--- title: "knowledge-gpt vs EnterpriseRAG-Bench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/geeks-of-data-knowledge-gpt-vs-onyx-dot-app-enterpriserag-bench" tools: ["geeks-of-data-knowledge-gpt", "onyx-dot-app-enterpriserag-bench"] --- # knowledge-gpt vs EnterpriseRAG-Bench *GraphCanon updated Aug 23, 2026* ## Verdict Pick knowledge-gpt if knowledge-gpt: Python toolkit for indexing and Q&A sessions with info sources using GPT & transformers; pick EnterpriseRAG-Bench if enterpriseRAG-Bench specializes in benchmarking RAG models on company internal documents, offering specific evaluation metrics for enterprise-level data. [knowledge-gpt](https://pypi.org/project/knowledgegpt/) reports 291 GitHub stars, 52 forks, and 8 open issues, last pushed Apr 25, 2023. [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 [knowledge-gpt's repository](https://github.com/geeks-of-data/knowledge-gpt) and [EnterpriseRAG-Bench's repository](https://github.com/onyx-dot-app/EnterpriseRAG-Bench). | | [knowledge-gpt](/tools/geeks-of-data-knowledge-gpt.md) | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | | --- | --- | --- | | Tagline | Extract knowledge from all information sources using GPT and other language models. Index and conduct Q&A sessions with information sources. | Dataset and benchmark for RAG on company internal documents | | Stars | 291 | 489 | | Forks | 52 | 52 | | Open issues | 8 | 9 | | Language | Python | - | | Adopt for | knowledge-gpt: Python toolkit for indexing and Q&A sessions with info sources using GPT & transformers. | 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, LLM Frameworks, Model Training | Data & Retrieval, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [knowledge-gpt](/tools/geeks-of-data-knowledge-gpt.md) | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 1216d | 81d | | Open issues (now) | 8 | 9 | | Stars delta | 0 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/geeks-of-data-knowledge-gpt/trust.md) | [trust report](/tools/onyx-dot-app-enterpriserag-bench/trust.md) | ## Decision facts: knowledge-gpt - **Adopt for:** knowledge-gpt: Python toolkit for indexing and Q&A sessions with info sources using GPT & transformers. ## 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 knowledge-gpt if… - Tags unique to knowledge-gpt: context, embedding-vectors, gpt, huggingface-transformers. - Also covers LLM Frameworks, Model Training. - knowledge-gpt ships Docker support for self-hosted deployment. - When you need a flexible, model-agnostic approach for Q&A over diverse data sources ### 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 291) - visibility, not fit. ## When NOT to use knowledge-gpt - Avoid if strictly needing real-time response performance without indexing capabilities - Not recommended if focusing solely on visual or multimedia content extraction ## 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 knowledge-gpt and EnterpriseRAG-Bench? knowledge-gpt: Extract knowledge from all information sources using GPT and other language models. Index and conduct Q&A sessions with information sources.. 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 knowledge-gpt over EnterpriseRAG-Bench? Choose knowledge-gpt over EnterpriseRAG-Bench when Tags unique to knowledge-gpt: context, embedding-vectors, gpt, huggingface-transformers; Also covers LLM Frameworks, Model Training; knowledge-gpt ships Docker support for self-hosted deployment; When you need a flexible, model-agnostic approach for Q&A over diverse data sources. ### When should I choose EnterpriseRAG-Bench over knowledge-gpt? Choose EnterpriseRAG-Bench over knowledge-gpt 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 291) - visibility, not fit. ### When should I avoid knowledge-gpt? Avoid if strictly needing real-time response performance without indexing capabilities Not recommended if focusing solely on visual or multimedia content extraction ### 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 knowledge-gpt or EnterpriseRAG-Bench more popular on GitHub? EnterpriseRAG-Bench has more GitHub stars (489 vs 291). Stars measure visibility, not whether either tool fits your constraints. ### Are knowledge-gpt and EnterpriseRAG-Bench open source? Yes - both are open-source projects on GitHub (knowledge-gpt: MIT, EnterpriseRAG-Bench: MIT). ### Where can I find alternatives to knowledge-gpt or EnterpriseRAG-Bench? GraphCanon lists graph-backed alternatives at [knowledge-gpt alternatives](/tools/geeks-of-data-knowledge-gpt/alternatives) and [EnterpriseRAG-Bench alternatives](/tools/onyx-dot-app-enterpriserag-bench/alternatives) ([knowledge-gpt markdown twin](/tools/geeks-of-data-knowledge-gpt/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/geeks-of-data-knowledge-gpt-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, knowledge-gpt or EnterpriseRAG-Bench? knowledge-gpt: Dormant. 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 knowledge-gpt and EnterpriseRAG-Bench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [knowledge-gpt trust report](/tools/geeks-of-data-knowledge-gpt/trust); [EnterpriseRAG-Bench trust report](/tools/onyx-dot-app-enterpriserag-bench/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=geeks-of-data-knowledge-gpt`](/api/graphcanon/graph?tool=geeks-of-data-knowledge-gpt) - 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/_