--- title: "data-juicer vs paperless-ngx" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datajuicer-data-juicer-vs-paperless-ngx-paperless-ngx" tools: ["datajuicer-data-juicer", "paperless-ngx-paperless-ngx"] --- # data-juicer vs paperless-ngx *GraphCanon updated Sep 20, 2026* ## Verdict Pick data-juicer if a Python library for foundational AI model data processing, offering a pipeline for tasks like instruction tuning and synthetic data generation; pick paperless-ngx if paperless-ngx is a community-supported document management system that leverages OCR and machine learning for scanning, indexing, and archiving documents. It is built with Python and is licensed under GPL-3.0. [data-juicer](https://datajuicer.github.io/data-juicer/) reports 6.9k GitHub stars, 404 forks, and 59 open issues, last pushed Aug 13, 2026. [paperless-ngx](http://docs.paperless-ngx.com/) has 45k stars, 3.1k forks, and 6 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [data-juicer's repository](https://github.com/datajuicer/data-juicer) and [paperless-ngx's repository](https://github.com/paperless-ngx/paperless-ngx). | | [data-juicer](/tools/datajuicer-data-juicer.md) | [paperless-ngx](/tools/paperless-ngx-paperless-ngx.md) | | --- | --- | --- | | Tagline | Data processing for and with foundation models | A community-supported supercharged document management system | | Stars | 6,897 | 45,263 | | Forks | 404 | 3,122 | | Open issues | 59 | 6 | | Language | Python | Python | | Adopt for | A Python library for foundational AI model data processing, offering a pipeline for tasks like instruction tuning and synthetic data generation. | paperless-ngx is a community-supported document management system that leverages OCR and machine learning for scanning, indexing, and archiving documents. It is built with Python and is licensed under GPL-3.0. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | GPL-3.0 | | Categories | Data & Retrieval, Model Training | Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [data-juicer](/tools/datajuicer-data-juicer.md) | [paperless-ngx](/tools/paperless-ngx-paperless-ngx.md) | | --- | --- | --- | | Days since push | 4d | 0d | | Open issues (now) | 59 | 6 | | Stars delta | +166 (30d) | Unknown | | Open issues delta | -3 (30d) | Unknown | | Full report | [trust report](/tools/datajuicer-data-juicer/trust.md) | [trust report](/tools/paperless-ngx-paperless-ngx/trust.md) | ## Decision facts: data-juicer - **Adopt for:** A Python library for foundational AI model data processing, offering a pipeline for tasks like instruction tuning and synthetic data generation. ## Decision facts: paperless-ngx - **Adopt for:** paperless-ngx is a community-supported document management system that leverages OCR and machine learning for scanning, indexing, and archiving documents. It is built with Python and is licensed under GPL-3.0. ## Choose when ### Choose data-juicer if… - License: data-juicer is Apache-2.0, paperless-ngx is GPL-3.0. - Tags unique to data-juicer: foundation-models, instruction-tuning, large-language-models, synthetic-data. - Also covers Model Training. - When you need to preprocess large datasets specifically for training large language models (LLMs) with pipelines that support sophisticated processes like instruction tuning. ### Choose paperless-ngx if… - License: paperless-ngx is GPL-3.0, data-juicer is Apache-2.0. - Tags unique to paperless-ngx: ai, angular, archiving, django. - Use paperless-ngx if you are looking for a system that supports scanning, indexing, and archiving documents with a strong community support and continuous updates. ## When NOT to use data-juicer - If your project does not involve foundational AI model training or if you do not require advanced data processing capabilities such as synthetic data generation. ## When NOT to use paperless-ngx - Avoid paperless-ngx if you require a proprietary solution with commercial support, as it is an open-source project under GPL-3.0. - Do not use paperless-ngx if you need a document management system that does not rely on Docker for deployment, as it heavily integrates Docker for its setup. - Skip paperless-ngx if you are looking for a system that does not involve community-supported development, as it may not meet specific enterprise-level requirements for customization and support. ## Common questions ### What is the difference between data-juicer and paperless-ngx? data-juicer: Data processing for and with foundation models. paperless-ngx: A community-supported supercharged document management system. See the comparison table for live GitHub stats and shared categories. ### When should I choose data-juicer over paperless-ngx? Choose data-juicer over paperless-ngx when License: data-juicer is Apache-2.0, paperless-ngx is GPL-3.0; Tags unique to data-juicer: foundation-models, instruction-tuning, large-language-models, synthetic-data; Also covers Model Training; When you need to preprocess large datasets specifically for training large language models (LLMs) with pipelines that support sophisticated processes like instruction tuning. ### When should I choose paperless-ngx over data-juicer? Choose paperless-ngx over data-juicer when License: paperless-ngx is GPL-3.0, data-juicer is Apache-2.0; Tags unique to paperless-ngx: ai, angular, archiving, django; Use paperless-ngx if you are looking for a system that supports scanning, indexing, and archiving documents with a strong community support and continuous updates. ### When should I avoid data-juicer? If your project does not involve foundational AI model training or if you do not require advanced data processing capabilities such as synthetic data generation. ### When should I avoid paperless-ngx? Avoid paperless-ngx if you require a proprietary solution with commercial support, as it is an open-source project under GPL-3.0. Do not use paperless-ngx if you need a document management system that does not rely on Docker for deployment, as it heavily integrates Docker for its setup. Skip paperless-ngx if you are looking for a system that does not involve community-supported development, as it may not meet specific enterprise-level requirements for customization and support. ### Is data-juicer or paperless-ngx more popular on GitHub? paperless-ngx has more GitHub stars (45,263 vs 6,897). Stars measure visibility, not whether either tool fits your constraints. ### Are data-juicer and paperless-ngx open source? Yes - both are open-source projects on GitHub (data-juicer: Apache-2.0, paperless-ngx: GPL-3.0). ### Where can I find alternatives to data-juicer or paperless-ngx? GraphCanon lists graph-backed alternatives at [data-juicer alternatives](/tools/datajuicer-data-juicer/alternatives) and [paperless-ngx alternatives](/tools/paperless-ngx-paperless-ngx/alternatives) ([data-juicer markdown twin](/tools/datajuicer-data-juicer/alternatives.md), [paperless-ngx markdown twin](/tools/paperless-ngx-paperless-ngx/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/datajuicer-data-juicer-vs-paperless-ngx-paperless-ngx.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, data-juicer or paperless-ngx? data-juicer: Very active. paperless-ngx: 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 data-juicer and paperless-ngx? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [data-juicer trust report](/tools/datajuicer-data-juicer/trust); [paperless-ngx trust report](/tools/paperless-ngx-paperless-ngx/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=datajuicer-data-juicer`](/api/graphcanon/graph?tool=datajuicer-data-juicer) - 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/_