--- title: "paperless-ai vs EnterpriseRAG-Bench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/clusterzx-paperless-ai-vs-onyx-dot-app-enterpriserag-bench" tools: ["clusterzx-paperless-ai", "onyx-dot-app-enterpriserag-bench"] --- # paperless-ai vs EnterpriseRAG-Bench *GraphCanon updated Sep 20, 2026* ## Verdict Pick paperless-ai if paperless-ai is a JavaScript-built automated document analyzer for Paperless-ngx that tags documents using OpenAI API and compatible services such as Ollama, Deepseek-r1, and Azure; pick EnterpriseRAG-Bench if enterpriseRAG-Bench specializes in benchmarking RAG models on company internal documents, offering specific evaluation metrics for enterprise-level data. [paperless-ai](https://clusterzx.github.io/paperless-ai/) reports 6.0k GitHub stars, 331 forks, and 56 open issues, last pushed Sep 19, 2026. [EnterpriseRAG-Bench](https://www.onyx.app/) has 562 stars, 62 forks, and 13 open issues, last pushed Sep 3, 2026. Figures are from public GitHub metadata via [paperless-ai's repository](https://github.com/clusterzx/paperless-ai) and [EnterpriseRAG-Bench's repository](https://github.com/onyx-dot-app/EnterpriseRAG-Bench). | | [paperless-ai](/tools/clusterzx-paperless-ai.md) | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | | --- | --- | --- | | Tagline | Automated document analyzer for Paperless-ngx using OpenAI API and compatible services to tag documents | Dataset and benchmark for RAG on company internal documents | | Stars | 5,950 | 562 | | Forks | 331 | 62 | | Open issues | 56 | 13 | | Language | JavaScript | - | | Adopt for | Paperless-ai is a JavaScript-built automated document analyzer for Paperless-ngx that tags documents using OpenAI API and compatible services such as Ollama, Deepseek-r1, and Azure. | 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 | Evaluation & Observability, Model Training | Data & Retrieval, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [paperless-ai](/tools/clusterzx-paperless-ai.md) | [EnterpriseRAG-Bench](/tools/onyx-dot-app-enterpriserag-bench.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 1d | 16d | | Open issues (now) | 56 | 13 | | Stars delta | +68 (30d) | +73 (30d) | | Open issues delta | -7 (30d) | +4 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/clusterzx-paperless-ai/trust.md) | [trust report](/tools/onyx-dot-app-enterpriserag-bench/trust.md) | ## Decision facts: paperless-ai - **Adopt for:** Paperless-ai is a JavaScript-built automated document analyzer for Paperless-ngx that tags documents using OpenAI API and compatible services such as Ollama, Deepseek-r1, and Azure. ## 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 paperless-ai if… - Tags unique to paperless-ai: ai, automation, gemma, llama. - Also covers Model Training. - paperless-ai ships Docker support for self-hosted deployment. - - When you require integration with Paperless-ngx for managing digital documents automatically with tagging capabilities ### Choose EnterpriseRAG-Bench if… - Tags unique to EnterpriseRAG-Bench: benchmark, dataset, enterprise-search, evaluation. - Also covers Data & Retrieval. - When you need to evaluate retrieval-augmented generation models specifically for processing extensive and complex enterprise documentation ## When NOT to use paperless-ai - - For projects that do not involve the management or automatic analysis of digital documents within a Paperless-ngx context - - In environments where the specific services it integrates with, such as Ollama and Deepseek-r1, are unavailable or unsupported ## 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 paperless-ai and EnterpriseRAG-Bench? paperless-ai: Automated document analyzer for Paperless-ngx using OpenAI API and compatible services to tag 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 paperless-ai over EnterpriseRAG-Bench? Choose paperless-ai over EnterpriseRAG-Bench when Tags unique to paperless-ai: ai, automation, gemma, llama; Also covers Model Training; paperless-ai ships Docker support for self-hosted deployment; - When you require integration with Paperless-ngx for managing digital documents automatically with tagging capabilities. ### When should I choose EnterpriseRAG-Bench over paperless-ai? Choose EnterpriseRAG-Bench over paperless-ai when Tags unique to EnterpriseRAG-Bench: benchmark, dataset, enterprise-search, evaluation; Also covers Data & Retrieval; When you need to evaluate retrieval-augmented generation models specifically for processing extensive and complex enterprise documentation. ### When should I avoid paperless-ai? - For projects that do not involve the management or automatic analysis of digital documents within a Paperless-ngx context - In environments where the specific services it integrates with, such as Ollama and Deepseek-r1, are unavailable or unsupported ### 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 paperless-ai or EnterpriseRAG-Bench more popular on GitHub? paperless-ai has more GitHub stars (5,950 vs 562). Stars measure visibility, not whether either tool fits your constraints. ### Are paperless-ai and EnterpriseRAG-Bench open source? Yes - both are open-source projects on GitHub (paperless-ai: MIT, EnterpriseRAG-Bench: MIT). ### Where can I find alternatives to paperless-ai or EnterpriseRAG-Bench? GraphCanon lists graph-backed alternatives at [paperless-ai alternatives](/tools/clusterzx-paperless-ai/alternatives) and [EnterpriseRAG-Bench alternatives](/tools/onyx-dot-app-enterpriserag-bench/alternatives) ([paperless-ai markdown twin](/tools/clusterzx-paperless-ai/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/clusterzx-paperless-ai-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, paperless-ai or EnterpriseRAG-Bench? paperless-ai: Very active. EnterpriseRAG-Bench: 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 paperless-ai and EnterpriseRAG-Bench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [paperless-ai trust report](/tools/clusterzx-paperless-ai/trust); [EnterpriseRAG-Bench trust report](/tools/onyx-dot-app-enterpriserag-bench/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=clusterzx-paperless-ai`](/api/graphcanon/graph?tool=clusterzx-paperless-ai) - 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/_