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Comparison

all-in-rag vs rag-time

Verdict

Pick all-in-rag if all-in-rag is a comprehensive guide for developers to learn about and implement RAG (Retrieval-Augmented Generation) technology, with a focus on end-to-end practical applications and multi-modal support. It provides an体系; pick rag-time if rAG Time is tailored for those looking to systematically learn and apply Retrieval-Augmented Generation techniques in a structured 5-week program.

Markdown twin · all-in-rag alternatives · rag-time alternatives

GraphCanon updated 2d

all-in-rag logo

all-in-rag

datawhalechina/all-in-rag

★ 10kpushed Jul 29, 2026
vs
rag-time logo

rag-time

microsoft/rag-time

★ 893pushed Jun 17, 2025

Trust & integrity

Signalall-in-ragrag-time
Maintenance
Active (20d since push)
As of 2d · github_public_v1
Dormant (401d since push)
As of 4w · github_public_v1
Provenance
Not a fork · Organization account
As of 2d · github_public_v1
Not a fork · Organization account
As of 4w · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
No lockfile (source not queried)
As of 1mo · 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
all-in-rag trust reportrag-time trust reportTrust methodology

Tagline

all-in-rag
🔍 检索增强生成 (RAG) 技术全栈指南
rag-time
RAG Time: A 5-week Learning Journey to Mastering RAG

Stars

all-in-rag
10k
rag-time
893

Forks

all-in-rag
5.2k
rag-time
316

Open issues

all-in-rag
23
rag-time
4

Language

all-in-rag
Python
rag-time
Jupyter Notebook

Adopt for

all-in-rag
all-in-rag is a comprehensive guide for developers to learn about and implement RAG (Retrieval-Augmented Generation) technology, with a focus on end-to-end practical applications and multi-modal support. It provides an体系
rag-time
RAG Time is tailored for those looking to systematically learn and apply Retrieval-Augmented Generation techniques in a structured 5-week program.

Persona

all-in-rag
-
rag-time
-

Runtime

all-in-rag
-
rag-time
-

License

all-in-rag
-
rag-time
The MIT License provides freedom to use, copy, modify and distribute the software provided that copyright and license information are retained.

Last pushed

all-in-rag
Jul 29, 2026
rag-time
Jun 17, 2025

Categories

all-in-rag
Data & Retrieval, LLM Frameworks
rag-time
Data & Retrieval, LLM Frameworks, Model Training

Trust and health

Maintenance

all-in-rag
Active (82%)
rag-time
Dormant (18%)

Days since push

all-in-rag
20d
rag-time
401d

Open issues (now)

all-in-rag
23
rag-time
4

Stars delta

all-in-rag
+815 (30d)
rag-time
Unknown

Open issues delta

all-in-rag
+3 (30d)
rag-time
Unknown

Full report

all-in-rag
Trust report
rag-time
Trust report

Choose all-in-rag if…

  • all-in-rag is primarily Python; rag-time is Jupyter Notebook.
  • Tags unique to all-in-rag: embedding, langchain, milvus, multimodal.
  • - When you want a comprehensive resource that covers both the theoretical foundations and practical application of RAG.

When NOT to use all-in-rag

  • - Avoid if you are looking for a solution that only focuses on theoretical aspects without practical implementation guidance.
  • - If your project does not require multi-modal support or is solely focused on text-based applications, more specialized tools might provide better optimization.
  • - Not suitable if you're seeking quick prototyping or a light-weight framework; all-in-rag emphasizes comprehensive learning and production-ready practices.
GitHubDetails

Choose rag-time if…

  • rag-time is primarily Jupyter Notebook; all-in-rag is Python.
  • Requirements: Min 8 GB RAM.
  • Tags unique to rag-time: generative-ai, hybrid-search, indexing, language-model.
  • Also covers Model Training.
  • When you need a detailed, week-by-week learning path specifically focused on the nuances of RAG techniques, from basics to advanced applications.

When NOT to use rag-time

  • If you prefer ad-hoc or self-directed learning without a structured timeline. Other tools may offer more flexible formats, which can be preferable if adhering to strict schedules is not ideal.
  • When your focus is solely on either indexing or generation models and not the integration of both for RAG. In this case, specialized resources for just indexing or model training might suffice.
GitHubDetails

Explore

all-in-rag trust report →rag-time trust report →Data & Retrieval category →LLM Frameworks category →Model Training category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: all-in-rag 10k · rag-time 893 (synced Aug 18, 2026).

Common questions

What is the difference between all-in-rag and rag-time?
all-in-rag: 🔍 检索增强生成 (RAG) 技术全栈指南. rag-time: RAG Time: A 5-week Learning Journey to Mastering RAG. See the comparison table for live GitHub stats and shared categories.
When should I choose all-in-rag over rag-time?
Choose all-in-rag over rag-time when all-in-rag is primarily Python; rag-time is Jupyter Notebook; Tags unique to all-in-rag: embedding, langchain, milvus, multimodal; - When you want a comprehensive resource that covers both the theoretical foundations and practical application of RAG.
When should I choose rag-time over all-in-rag?
Choose rag-time over all-in-rag when rag-time is primarily Jupyter Notebook; all-in-rag is Python; Requirements: Min 8 GB RAM; Tags unique to rag-time: generative-ai, hybrid-search, indexing, language-model; Also covers Model Training; When you need a detailed, week-by-week learning path specifically focused on the nuances of RAG techniques, from basics to advanced applications.
When should I avoid all-in-rag?
- Avoid if you are looking for a solution that only focuses on theoretical aspects without practical implementation guidance. - If your project does not require multi-modal support or is solely focused on text-based applications, more specialized tools might provide better optimization. - Not suitable if you're seeking quick prototyping or a light-weight framework; all-in-rag emphasizes comprehensive learning and production-ready practices.
When should I avoid rag-time?
If you prefer ad-hoc or self-directed learning without a structured timeline. Other tools may offer more flexible formats, which can be preferable if adhering to strict schedules is not ideal. When your focus is solely on either indexing or generation models and not the integration of both for RAG. In this case, specialized resources for just indexing or model training might suffice.
Is all-in-rag or rag-time more popular on GitHub?
all-in-rag has more GitHub stars (10,437 vs 893). Stars measure visibility, not whether either tool fits your constraints.
Are all-in-rag and rag-time open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to all-in-rag or rag-time?
GraphCanon lists graph-backed alternatives at all-in-rag alternatives and rag-time alternatives (all-in-rag markdown twin, rag-time 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, all-in-rag or rag-time?
all-in-rag: Active. rag-time: Dormant. 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 all-in-rag and rag-time?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: all-in-rag trust report; rag-time trust report.

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