Comparison
awesome-evals vs uptrain
Verdict
Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick uptrain if upTrain, an open-source platform, evaluates and enhances Generative AI applications with preconfigured checks, root cause analysis, and actionable insights.
Markdown twin · awesome-evals alternatives · uptrain alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | awesome-evals | uptrain |
|---|---|---|
| Maintenance | Active (26d since push) As of 3w · github_public_v1 | Dormant (731d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 1d · 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
- awesome-evals
- A curated library of resources for building and evaluating AI agents
- uptrain
- Unified platform for evaluating and improving Generative AI applications
Stars
- awesome-evals
- 761
- uptrain
- 2.4k
Forks
- awesome-evals
- 71
- uptrain
- 204
Open issues
- awesome-evals
- 21
- uptrain
- 58
Language
- awesome-evals
- -
- uptrain
- Python
Adopt for
- awesome-evals
- Curated resources for AI agent evaluation with BenchFlow backing its maintenance
- uptrain
- UpTrain, an open-source platform, evaluates and enhances Generative AI applications with preconfigured checks, root cause analysis, and actionable insights.
Persona
- awesome-evals
- -
- uptrain
- -
Runtime
- awesome-evals
- -
- uptrain
- -
License
- awesome-evals
- Other
- uptrain
- The tool is available under the Apache-2.0 license, suitable for both free and commercial use with appropriate attribution.
Last pushed
- awesome-evals
- Jul 1, 2026
- uptrain
- Aug 18, 2024
Categories
- awesome-evals
- AI Agents, Evaluation & Observability
- uptrain
- Evaluation & Observability
Trust and health
Maintenance
- awesome-evals
- Active (82%)
- uptrain
- Dormant (18%)
Days since push
- awesome-evals
- 26d
- uptrain
- 731d
Open issues (now)
- awesome-evals
- 21
- uptrain
- 58
Stars delta
- awesome-evals
- Unknown
- uptrain
- +4 (30d)
Open issues delta
- awesome-evals
- Unknown
- uptrain
- +3 (30d)
Full report
- awesome-evals
- Trust report
- uptrain
- Trust report
Choose awesome-evals if…
- License: awesome-evals is Other, uptrain is Apache-2.0.
- Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks.
- 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 uptrain if…
- License: uptrain is Apache-2.0, awesome-evals is Other.
- UpTrain can be installed on-premises using pip or accessed through a managed version.
- Tags unique to uptrain: autoevaluation, evaluation, experimentation, hallucination-detection.
- uptrain ships Docker support for self-hosted deployment.
- - When you need to evaluate Generative AI applications across various use-cases including language models, code generation, and embeddings.
When NOT to use uptrain
- - When your application does not require extensive monitoring or do not need insights into improving Generative AI performance through root cause analysis.
- - If you prioritize a highly hands-off user experience without the capability to customize evaluation checks, consider using UpTrain's managed version instead of self-managing it.
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 Jul 28, 2026
- GitHub forks (benchflow-ai/awesome-evals) · observed Jul 28, 2026
- Last push (benchflow-ai/awesome-evals) · observed Jul 1, 2026
- License file (Other) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (uptrain-ai/uptrain) · observed Aug 20, 2026
- GitHub forks (uptrain-ai/uptrain) · observed Aug 20, 2026
- Last push (uptrain-ai/uptrain) · observed Aug 18, 2024
- License file (Apache-2.0) · observed Aug 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-evals 761 · uptrain 2.4k (synced Jul 28, 2026).
Common questions
- What is the difference between awesome-evals and uptrain?
- awesome-evals: A curated library of resources for building and evaluating AI agents. uptrain: Unified platform for evaluating and improving Generative AI applications. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-evals over uptrain?
- Choose awesome-evals over uptrain when License: awesome-evals is Other, uptrain is Apache-2.0; Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks; Also covers AI Agents; Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation.
- When should I choose uptrain over awesome-evals?
- Choose uptrain over awesome-evals when License: uptrain is Apache-2.0, awesome-evals is Other; UpTrain can be installed on-premises using pip or accessed through a managed version; Tags unique to uptrain: autoevaluation, evaluation, experimentation, hallucination-detection; uptrain ships Docker support for self-hosted deployment; - When you need to evaluate Generative AI applications across various use-cases including language models, code generation, and embeddings.
- 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 uptrain?
- - When your application does not require extensive monitoring or do not need insights into improving Generative AI performance through root cause analysis. - If you prioritize a highly hands-off user experience without the capability to customize evaluation checks, consider using UpTrain's managed version instead of self-managing it.
- Is awesome-evals or uptrain more popular on GitHub?
- uptrain has more GitHub stars (2,359 vs 761). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-evals and uptrain open source?
- Yes - both are open-source projects on GitHub (awesome-evals: Other, uptrain: Apache-2.0).
- Where can I find alternatives to awesome-evals or uptrain?
- GraphCanon lists graph-backed alternatives at awesome-evals alternatives and uptrain alternatives (awesome-evals markdown twin, uptrain 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 uptrain?
- awesome-evals: Active. uptrain: 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 awesome-evals and uptrain?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-evals trust report; uptrain trust report.