--- title: "EnterpriseRAG-Bench vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/onyx-dot-app-enterpriserag-bench-vs-sindresorhus-awesome" tools: ["onyx-dot-app-enterpriserag-bench", "sindresorhus-awesome"] --- # EnterpriseRAG-Bench vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick EnterpriseRAG-Bench when license: EnterpriseRAG-Bench is MIT, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, EnterpriseRAG-Bench is MIT. [EnterpriseRAG-Bench](https://www.onyx.app/) reports 454 GitHub stars, 46 forks, and 9 open issues, last pushed May 8, 2026. [awesome](https://github.com/sindresorhus/awesome) has 484k stars, 36k forks, and 92 open issues, last pushed Jun 30, 2026. Figures are from public GitHub metadata via [EnterpriseRAG-Bench's repository](https://github.com/onyx-dot-app/EnterpriseRAG-Bench) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | Dataset and benchmark for RAG on company internal documents. | 😎 Curated list of awesome topics including hardware resources | | Stars | 454 | 484,026 | | Forks | 46 | 35,799 | | Open issues | 9 | 92 | | Language | - | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | CC0-1.0 | | Categories | LLM Frameworks, Data & Retrieval, Evaluation & Observability | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Active (82%) | | Days since push | 64d | 11d | | Open issues (now) | 9 | 92 | | Owner type | Organization | User | | Full report | [trust report](/tools/onyx-dot-app-enterpriserag-bench/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose EnterpriseRAG-Bench if… - License: EnterpriseRAG-Bench is MIT, awesome is CC0-1.0. - Tags unique to EnterpriseRAG-Bench: evaluation, dataset, benchmark, enterprise-search. - Also covers Data & Retrieval, Evaluation & Observability. ### Choose awesome if… - License: awesome is CC0-1.0, EnterpriseRAG-Bench is MIT. - Tags unique to awesome: resources, awesome-list. - More GitHub stars (484k vs 454) - visibility, not fit. ## When NOT to use EnterpriseRAG-Bench - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## When NOT to use awesome - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between EnterpriseRAG-Bench and awesome? EnterpriseRAG-Bench: Dataset and benchmark for RAG on company internal documents.. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose EnterpriseRAG-Bench over awesome? Choose EnterpriseRAG-Bench over awesome when License: EnterpriseRAG-Bench is MIT, awesome is CC0-1.0; Tags unique to EnterpriseRAG-Bench: evaluation, dataset, benchmark, enterprise-search; Also covers Data & Retrieval, Evaluation & Observability. ### When should I choose awesome over EnterpriseRAG-Bench? Choose awesome over EnterpriseRAG-Bench when License: awesome is CC0-1.0, EnterpriseRAG-Bench is MIT; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 454) - visibility, not fit. ### When should I avoid EnterpriseRAG-Bench? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### When should I avoid awesome? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is EnterpriseRAG-Bench or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 454). Stars measure visibility, not whether either tool fits your constraints. ### Are EnterpriseRAG-Bench and awesome open source? Yes - both are open-source projects on GitHub (EnterpriseRAG-Bench: MIT, awesome: CC0-1.0). ### Where can I find alternatives to EnterpriseRAG-Bench or awesome? GraphCanon lists graph-backed alternatives at [EnterpriseRAG-Bench alternatives](/tools/onyx-dot-app-enterpriserag-bench/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([EnterpriseRAG-Bench markdown twin](/tools/onyx-dot-app-enterpriserag-bench/alternatives.md), [awesome markdown twin](/tools/sindresorhus-awesome/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/onyx-dot-app-enterpriserag-bench-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, EnterpriseRAG-Bench or awesome? EnterpriseRAG-Bench: Steady. awesome: 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 EnterpriseRAG-Bench and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [EnterpriseRAG-Bench trust report](/tools/onyx-dot-app-enterpriserag-bench/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=onyx-dot-app-enterpriserag-bench`](/api/graphcanon/graph?tool=onyx-dot-app-enterpriserag-bench) - 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/_