Comparison
auto-evaluator vs continuous-eval
Verdict
Pick auto-evaluator when auto-evaluator is primarily TypeScript; continuous-eval is Python; pick continuous-eval when continuous-eval is primarily Python; auto-evaluator is TypeScript.
Markdown twin · auto-evaluator alternatives · continuous-eval alternatives
GraphCanon updated Sep 20, 2026
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Trust & integrity
| Signal | auto-evaluator | continuous-eval |
|---|---|---|
| Maintenance | Archived (438d since push) As of Sep 8, 2026 · github_public_v1 | Steady (40d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 8, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 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 Jul 11, 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
- auto-evaluator
- auto-evaluator
- continuous-eval
- Data-Driven Evaluation for LLM-Powered Applications
Stars
- auto-evaluator
- 783
- continuous-eval
- 517
Forks
- auto-evaluator
- 99
- continuous-eval
- 38
Open issues
- auto-evaluator
- 21
- continuous-eval
- 14
Language
- auto-evaluator
- TypeScript
- continuous-eval
- Python
Adopt for
- auto-evaluator
- -
- continuous-eval
- Continuous-eval is a Python framework for evaluating large language models, with emphasis on evaluation metrics and information retrieval.
Persona
- auto-evaluator
- -
- continuous-eval
- -
Runtime
- auto-evaluator
- -
- continuous-eval
- -
License
- auto-evaluator
- Other
- 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
- auto-evaluator
- Jun 26, 2025
- continuous-eval
- Aug 10, 2026
Categories
- auto-evaluator
- Evaluation & Observability
- continuous-eval
- Data & Retrieval, Evaluation & Observability
Trust and health
Maintenance
- auto-evaluator
- Archived (8%)
- continuous-eval
- Steady (60%)
Days since push
- auto-evaluator
- 438d
- continuous-eval
- 40d
Archived on GitHub
- auto-evaluator
- Yes
- continuous-eval
- No
Open issues (now)
- auto-evaluator
- 21
- continuous-eval
- 14
Stars delta
- auto-evaluator
- 0 (30d)
- continuous-eval
- +1 (30d)
Open issues delta
- auto-evaluator
- 0 (30d)
- continuous-eval
- +2 (30d)
Full report
- auto-evaluator
- Trust report
- continuous-eval
- Trust report
Choose auto-evaluator if…
- auto-evaluator is primarily TypeScript; continuous-eval is Python.
- License: auto-evaluator is Other, continuous-eval is Apache-2.0.
- Tags unique to auto-evaluator: auto-evaluation, railway, typescript, vercel.
- Use auto-evaluator when you are working with TypeScript and need an integrated solution for evaluating AI models
When NOT to use auto-evaluator
- Avoid using auto-evaluator if you require a multi-language support environment, as it focuses solely on TypeScript
- Do not use this tool if your project's hosting requirements do not align with using Vercel or Railway
Choose continuous-eval if…
- continuous-eval is primarily Python; auto-evaluator is TypeScript.
- License: continuous-eval is Apache-2.0, auto-evaluator is Other.
- 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: evaluation-framework, evaluation-metrics, information retrieval, llm-evaluation.
- 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 (langchain-ai/auto-evaluator) · observed Sep 20, 2026
- GitHub forks (langchain-ai/auto-evaluator) · observed Sep 20, 2026
- Last push (langchain-ai/auto-evaluator) · observed Jun 26, 2025
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (relari-ai/continuous-eval) · observed Sep 20, 2026
- GitHub forks (relari-ai/continuous-eval) · observed Sep 20, 2026
- Last push (relari-ai/continuous-eval) · observed Aug 10, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: auto-evaluator 783 · continuous-eval 517 (synced Sep 20, 2026).
Common questions
- What is the difference between auto-evaluator and continuous-eval?
- auto-evaluator: auto-evaluator. continuous-eval: Data-Driven Evaluation for LLM-Powered Applications. See the comparison table for live GitHub stats and shared categories.
- When should I choose auto-evaluator over continuous-eval?
- Choose auto-evaluator over continuous-eval when auto-evaluator is primarily TypeScript; continuous-eval is Python; License: auto-evaluator is Other, continuous-eval is Apache-2.0; Tags unique to auto-evaluator: auto-evaluation, railway, typescript, vercel; Use auto-evaluator when you are working with TypeScript and need an integrated solution for evaluating AI models.
- When should I choose continuous-eval over auto-evaluator?
- Choose continuous-eval over auto-evaluator when continuous-eval is primarily Python; auto-evaluator is TypeScript; License: continuous-eval is Apache-2.0, auto-evaluator is Other; 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: evaluation-framework, evaluation-metrics, information retrieval, llm-evaluation; Also covers Data & Retrieval; When developing LLM-powered applications where a continuous evaluation of model performance over time is required.
- When should I avoid auto-evaluator?
- Avoid using auto-evaluator if you require a multi-language support environment, as it focuses solely on TypeScript Do not use this tool if your project's hosting requirements do not align with using Vercel or Railway
- 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 auto-evaluator or continuous-eval more popular on GitHub?
- auto-evaluator has more GitHub stars (783 vs 517). Stars measure visibility, not whether either tool fits your constraints.
- Are auto-evaluator and continuous-eval open source?
- Yes - both are open-source projects on GitHub (auto-evaluator: Other, continuous-eval: Apache-2.0).
- Where can I find alternatives to auto-evaluator or continuous-eval?
- GraphCanon lists graph-backed alternatives at auto-evaluator alternatives and continuous-eval alternatives (auto-evaluator 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, auto-evaluator or continuous-eval?
- auto-evaluator: Archived. continuous-eval: Steady. 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 auto-evaluator and continuous-eval?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: auto-evaluator trust report; continuous-eval trust report.