--- title: "EnterpriseRAG-Bench vs llm-app" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/onyx-dot-app-enterpriserag-bench-vs-pathwaycom-llm-app" tools: ["onyx-dot-app-enterpriserag-bench", "pathwaycom-llm-app"] --- # EnterpriseRAG-Bench vs llm-app *GraphCanon updated Jul 11, 2026* ## Verdict Pick EnterpriseRAG-Bench when tags unique to EnterpriseRAG-Bench: evaluation, dataset, benchmark, enterprise-search; pick llm-app when requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.. [EnterpriseRAG-Bench](https://www.onyx.app/) reports 454 GitHub stars, 46 forks, and 9 open issues, last pushed May 8, 2026. [llm-app](https://pathway.com/developers/templates/) has 59k stars, 1.4k forks, and 10 open issues, last pushed Jul 5, 2026. Figures are from public GitHub metadata via [EnterpriseRAG-Bench's repository](https://github.com/onyx-dot-app/EnterpriseRAG-Bench) and [llm-app's repository](https://github.com/pathwaycom/llm-app). | | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Tagline | Dataset and benchmark for RAG on company internal documents. | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | | Stars | 454 | 59,068 | | Forks | 46 | 1,432 | | Open issues | 9 | 10 | | Language | - | Jupyter Notebook | | Adopt for | - | llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | LLM Frameworks, Data & Retrieval, Evaluation & Observability | LLM Frameworks, Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 64d | 5d | | Open issues (now) | 9 | 10 | | Full report | [trust report](/tools/onyx-dot-app-enterpriserag-bench/trust.md) | [trust report](/tools/pathwaycom-llm-app/trust.md) | ## Decision facts: llm-app - **Requirements:** Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others. - **Adopt for:** llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz ## Choose when ### Choose EnterpriseRAG-Bench if… - Tags unique to EnterpriseRAG-Bench: evaluation, dataset, benchmark, enterprise-search. - Also covers Evaluation & Observability. - Leaner open-issue backlog (9). ### Choose llm-app if… - Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.. - Tags unique to llm-app: vector-database, llm, hugging-face, retrieval-augmented-generation. - Also covers Vector Databases. - - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs. ## 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 llm-app - - You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app. - - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app. ## Common questions ### What is the difference between EnterpriseRAG-Bench and llm-app? EnterpriseRAG-Bench: Dataset and benchmark for RAG on company internal documents.. llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. See the comparison table for live GitHub stats and shared categories. ### When should I choose EnterpriseRAG-Bench over llm-app? Choose EnterpriseRAG-Bench over llm-app when Tags unique to EnterpriseRAG-Bench: evaluation, dataset, benchmark, enterprise-search; Also covers Evaluation & Observability; Leaner open-issue backlog (9). ### When should I choose llm-app over EnterpriseRAG-Bench? Choose llm-app over EnterpriseRAG-Bench when Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.; Tags unique to llm-app: vector-database, llm, hugging-face, retrieval-augmented-generation; Also covers Vector Databases; - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs. ### 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 llm-app? - You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app. - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app. ### Is EnterpriseRAG-Bench or llm-app more popular on GitHub? llm-app has more GitHub stars (59,068 vs 454). Stars measure visibility, not whether either tool fits your constraints. ### Are EnterpriseRAG-Bench and llm-app open source? Yes - both are open-source projects on GitHub (EnterpriseRAG-Bench: MIT, llm-app: MIT). ### Where can I find alternatives to EnterpriseRAG-Bench or llm-app? GraphCanon lists graph-backed alternatives at [EnterpriseRAG-Bench alternatives](/tools/onyx-dot-app-enterpriserag-bench/alternatives) and [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) ([EnterpriseRAG-Bench markdown twin](/tools/onyx-dot-app-enterpriserag-bench/alternatives.md), [llm-app markdown twin](/tools/pathwaycom-llm-app/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-pathwaycom-llm-app.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, EnterpriseRAG-Bench or llm-app? EnterpriseRAG-Bench: Steady. llm-app: 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 EnterpriseRAG-Bench and llm-app? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [EnterpriseRAG-Bench trust report](/tools/onyx-dot-app-enterpriserag-bench/trust); [llm-app trust report](/tools/pathwaycom-llm-app/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/_