--- title: "knowledge_gpt vs EnterpriseRAG-Bench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mmz-001-knowledge-gpt-vs-onyx-dot-app-enterpriserag-bench" tools: ["mmz-001-knowledge-gpt", "onyx-dot-app-enterpriserag-bench"] --- # knowledge_gpt vs EnterpriseRAG-Bench *GraphCanon updated Aug 15, 2026* ## Verdict Pick knowledge_gpt if knowledge_gpt is a Python-based tool with Streamlit UI, MIT licensed, for generating accurate document responses with citations; 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://knowledgegpt.streamlit.app/) reports 1.6k GitHub stars, 787 forks, and 16 open issues, last pushed May 29, 2024. [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/mmz-001/knowledge_gpt) and [EnterpriseRAG-Bench's repository](https://github.com/onyx-dot-app/EnterpriseRAG-Bench). | | [knowledge_gpt](/tools/mmz-001-knowledge-gpt.md) | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | | --- | --- | --- | | Tagline | Accurate answers and instant citations for your documents. | Dataset and benchmark for RAG on company internal documents | | Stars | 1,634 | 489 | | Forks | 787 | 52 | | Open issues | 16 | 9 | | Language | Python | - | | Adopt for | knowledge_gpt is a Python-based tool with Streamlit UI, MIT licensed, for generating accurate document responses with citations. | 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._ | | [knowledge_gpt](/tools/mmz-001-knowledge-gpt.md) | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Steady (60%) | | Days since push | 807d | 81d | | Archived on GitHub | Yes | No | | Open issues (now) | 16 | 9 | | Stars delta | -3 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/mmz-001-knowledge-gpt/trust.md) | [trust report](/tools/onyx-dot-app-enterpriserag-bench/trust.md) | ## Decision facts: knowledge_gpt - **Adopt for:** knowledge_gpt is a Python-based tool with Streamlit UI, MIT licensed, for generating accurate document responses with citations. ## 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: docker, document-analysis, python, streamlit. - knowledge_gpt ships Docker support for self-hosted deployment. - When you need to generate answers from documents alongside instant citations ### 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 recently updated (last pushed May 8, 2026). ## When NOT to use knowledge_gpt - If your project exclusively requires web-based services without local deployments - In scenarios where real-time citation generation is not necessary ## 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: Accurate answers and instant citations for your documents.. 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: docker, document-analysis, python, streamlit; knowledge_gpt ships Docker support for self-hosted deployment; When you need to generate answers from documents alongside instant citations. ### 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 recently updated (last pushed May 8, 2026). ### When should I avoid knowledge_gpt? If your project exclusively requires web-based services without local deployments In scenarios where real-time citation generation is not necessary ### 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? knowledge_gpt has more GitHub stars (1,634 vs 489). 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/mmz-001-knowledge-gpt/alternatives) and [EnterpriseRAG-Bench alternatives](/tools/onyx-dot-app-enterpriserag-bench/alternatives) ([knowledge_gpt markdown twin](/tools/mmz-001-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/mmz-001-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: Archived. 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/mmz-001-knowledge-gpt/trust); [EnterpriseRAG-Bench trust report](/tools/onyx-dot-app-enterpriserag-bench/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mmz-001-knowledge-gpt`](/api/graphcanon/graph?tool=mmz-001-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/_