--- title: "ragtune vs EnterpriseRAG-Bench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/metawake-ragtune-vs-onyx-dot-app-enterpriserag-bench" tools: ["metawake-ragtune", "onyx-dot-app-enterpriserag-bench"] --- # ragtune vs EnterpriseRAG-Bench *GraphCanon updated Aug 2, 2026* ## Verdict Pick ragtune if ragtune is a Go-based benchmarking tool for RAG retrieval systems that allows users to inspect, debug, benchmark, and tune the retrieval layer; pick EnterpriseRAG-Bench if enterpriseRAG-Bench specializes in benchmarking RAG models on company internal documents, offering specific evaluation metrics for enterprise-level data. [ragtune](https://github.com/metawake/ragtune) reports 13 GitHub stars, 1 forks, and 0 open issues, last pushed Mar 25, 2026. [EnterpriseRAG-Bench](https://www.onyx.app/) has 489 stars, 52 forks, and 9 open issues, last pushed May 8, 2026. Figures are from public GitHub metadata via [ragtune's repository](https://github.com/metawake/ragtune) and [EnterpriseRAG-Bench's repository](https://github.com/onyx-dot-app/EnterpriseRAG-Bench). | | [ragtune](/tools/metawake-ragtune.md) | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | | --- | --- | --- | | Tagline | Benchmarking tool for RAG retrieval, aiding in tuning and evaluating retrieval layers | Dataset and benchmark for RAG on company internal documents | | Stars | 13 | 489 | | Forks | 1 | 52 | | Open issues | 0 | 9 | | Language | Go | - | | Adopt for | Ragtune is a Go-based benchmarking tool for RAG retrieval systems that allows users to inspect, debug, benchmark, and tune the retrieval layer. | EnterpriseRAG-Bench specializes in benchmarking RAG models on company internal documents, offering specific evaluation metrics for enterprise-level data. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT license allows free usage and modification with attribution. | | Categories | Data & Retrieval, Evaluation & Observability | Data & Retrieval, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ragtune](/tools/metawake-ragtune.md) | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Steady (60%) | | Days since push | 129d | 81d | | Open issues (now) | 0 | 9 | | Owner type | User | Organization | | Full report | [trust report](/tools/metawake-ragtune/trust.md) | [trust report](/tools/onyx-dot-app-enterpriserag-bench/trust.md) | ## Decision facts: ragtune - **Adopt for:** Ragtune is a Go-based benchmarking tool for RAG retrieval systems that allows users to inspect, debug, benchmark, and tune the retrieval layer. ## Decision facts: EnterpriseRAG-Bench - **Adopt for:** EnterpriseRAG-Bench specializes in benchmarking RAG models on company internal documents, offering specific evaluation metrics for enterprise-level data. - **License detail:** MIT license allows free usage and modification with attribution. ## Choose when ### Choose ragtune if… - Tags unique to ragtune: benchmarking, embeddings, metrics, retrieval-augmented-generation. - For organizations using multiple vector search engines like Chroma or Pinecone because Ragtune supports them directly. - Leaner open-issue backlog (0). ### Choose EnterpriseRAG-Bench if… - Tags unique to EnterpriseRAG-Bench: benchmark, dataset, enterprise-search, evaluation. - When you need to evaluate retrieval-augmented generation models specifically for processing extensive and complex enterprise documentation - More GitHub stars (489 vs 13) - visibility, not fit. ## When NOT to use ragtune - If your project relies on languages other than Go, as Ragtune might not integrate smoothly without additional effort. - When the primary focus of retrieval layer tuning lies outside supported vector search engines like Chroma or Qdrant and no customization can be applied via the tool. ## When NOT to use EnterpriseRAG-Bench - Avoid if your focus is on general web or public-domain document benchmarking, as EnterpriseRAG-Bench is tuned exclusively for company internal documents - Do not use if you require a solution that supports languages other than those implied by the existing dataset without further customization ## Common questions ### What is the difference between ragtune and EnterpriseRAG-Bench? ragtune: Benchmarking tool for RAG retrieval, aiding in tuning and evaluating retrieval layers. EnterpriseRAG-Bench: Dataset and benchmark for RAG on company internal documents. See the comparison table for live GitHub stats and shared categories. ### When should I choose ragtune over EnterpriseRAG-Bench? Choose ragtune over EnterpriseRAG-Bench when Tags unique to ragtune: benchmarking, embeddings, metrics, retrieval-augmented-generation; For organizations using multiple vector search engines like Chroma or Pinecone because Ragtune supports them directly; Leaner open-issue backlog (0). ### When should I choose EnterpriseRAG-Bench over ragtune? Choose EnterpriseRAG-Bench over ragtune when Tags unique to EnterpriseRAG-Bench: benchmark, dataset, enterprise-search, evaluation; When you need to evaluate retrieval-augmented generation models specifically for processing extensive and complex enterprise documentation; More GitHub stars (489 vs 13) - visibility, not fit. ### When should I avoid ragtune? If your project relies on languages other than Go, as Ragtune might not integrate smoothly without additional effort. When the primary focus of retrieval layer tuning lies outside supported vector search engines like Chroma or Qdrant and no customization can be applied via the tool. ### When should I avoid EnterpriseRAG-Bench? Avoid if your focus is on general web or public-domain document benchmarking, as EnterpriseRAG-Bench is tuned exclusively for company internal documents Do not use if you require a solution that supports languages other than those implied by the existing dataset without further customization ### Is ragtune or EnterpriseRAG-Bench more popular on GitHub? EnterpriseRAG-Bench has more GitHub stars (489 vs 13). Stars measure visibility, not whether either tool fits your constraints. ### Are ragtune and EnterpriseRAG-Bench open source? Yes - both are open-source projects on GitHub (ragtune: MIT, EnterpriseRAG-Bench: MIT). ### Where can I find alternatives to ragtune or EnterpriseRAG-Bench? GraphCanon lists graph-backed alternatives at [ragtune alternatives](/tools/metawake-ragtune/alternatives) and [EnterpriseRAG-Bench alternatives](/tools/onyx-dot-app-enterpriserag-bench/alternatives) ([ragtune markdown twin](/tools/metawake-ragtune/alternatives.md), [EnterpriseRAG-Bench markdown twin](/tools/onyx-dot-app-enterpriserag-bench/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/metawake-ragtune-vs-onyx-dot-app-enterpriserag-bench.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ragtune or EnterpriseRAG-Bench? ragtune: Slowing. EnterpriseRAG-Bench: Steady. 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 ragtune and EnterpriseRAG-Bench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ragtune trust report](/tools/metawake-ragtune/trust); [EnterpriseRAG-Bench trust report](/tools/onyx-dot-app-enterpriserag-bench/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=metawake-ragtune`](/api/graphcanon/graph?tool=metawake-ragtune) - 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/_