Comparison
athina-evals vs continuous-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 continuous-eval if continuous-eval is a Python framework for evaluating large language models, with emphasis on evaluation metrics and information retrieval.
Markdown twin · athina-evals alternatives · continuous-eval alternatives
GraphCanon updated today
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Trust & integrity
| Signal | athina-evals | continuous-eval |
|---|---|---|
| Maintenance | Dormant (417d since push) As of 3w · github_public_v1 | Active (10d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) 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
- continuous-eval
- Data-Driven Evaluation for LLM-Powered Applications
Stars
- athina-evals
- 301
- continuous-eval
- 515
Forks
- athina-evals
- 22
- continuous-eval
- 38
Open issues
- athina-evals
- 3
- continuous-eval
- 14
Language
- athina-evals
- Python
- continuous-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.
- continuous-eval
- Continuous-eval is a Python framework for evaluating large language models, with emphasis on evaluation metrics and information retrieval.
Persona
- athina-evals
- -
- continuous-eval
- -
Runtime
- athina-evals
- -
- continuous-eval
- -
License
- athina-evals
- -
- continuous-eval
- Continuous-eval is available under the Apache-2.0 license, allowing free use with attribution and no warranty provided by the authors.
Last pushed
- athina-evals
- Jun 6, 2025
- continuous-eval
- Aug 10, 2026
Categories
- athina-evals
- Evaluation & Observability
- continuous-eval
- Data & Retrieval, Evaluation & Observability
Trust and health
Maintenance
- athina-evals
- Dormant (18%)
- continuous-eval
- Active (82%)
Days since push
- athina-evals
- 417d
- continuous-eval
- 10d
Open issues (now)
- athina-evals
- 3
- continuous-eval
- 14
Stars delta
- athina-evals
- Unknown
- continuous-eval
- -1 (30d)
Open issues delta
- athina-evals
- Unknown
- continuous-eval
- +2 (30d)
Full report
- athina-evals
- Trust report
- continuous-eval
- Trust report
Choose athina-evals if…
- Tags unique to athina-evals: evaluation, llm-eval, llm-evaluation-toolkit, llm-ops.
- When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics
- Leaner open-issue backlog (3).
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 continuous-eval if…
- Pricing: The framework itself is open source and free to use, but enhanced or enterprise features may require additional cost..
- Requirements: Min 4 GB RAM.
- Tags unique to continuous-eval: information-retrieval, llmops, rag, retrieval-augmented-generation.
- Also covers Data & Retrieval.
- When developing LLM-powered applications where a continuous evaluation of model performance over time is required.
When NOT to use continuous-eval
- If your project strictly focuses on small scale or simple applications that do not require robust evaluation metrics or information retrieval features.
- When working in environments where Python is not preferred, as continuous-eval is specifically built for Python applications.
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 (relari-ai/continuous-eval) · observed Aug 21, 2026
- GitHub forks (relari-ai/continuous-eval) · observed Aug 21, 2026
- Last push (relari-ai/continuous-eval) · observed Aug 10, 2026
- License file (Apache-2.0) · observed Aug 21, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: athina-evals 301 · continuous-eval 515 (synced Jul 28, 2026).
Common questions
- What is the difference between athina-evals and continuous-eval?
- athina-evals: Python SDK for evaluating LLM generated responses. continuous-eval: Data-Driven Evaluation for LLM-Powered Applications. See the comparison table for live GitHub stats and shared categories.
- When should I choose athina-evals over continuous-eval?
- Choose athina-evals over continuous-eval when Tags unique to athina-evals: evaluation, llm-eval, llm-evaluation-toolkit, llm-ops; When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics; Leaner open-issue backlog (3).
- When should I choose continuous-eval over athina-evals?
- Choose continuous-eval over athina-evals when Pricing: The framework itself is open source and free to use, but enhanced or enterprise features may require additional cost.; Requirements: Min 4 GB RAM; Tags unique to continuous-eval: information-retrieval, llmops, rag, retrieval-augmented-generation; Also covers Data & Retrieval; When developing LLM-powered applications where a continuous evaluation of model performance over time is required.
- 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 continuous-eval?
- If your project strictly focuses on small scale or simple applications that do not require robust evaluation metrics or information retrieval features. When working in environments where Python is not preferred, as continuous-eval is specifically built for Python applications.
- Is athina-evals or continuous-eval more popular on GitHub?
- continuous-eval has more GitHub stars (515 vs 301). Stars measure visibility, not whether either tool fits your constraints.
- Are athina-evals and continuous-eval open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to athina-evals or continuous-eval?
- GraphCanon lists graph-backed alternatives at athina-evals alternatives and continuous-eval alternatives (athina-evals markdown twin, continuous-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 continuous-eval?
- athina-evals: Dormant. continuous-eval: 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 athina-evals and continuous-eval?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: athina-evals trust report; continuous-eval trust report.