Comparison
continuous-eval vs auto-evaluator
Verdict
Pick continuous-eval if continuous-eval is a Python framework for evaluating large language models, with emphasis on evaluation metrics and information retrieval; pick auto-evaluator if auto-evaluator is a Python-based tool designed for evaluating LLM QA chains with the capability to auto-generate question-answer pairs from user-provided documents and evaluate answers using configurations chosen via UI.
Markdown twin · continuous-eval alternatives · auto-evaluator alternatives
GraphCanon updated Sep 20, 2026
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Trust & integrity
| Signal | continuous-eval | auto-evaluator |
|---|---|---|
| Maintenance | Steady (40d since push) As of Sep 20, 2026 · github_public_v1 | Dormant (1216d since push) As of Sep 8, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 8, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings 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
- continuous-eval
- Data-Driven Evaluation for LLM-Powered Applications
- auto-evaluator
- A lightweight evaluation tool for question-answering using Langchain
Stars
- continuous-eval
- 517
- auto-evaluator
- 1.1k
Forks
- continuous-eval
- 38
- auto-evaluator
- 92
Open issues
- continuous-eval
- 14
- auto-evaluator
- 3
Language
- continuous-eval
- Python
- auto-evaluator
- Python
Adopt for
- continuous-eval
- Continuous-eval is a Python framework for evaluating large language models, with emphasis on evaluation metrics and information retrieval.
- auto-evaluator
- Auto-evaluator is a Python-based tool designed for evaluating LLM QA chains with the capability to auto-generate question-answer pairs from user-provided documents and evaluate answers using configurations chosen via UI.
Persona
- continuous-eval
- -
- auto-evaluator
- -
Runtime
- continuous-eval
- -
- auto-evaluator
- -
License
- continuous-eval
- Continuous-eval is available under the Apache-2.0 license, allowing free use with attribution and no warranty provided by the authors.
- auto-evaluator
- -
Last pushed
- continuous-eval
- Aug 10, 2026
- auto-evaluator
- May 10, 2023
Categories
- continuous-eval
- Data & Retrieval, Evaluation & Observability
- auto-evaluator
- Evaluation & Observability
Trust and health
Maintenance
- continuous-eval
- Steady (60%)
- auto-evaluator
- Dormant (18%)
Days since push
- continuous-eval
- 40d
- auto-evaluator
- 1216d
Open issues (now)
- continuous-eval
- 14
- auto-evaluator
- 3
Stars delta
- continuous-eval
- +1 (30d)
- auto-evaluator
- -3 (30d)
Open issues delta
- continuous-eval
- +2 (30d)
- auto-evaluator
- 0 (30d)
Owner type
- continuous-eval
- Organization
- auto-evaluator
- User
OSV dependency advisories
- continuous-eval
- No lockfile (source not queried)
- auto-evaluator
- Published findings
Full report
- continuous-eval
- Trust report
- auto-evaluator
- Trust report
Shared compatibility
- Python · continuous-eval: Python runtime · auto-evaluator: Python runtime
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: 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.
Choose auto-evaluator if…
- Tags unique to auto-evaluator: evaluation, gpt-3.5-turbo, langchain, llm.
- Use when you need a lightweight solution for testing question-answering capabilities of Langchain models.
- More GitHub stars (1.1k vs 517) - visibility, not fit.
When NOT to use auto-evaluator
- Avoid using this tool when you do not have access to an OpenAI API key providing access to GPT-4, as it uses that by default for optimal settings.
- If you are looking for a tool that does not require you to input documents for question generation and prefer a more customized prompt approach rather than the auto-generation feature.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (rlancemartin/auto-evaluator) · observed Sep 20, 2026
- GitHub forks (rlancemartin/auto-evaluator) · observed Sep 20, 2026
- Last push (rlancemartin/auto-evaluator) · observed May 10, 2023
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: continuous-eval 517 · auto-evaluator 1.1k (synced Sep 20, 2026).
Common questions
- What is the difference between continuous-eval and auto-evaluator?
- continuous-eval: Data-Driven Evaluation for LLM-Powered Applications. auto-evaluator: A lightweight evaluation tool for question-answering using Langchain. See the comparison table for live GitHub stats and shared categories.
- When should I choose continuous-eval over auto-evaluator?
- Choose continuous-eval over auto-evaluator 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: 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 choose auto-evaluator over continuous-eval?
- Choose auto-evaluator over continuous-eval when Tags unique to auto-evaluator: evaluation, gpt-3.5-turbo, langchain, llm; Use when you need a lightweight solution for testing question-answering capabilities of Langchain models; More GitHub stars (1.1k vs 517) - visibility, not fit.
- 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.
- When should I avoid auto-evaluator?
- Avoid using this tool when you do not have access to an OpenAI API key providing access to GPT-4, as it uses that by default for optimal settings. If you are looking for a tool that does not require you to input documents for question generation and prefer a more customized prompt approach rather than the auto-generation feature.
- Is continuous-eval or auto-evaluator more popular on GitHub?
- auto-evaluator has more GitHub stars (1,102 vs 517). Stars measure visibility, not whether either tool fits your constraints.
- Are continuous-eval and auto-evaluator open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to continuous-eval or auto-evaluator?
- GraphCanon lists graph-backed alternatives at continuous-eval alternatives and auto-evaluator alternatives (continuous-eval markdown twin, auto-evaluator 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, continuous-eval or auto-evaluator?
- continuous-eval: Steady. auto-evaluator: 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 continuous-eval and auto-evaluator?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: continuous-eval trust report; auto-evaluator trust report.