--- title: "awesome-evals vs mteb" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/benchflow-ai-awesome-evals-vs-embeddings-benchmark-mteb" tools: ["benchflow-ai-awesome-evals", "embeddings-benchmark-mteb"] --- # awesome-evals vs mteb *GraphCanon updated Aug 22, 2026* ## 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. [awesome-evals](https://github.com/benchflow-ai/awesome-evals) reports 761 GitHub stars, 71 forks, and 21 open issues, last pushed Jul 1, 2026. [mteb](https://docs.mteb.org) has 3.4k stars, 670 forks, and 340 open issues, last pushed Aug 21, 2026. Figures are from public GitHub metadata via [awesome-evals's repository](https://github.com/benchflow-ai/awesome-evals) and [mteb's repository](https://github.com/embeddings-benchmark/mteb). | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [mteb](/tools/embeddings-benchmark-mteb.md) | | --- | --- | --- | | Tagline | A curated library of resources for building and evaluating AI agents | State-of-the-art evaluation of embeddings across languages and modalities | | Stars | 761 | 3,400 | | Forks | 71 | 670 | | Open issues | 21 | 340 | | Language | - | Python | | Adopt for | Curated resources for AI agent evaluation with BenchFlow backing its maintenance | MTEB is an evaluator for embedding models across languages and modalities under the Apache-2.0 license. | | Persona | - | - | | Runtime | - | - | | License | Other | Apache-2.0 | | Categories | AI Agents, Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [mteb](/tools/embeddings-benchmark-mteb.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 26d | 0d | | Open issues (now) | 21 | 340 | | Stars delta | Unknown | +36 (30d) | | Open issues delta | Unknown | +31 (30d) | | Full report | [trust report](/tools/benchflow-ai-awesome-evals/trust.md) | [trust report](/tools/embeddings-benchmark-mteb/trust.md) | ## Decision facts: awesome-evals - **Adopt for:** Curated resources for AI agent evaluation with BenchFlow backing its maintenance ## Decision facts: mteb - **Adopt for:** MTEB is an evaluator for embedding models across languages and modalities under the Apache-2.0 license. ## Choose when ### 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 ### 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 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 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. ## 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 (3,400 vs 761). 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](/tools/benchflow-ai-awesome-evals/alternatives) and [mteb alternatives](/tools/embeddings-benchmark-mteb/alternatives) ([awesome-evals markdown twin](/tools/benchflow-ai-awesome-evals/alternatives.md), [mteb markdown twin](/tools/embeddings-benchmark-mteb/alternatives.md)), 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](/compare/benchflow-ai-awesome-evals-vs-embeddings-benchmark-mteb.md) 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: 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](/tools/benchflow-ai-awesome-evals/trust); [mteb trust report](/tools/embeddings-benchmark-mteb/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=benchflow-ai-awesome-evals`](/api/graphcanon/graph?tool=benchflow-ai-awesome-evals) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_