Comparison
athina-evals vs instruct-eval
Verdict
Pick athina-evals if athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks; pick instruct-eval if key facts about instruct-eval.
Markdown twin · athina-evals alternatives · instruct-eval alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | athina-evals | instruct-eval |
|---|---|---|
| Maintenance | Dormant (417d since push) As of 4w · github_public_v1 | Dormant (879d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 4w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings As of 1mo · 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
- athina-evals
- Python SDK for evaluating LLM generated responses
- instruct-eval
- Quantitative evaluation for instruction-tuned language models
Stars
- athina-evals
- 301
- instruct-eval
- 552
Forks
- athina-evals
- 22
- instruct-eval
- 45
Open issues
- athina-evals
- 3
- instruct-eval
- 24
Language
- athina-evals
- Python
- instruct-eval
- Python
Adopt for
- athina-evals
- athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks.
- instruct-eval
- Key facts about instruct-eval
Persona
- athina-evals
- -
- instruct-eval
- -
Runtime
- athina-evals
- -
- instruct-eval
- -
License
- athina-evals
- -
- instruct-eval
- The tool is distributed under Apache-2.0 license
Last pushed
- athina-evals
- Jun 6, 2025
- instruct-eval
- Mar 10, 2024
Categories
- athina-evals
- Evaluation & Observability
- instruct-eval
- Evaluation & Observability
Trust and health
Days since push
- athina-evals
- 417d
- instruct-eval
- 879d
Open issues (now)
- athina-evals
- 3
- instruct-eval
- 24
OSV dependency advisories
- athina-evals
- No lockfile (source not queried)
- instruct-eval
- Published findings
Full report
- athina-evals
- Trust report
- instruct-eval
- Trust report
Choose athina-evals if…
- Tags unique to athina-evals: evaluation-framework, evaluation-metrics, llm-eval, llm-evaluation.
- When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics
- More recently updated (last pushed Jun 6, 2025).
When NOT to use athina-evals
- If open-source alternatives with transparent customization options are preferred over athina-evals' approach
- In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments
Choose instruct-eval if…
- Requirements: Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation..
- Tags unique to instruct-eval: benchmarking, instruct-tuning, llm, safety.
- When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks.
When NOT to use instruct-eval
- When primarily interested in general model evaluation without a focus on instruction-tuned LMs.
- If your primary interest lies in qualitative assessment rather than quantitative metrics.
- If you need support for non-HuggingFace Transformer models, as instruct-eval mainly supports models from the HuggingFace ecosystem.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (athina-ai/athina-evals) · observed Jul 28, 2026
- GitHub forks (athina-ai/athina-evals) · observed Jul 28, 2026
- Last push (athina-ai/athina-evals) · observed Jun 6, 2025
- License file (unknown) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (declare-lab/instruct-eval) · observed Aug 7, 2026
- GitHub forks (declare-lab/instruct-eval) · observed Aug 7, 2026
- Last push (declare-lab/instruct-eval) · observed Mar 10, 2024
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: athina-evals 301 · instruct-eval 552 (synced Jul 28, 2026).
Common questions
- What is the difference between athina-evals and instruct-eval?
- athina-evals: Python SDK for evaluating LLM generated responses. instruct-eval: Quantitative evaluation for instruction-tuned language models. See the comparison table for live GitHub stats and shared categories.
- When should I choose athina-evals over instruct-eval?
- Choose athina-evals over instruct-eval when Tags unique to athina-evals: evaluation-framework, evaluation-metrics, llm-eval, llm-evaluation; When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics; More recently updated (last pushed Jun 6, 2025).
- When should I choose instruct-eval over athina-evals?
- Choose instruct-eval over athina-evals when Requirements: Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation.; Tags unique to instruct-eval: benchmarking, instruct-tuning, llm, safety; When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks.
- When should I avoid athina-evals?
- If open-source alternatives with transparent customization options are preferred over athina-evals' approach In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments
- When should I avoid instruct-eval?
- When primarily interested in general model evaluation without a focus on instruction-tuned LMs. If your primary interest lies in qualitative assessment rather than quantitative metrics. If you need support for non-HuggingFace Transformer models, as instruct-eval mainly supports models from the HuggingFace ecosystem.
- Is athina-evals or instruct-eval more popular on GitHub?
- instruct-eval has more GitHub stars (552 vs 301). Stars measure visibility, not whether either tool fits your constraints.
- Are athina-evals and instruct-eval open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to athina-evals or instruct-eval?
- GraphCanon lists graph-backed alternatives at athina-evals alternatives and instruct-eval alternatives (athina-evals markdown twin, instruct-eval 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, athina-evals or instruct-eval?
- athina-evals: Dormant. instruct-eval: Dormant. 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 athina-evals and instruct-eval?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: athina-evals trust report; instruct-eval trust report.