Comparison
awesome-evals vs deepeval
Verdict
Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick deepeval if deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies.
Markdown twin · awesome-evals alternatives · deepeval alternatives
GraphCanon updated Sep 20, 2026
10views this month
vs
Trust & integrity
| Signal | awesome-evals | deepeval |
|---|---|---|
| Maintenance | Very active (4d since push) As of Sep 20, 2026 · github_public_v1 | Very active (1d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 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
- awesome-evals
- A curated library of resources for building and evaluating AI agents
- deepeval
- LLM Evaluation Framework.
Stars
- awesome-evals
- 901
- deepeval
- 18k
Forks
- awesome-evals
- 104
- deepeval
- 2.0k
Open issues
- awesome-evals
- 34
- deepeval
- 624
Language
- awesome-evals
- -
- deepeval
- Python
Adopt for
- awesome-evals
- Curated resources for AI agent evaluation with BenchFlow backing its maintenance
- deepeval
- Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies.
Persona
- awesome-evals
- -
- deepeval
- -
Runtime
- awesome-evals
- -
- deepeval
- -
License
- awesome-evals
- Other
- deepeval
- Apache-2.0 License
Last pushed
- awesome-evals
- Sep 15, 2026
- deepeval
- Sep 18, 2026
Categories
- awesome-evals
- AI Agents, Evaluation & Observability
- deepeval
- Evaluation & Observability
Trust and health
Days since push
- awesome-evals
- 4d
- deepeval
- 1d
Open issues (now)
- awesome-evals
- 34
- deepeval
- 624
Stars delta
- awesome-evals
- +140 (30d)
- deepeval
- +1.1k (30d)
Open issues delta
- awesome-evals
- +13 (30d)
- deepeval
- +220 (30d)
Full report
- awesome-evals
- Trust report
- deepeval
- Trust report
Choose awesome-evals if…
- License: awesome-evals is Other, deepeval 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 deepeval if…
- License: deepeval is Apache-2.0, awesome-evals is Other.
- Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities..
- Tags unique to deepeval: evaluation, metrics.
- When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics.
When NOT to use deepeval
- For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill.
- In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis.
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 Sep 20, 2026
- GitHub forks (benchflow-ai/awesome-evals) · observed Sep 20, 2026
- Last push (benchflow-ai/awesome-evals) · observed Sep 15, 2026
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (confident-ai/deepeval) · observed Sep 20, 2026
- GitHub forks (confident-ai/deepeval) · observed Sep 20, 2026
- Last push (confident-ai/deepeval) · observed Sep 18, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-evals 901 · deepeval 18k (synced Sep 20, 2026).
Common questions
- What is the difference between awesome-evals and deepeval?
- awesome-evals: A curated library of resources for building and evaluating AI agents. deepeval: LLM Evaluation Framework.. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-evals over deepeval?
- Choose awesome-evals over deepeval when License: awesome-evals is Other, deepeval 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 deepeval over awesome-evals?
- Choose deepeval over awesome-evals when License: deepeval is Apache-2.0, awesome-evals is Other; Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.; Tags unique to deepeval: evaluation, metrics; When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics.
- 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 deepeval?
- For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis.
- Is awesome-evals or deepeval more popular on GitHub?
- deepeval has more GitHub stars (18,342 vs 901). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-evals and deepeval open source?
- Yes - both are open-source projects on GitHub (awesome-evals: Other, deepeval: Apache-2.0).
- Where can I find alternatives to awesome-evals or deepeval?
- GraphCanon lists graph-backed alternatives at awesome-evals alternatives and deepeval alternatives (awesome-evals markdown twin, deepeval 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 deepeval?
- awesome-evals: Very active. deepeval: Very 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 awesome-evals and deepeval?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-evals trust report; deepeval trust report.