Comparison
paperless-ai vs data-juicer
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 data-juicer if a Python library for foundational AI model data processing, offering a pipeline for tasks like instruction tuning and synthetic data generation.
Markdown twin · paperless-ai alternatives · data-juicer alternatives
GraphCanon updated Sep 20, 2026
13views this month
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Trust & integrity
| Signal | paperless-ai | data-juicer |
|---|---|---|
| Maintenance | Very active (1d since push) As of Sep 20, 2026 · github_public_v1 | Very active (4d since push) As of Aug 17, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Aug 17, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 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
- paperless-ai
- Automated document analyzer for Paperless-ngx using OpenAI API and compatible services to tag documents
- data-juicer
- Data processing for and with foundation models
Stars
- paperless-ai
- 6.0k
- data-juicer
- 6.9k
Forks
- paperless-ai
- 331
- data-juicer
- 404
Open issues
- paperless-ai
- 56
- data-juicer
- 59
Language
- paperless-ai
- JavaScript
- data-juicer
- Python
Adopt for
- paperless-ai
- 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.
- data-juicer
- A Python library for foundational AI model data processing, offering a pipeline for tasks like instruction tuning and synthetic data generation.
Persona
- paperless-ai
- -
- data-juicer
- -
Runtime
- paperless-ai
- -
- data-juicer
- -
License
- paperless-ai
- MIT
- data-juicer
- Apache-2.0
Last pushed
- paperless-ai
- Sep 19, 2026
- data-juicer
- Aug 13, 2026
Categories
- paperless-ai
- Evaluation & Observability, Model Training
- data-juicer
- Data & Retrieval, Model Training
Trust and health
Days since push
- paperless-ai
- 1d
- data-juicer
- 4d
Open issues (now)
- paperless-ai
- 56
- data-juicer
- 59
Stars delta
- paperless-ai
- +68 (30d)
- data-juicer
- +166 (30d)
Open issues delta
- paperless-ai
- -7 (30d)
- data-juicer
- -3 (30d)
Owner type
- paperless-ai
- User
- data-juicer
- Organization
Full report
- paperless-ai
- Trust report
- data-juicer
- Trust report
Choose paperless-ai if…
- paperless-ai is primarily JavaScript; data-juicer is Python.
- License: paperless-ai is MIT, data-juicer is Apache-2.0.
- Tags unique to paperless-ai: ai, automation, gemma, llama.
- Also covers Evaluation & Observability.
- - When you require integration with Paperless-ngx for managing digital documents automatically with tagging capabilities
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
Choose data-juicer if…
- data-juicer is primarily Python; paperless-ai is JavaScript.
- License: data-juicer is Apache-2.0, paperless-ai is MIT.
- Tags unique to data-juicer: foundation-models, instruction-tuning, large-language-models, llm.
- Also covers Data & Retrieval.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (clusterzx/paperless-ai) · observed Sep 20, 2026
- GitHub forks (clusterzx/paperless-ai) · observed Sep 20, 2026
- Last push (clusterzx/paperless-ai) · observed Sep 19, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (datajuicer/data-juicer) · observed Sep 20, 2026
- GitHub forks (datajuicer/data-juicer) · observed Sep 20, 2026
- Last push (datajuicer/data-juicer) · observed Aug 13, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: paperless-ai 6.0k · data-juicer 6.9k (synced Sep 20, 2026).
Common questions
- What is the difference between paperless-ai and data-juicer?
- paperless-ai: Automated document analyzer for Paperless-ngx using OpenAI API and compatible services to tag documents. data-juicer: Data processing for and with foundation models. See the comparison table for live GitHub stats and shared categories.
- When should I choose paperless-ai over data-juicer?
- Choose paperless-ai over data-juicer when paperless-ai is primarily JavaScript; data-juicer is Python; License: paperless-ai is MIT, data-juicer is Apache-2.0; Tags unique to paperless-ai: ai, automation, gemma, llama; Also covers Evaluation & Observability; - When you require integration with Paperless-ngx for managing digital documents automatically with tagging capabilities.
- When should I choose data-juicer over paperless-ai?
- Choose data-juicer over paperless-ai when data-juicer is primarily Python; paperless-ai is JavaScript; License: data-juicer is Apache-2.0, paperless-ai is MIT; Tags unique to data-juicer: foundation-models, instruction-tuning, large-language-models, llm; Also covers Data & Retrieval; 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 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 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.
- Is paperless-ai or data-juicer more popular on GitHub?
- data-juicer has more GitHub stars (6,897 vs 5,950). Stars measure visibility, not whether either tool fits your constraints.
- Are paperless-ai and data-juicer open source?
- Yes - both are open-source projects on GitHub (paperless-ai: MIT, data-juicer: Apache-2.0).
- Where can I find alternatives to paperless-ai or data-juicer?
- GraphCanon lists graph-backed alternatives at paperless-ai alternatives and data-juicer alternatives (paperless-ai markdown twin, data-juicer 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, paperless-ai or data-juicer?
- paperless-ai: Very active. data-juicer: 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 paperless-ai and data-juicer?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: paperless-ai trust report; data-juicer trust report.