execution.

    Pending

  • Review privacy notesRequired

    Privacy notes missing; inspect network/data behavior manually.

    Pending

  • Verify package integrity metadata

    No package verification/checksum metadata.

    Pending

Rollout

Adopt in controlled steps based on the selected plan.

  • Run in isolated sandbox firstRequired

    Use a constrained sandbox and observe behavior across multiple tasks.

    Pending

  • Roll out graduallyRequired

    Roll out to a small cohort before wider usage.

    Pending

  • Set monitoring and fallback

    Define rollback path and monitor errors after adoption.

    Pending

Evidence readiness

Evidence readiness matrix · balanced

Missing required evidence: Safety notes. Risk score 36.

Risk 36

Source provenance

Present

Source repository/provenance is listed.

Required in this preset

Metadata review

Present

Review metadata is present.

Required in this preset

Safety notes

Missing

Safety notes are missing.

Required in this preset

Privacy notes

Missing

Privacy notes are missing.

Optional in this preset

Package integrity

Missing

Package integrity metadata is missing.

Optional in this preset

Install payload

Present

Install payload is available.

Required in this preset

Required gaps: Safety notes

Decision timeline

Decision timeline · balanced

Blocking gaps: Review safety notes. Risk 32.

Risk 32

triage

Confirm source provenanceRequired

Source/provenance metadata is available.

Done

triage

Check metadata review statusRequired

Review metadata is available.

Done

verify

Review safety notesRequired

Safety notes are missing.

Pending

verify

Review privacy notes

Privacy notes are missing.

Pending

verify

Validate package integrity metadata

Package integrity metadata is missing.

Pending

rollout

Verify install payload and commandsRequired

Install payload is available.

Done

Blockers: Review safety notes

Schema details

Install type
copy
Troubleshooting
No
Source repository stats
Scope
Source repo
Tool listing metadata
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.

#evaluation#security

Source citations

Add this badge to your README

Show that Giskard is listed on HeyClaude. Paste this Markdown into your README — it renders the badge and links back to this page.

Listed on HeyClaude
[![Listed on HeyClaude](https://heyclau.de/badge/tools/giskard.svg)](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.

1 trust signal differ across this comparison (Submitter).

Next steps differ across entries — use the actions in the table below to copy install commands and source links per resource.

Field
Giskard logoGiskard

AI testing platform for evaluating, scanning, and monitoring machine learning and LLM application quality.

Open dossier
DeepEval logoDeepEval

Open-source Python framework for unit-testing LLM applications, agents, RAG pipelines, metrics, regression suites, and traces.

Open dossier
Garak

Open-source LLM vulnerability scanner for probing model behavior, prompt attack surfaces, and safety failures.

Open dossier
OpenAI Evals

Open-source framework from OpenAI for evaluating LLM and agent behavior with reusable eval definitions, grading logic, datasets, and regression workflows.

Open dossier
Next stepsDiffers
Open dossierAPI JSONOpen LLMOpen sourceNewsletterClaim listing
Open dossierAPI JSONOpen LLMOpen sourceNewsletterClaim listing
Open dossierAPI JSONOpen LLMOpen sourceNewsletterClaim listing
Open dossierAPI JSONOpen LLMOpen sourceNewsletterClaim listing
Trust
Review statusReviewedMaintainer reviewedReviewedMaintainer reviewedReviewedMaintainer reviewedReviewedMaintainer reviewed
Package trustPackage not verifiedPackage not verifiedPackage not verifiedPackage not verified
Source provenanceSource-backedSource-backedSource-backedSource-backed
SubmitterDiffers—oktofeesh1—JSONbored
Install riskReview firstReview firstReview firstReview first
Notes Safety · Privacy · Safety ✓ Privacy ✓ Safety · Privacy · Safety ✓ Privacy ✓
BrandGiskard logoGiskardDeepEval logoDeepEval——
Categorytoolstoolstoolstools
Sourcesource-backedsource-backedsource-backedsource-backed
AuthorGiskardConfident AINVIDIAOpenAI
Added2026-04-272026-06-032026-04-272026-06-05
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
  • Python environment for installing and running the `deepeval` package in the project being tested.
  • Representative LLM test cases, expected outputs, retrieval context, traces, datasets, or golden examples for the behavior being evaluated.
  • Model provider credentials for LLM-as-a-judge metrics such as G-Eval, Answer Relevancy, or other configured metrics.
  • CI policy for which evaluation thresholds are advisory, which are blocking, and who reviews failures before release decisions.
— none listed
  • Python environment suitable for installing and running eval tooling.
  • Representative prompts, expected outputs, graders, and datasets for the behavior being tested.
  • Model-provider credentials only when the selected completion function requires them.
Install———
pip install evals
Config————
Citations
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