Comparison
data-juicer vs paperless-ngx
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.
Markdown twin · data-juicer alternatives · paperless-ngx alternatives
GraphCanon updated Sep 18, 2026
vs
Trust & integrity
| Signal | data-juicer | paperless-ngx |
|---|---|---|
| Maintenance | Very active (4d since push) As of Aug 17, 2026 · github_public_v1 | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Aug 17, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- data-juicer
- Data processing for and with foundation models
- paperless-ngx
- A community-supported supercharged document management system
Stars
- data-juicer
- 6.9k
- paperless-ngx
- 45k
Forks
- data-juicer
- 404
- paperless-ngx
- 3.1k
Open issues
- data-juicer
- 59
- paperless-ngx
- 6
Language
- data-juicer
- Python
- paperless-ngx
- Python
Adopt for
- data-juicer
- A Python library for foundational AI model data processing, offering a pipeline for tasks like instruction tuning and synthetic data generation.
- paperless-ngx
- 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
- data-juicer
- -
- paperless-ngx
- -
Runtime
- data-juicer
- -
- paperless-ngx
- -
License
- data-juicer
- Apache-2.0
- paperless-ngx
- GPL-3.0
Last pushed
- data-juicer
- Aug 13, 2026
- paperless-ngx
- Sep 18, 2026
Categories
- data-juicer
- Data & Retrieval, Model Training
- paperless-ngx
- Data & Retrieval
Trust and health
Days since push
- data-juicer
- 4d
- paperless-ngx
- 0d
Open issues (now)
- data-juicer
- 59
- paperless-ngx
- 6
Stars delta
- data-juicer
- +166 (30d)
- paperless-ngx
- Unknown
Open issues delta
- data-juicer
- -3 (30d)
- paperless-ngx
- Unknown
Full report
- data-juicer
- Trust report
- paperless-ngx
- Trust report
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (datajuicer/data-juicer) · observed Aug 17, 2026
- GitHub forks (datajuicer/data-juicer) · observed Aug 17, 2026
- Last push (datajuicer/data-juicer) · observed Aug 13, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (paperless-ngx/paperless-ngx) · observed Sep 18, 2026
- GitHub forks (paperless-ngx/paperless-ngx) · observed Sep 18, 2026
- Last push (paperless-ngx/paperless-ngx) · observed Sep 18, 2026
- License file (GPL-3.0) · observed Sep 18, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: data-juicer 6.9k · paperless-ngx 45k (synced Aug 17, 2026).
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 and paperless-ngx alternatives (data-juicer markdown twin, paperless-ngx markdown twin), 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 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; paperless-ngx trust report.