Comparison
awesome-evals vs giskard-oss
Verdict
Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick giskard-oss if giskard-oss is an open-source Python library for testing and evaluating agentic systems, including LLMs, black-box agents, and multi-step pipelines. It offers modules for checks, scans, and provider-agnostic LLM routing.
Markdown twin · awesome-evals alternatives · giskard-oss alternatives
GraphCanon updated Sep 20, 2026
7views this month
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Trust & integrity
| Signal | awesome-evals | giskard-oss |
|---|---|---|
| Maintenance | Very active (4d since push) As of Sep 20, 2026 · github_public_v1 | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- awesome-evals
- A curated library of resources for building and evaluating AI agents
- giskard-oss
- Open-Source Evaluation & Testing library for LLM Agents
Stars
- awesome-evals
- 901
- giskard-oss
- 5.8k
Forks
- awesome-evals
- 104
- giskard-oss
- 537
Open issues
- awesome-evals
- 34
- giskard-oss
- 65
Language
- awesome-evals
- -
- giskard-oss
- Python
Adopt for
- awesome-evals
- Curated resources for AI agent evaluation with BenchFlow backing its maintenance
- giskard-oss
- Giskard-oss is an open-source Python library for testing and evaluating agentic systems, including LLMs, black-box agents, and multi-step pipelines. It offers modules for checks, scans, and provider-agnostic LLM routing.
Persona
- awesome-evals
- -
- giskard-oss
- -
Runtime
- awesome-evals
- -
- giskard-oss
- -
License
- awesome-evals
- Other
- giskard-oss
- Apache-2.0
Last pushed
- awesome-evals
- Sep 15, 2026
- giskard-oss
- Sep 18, 2026
Categories
- awesome-evals
- AI Agents, Evaluation & Observability
- giskard-oss
- Evaluation & Observability
Trust and health
Days since push
- awesome-evals
- 4d
- giskard-oss
- 0d
Open issues (now)
- awesome-evals
- 34
- giskard-oss
- 65
Stars delta
- awesome-evals
- +140 (30d)
- giskard-oss
- +97 (30d)
Open issues delta
- awesome-evals
- +13 (30d)
- giskard-oss
- -27 (30d)
Full report
- awesome-evals
- Trust report
- giskard-oss
- Trust report
Choose awesome-evals if…
- License: awesome-evals is Other, giskard-oss is Apache-2.0.
- Tags unique to awesome-evals: ai-agents, awesome-list, benchmarks, llm-evaluation.
- Also covers AI Agents.
- Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation
When NOT to use awesome-evals
- Require real-time interactive support or direct tool integrations not covered by a static resource list
- Seeking proprietary tools from specific vendors rather than open resources and community content
Choose giskard-oss if…
- License: giskard-oss is Apache-2.0, awesome-evals is Other.
- Tags unique to giskard-oss: ai-red-team, ai-security, ai-testing, fairness-ai.
- When you need a comprehensive testing and evaluation library for agentic systems, including LLMs, black-box agents, and multi-step pipelines.
When NOT to use giskard-oss
- If you are working with Python versions below 3.12, as Giskard-oss requires Python 3.12+.
- When you prefer not to use tools that collect telemetry data, even if it is aggregated and does not include prompts or outputs.
- If you are looking for a tool that does not offer a modular architecture, as Giskard-oss is built with a modular set of focused packages.
- When you need a tool that does not support open-source licensing under Apache-2.0.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (benchflow-ai/awesome-evals) · observed Sep 20, 2026
- GitHub forks (benchflow-ai/awesome-evals) · observed Sep 20, 2026
- Last push (benchflow-ai/awesome-evals) · observed Sep 15, 2026
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Giskard-AI/giskard-oss) · observed Sep 20, 2026
- GitHub forks (Giskard-AI/giskard-oss) · observed Sep 20, 2026
- Last push (Giskard-AI/giskard-oss) · observed Sep 18, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: awesome-evals 901 · giskard-oss 5.8k (synced Sep 20, 2026).
Common questions
- What is the difference between awesome-evals and giskard-oss?
- awesome-evals: A curated library of resources for building and evaluating AI agents. giskard-oss: Open-Source Evaluation & Testing library for LLM Agents. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-evals over giskard-oss?
- Choose awesome-evals over giskard-oss when License: awesome-evals is Other, giskard-oss is Apache-2.0; Tags unique to awesome-evals: ai-agents, awesome-list, benchmarks, llm-evaluation; Also covers AI Agents; Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation.
- When should I choose giskard-oss over awesome-evals?
- Choose giskard-oss over awesome-evals when License: giskard-oss is Apache-2.0, awesome-evals is Other; Tags unique to giskard-oss: ai-red-team, ai-security, ai-testing, fairness-ai; When you need a comprehensive testing and evaluation library for agentic systems, including LLMs, black-box agents, and multi-step pipelines.
- When should I avoid awesome-evals?
- Require real-time interactive support or direct tool integrations not covered by a static resource list Seeking proprietary tools from specific vendors rather than open resources and community content
- When should I avoid giskard-oss?
- If you are working with Python versions below 3.12, as Giskard-oss requires Python 3.12+. When you prefer not to use tools that collect telemetry data, even if it is aggregated and does not include prompts or outputs. If you are looking for a tool that does not offer a modular architecture, as Giskard-oss is built with a modular set of focused packages. When you need a tool that does not support open-source licensing under Apache-2.0.
- Is awesome-evals or giskard-oss more popular on GitHub?
- giskard-oss has more GitHub stars (5,824 vs 901). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-evals and giskard-oss open source?
- Yes - both are open-source projects on GitHub (awesome-evals: Other, giskard-oss: Apache-2.0).
- Where can I find alternatives to awesome-evals or giskard-oss?
- GraphCanon lists graph-backed alternatives at awesome-evals alternatives and giskard-oss alternatives (awesome-evals markdown twin, giskard-oss markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, awesome-evals or giskard-oss?
- awesome-evals: Very active. giskard-oss: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for awesome-evals and giskard-oss?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-evals trust report; giskard-oss trust report.