Comparison
awesome-evals vs mteb
Verdict
Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick mteb if mTEB is an evaluator for embedding models across languages and modalities under the Apache-2.0 license.
Markdown twin · awesome-evals alternatives · mteb alternatives
GraphCanon updated Sep 20, 2026
11views this month
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Trust & integrity
| Signal | awesome-evals | mteb |
|---|---|---|
| Maintenance | Very active (4d since push) As of Sep 20, 2026 · github_public_v1 | Very active (0d 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 | Published findings As of Sep 27, 2026 · openssf-scorecard@v1 |
Tagline
- awesome-evals
- A curated library of resources for building and evaluating AI agents
- mteb
- State-of-the-art evaluation of embeddings across languages and modalities
Stars
- awesome-evals
- 901
- mteb
- 3.4k
Forks
- awesome-evals
- 104
- mteb
- 704
Open issues
- awesome-evals
- 34
- mteb
- 324
Language
- awesome-evals
- -
- mteb
- Python
Adopt for
- awesome-evals
- Curated resources for AI agent evaluation with BenchFlow backing its maintenance
- mteb
- MTEB is an evaluator for embedding models across languages and modalities under the Apache-2.0 license.
Persona
- awesome-evals
- -
- mteb
- -
Runtime
- awesome-evals
- -
- mteb
- -
License
- awesome-evals
- Other
- mteb
- Apache-2.0
Last pushed
- awesome-evals
- Sep 15, 2026
- mteb
- Sep 19, 2026
Categories
- awesome-evals
- AI Agents, Evaluation & Observability
- mteb
- Evaluation & Observability
Trust and health
Days since push
- awesome-evals
- 4d
- mteb
- 0d
Open issues (now)
- awesome-evals
- 34
- mteb
- 324
Stars delta
- awesome-evals
- +140 (30d)
- mteb
- +63 (30d)
Open issues delta
- awesome-evals
- +13 (30d)
- mteb
- +15 (30d)
OpenSSF Scorecard
- awesome-evals
- Not queried
- mteb
- Published findings
Full report
- awesome-evals
- Trust report
- mteb
- Trust report
Choose awesome-evals if…
- License: awesome-evals is Other, mteb 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 mteb if…
- License: mteb is Apache-2.0, awesome-evals is Other.
- Tags unique to mteb: benchmark, bitext-mining, clustering, embeddings.
- mteb ships Docker support for self-hosted deployment.
- You require benchmarking tools specifically designed for state-of-the-art embedding evaluations in low-resource NLP contexts.
When NOT to use mteb
- Your project exclusively focuses on a single language or modality not covered by MTEB’s broad scope.
- You need a tool that supports operations beyond evaluation, such as model training or fine-tuning directly within the same system.
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 (embeddings-benchmark/mteb) · observed Sep 20, 2026
- GitHub forks (embeddings-benchmark/mteb) · observed Sep 20, 2026
- Last push (embeddings-benchmark/mteb) · observed Sep 19, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-evals 901 · mteb 3.4k (synced Sep 20, 2026).
Common questions
- What is the difference between awesome-evals and mteb?
- awesome-evals: A curated library of resources for building and evaluating AI agents. mteb: State-of-the-art evaluation of embeddings across languages and modalities. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-evals over mteb?
- Choose awesome-evals over mteb when License: awesome-evals is Other, mteb 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 mteb over awesome-evals?
- Choose mteb over awesome-evals when License: mteb is Apache-2.0, awesome-evals is Other; Tags unique to mteb: benchmark, bitext-mining, clustering, embeddings; mteb ships Docker support for self-hosted deployment; You require benchmarking tools specifically designed for state-of-the-art embedding evaluations in low-resource NLP contexts.
- 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 mteb?
- Your project exclusively focuses on a single language or modality not covered by MTEB’s broad scope. You need a tool that supports operations beyond evaluation, such as model training or fine-tuning directly within the same system.
- Is awesome-evals or mteb more popular on GitHub?
- mteb has more GitHub stars (3,427 vs 901). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-evals and mteb open source?
- Yes - both are open-source projects on GitHub (awesome-evals: Other, mteb: Apache-2.0).
- Where can I find alternatives to awesome-evals or mteb?
- GraphCanon lists graph-backed alternatives at awesome-evals alternatives and mteb alternatives (awesome-evals markdown twin, mteb 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 mteb?
- awesome-evals: Very active. mteb: 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 mteb?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-evals trust report; mteb trust report.