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Comparison

all-in-rag vs AutoRAG

Verdict

Pick all-in-rag when tags unique to all-in-rag: neo4j, ai, python, embedding; pick AutoRAG when tags unique to AutoRAG: automl, evaluation, embeddings, document-parser.

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

GraphCanon updated today

all-in-rag logo

all-in-rag

datawhalechina/all-in-rag

★ 9.4kpushed Jun 5, 2026
vs
AutoRAG logo

AutoRAG

Marker-Inc-Korea/AutoRAG

★ 4.9kpushed Jul 2, 2026

Trust & integrity

Signalall-in-ragAutoRAG
Maintenance
Steady (36d since push)
As of today · github_public_v1
Active (9d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
No lockfile
As of today · none
No lockfile
As of today · none
all-in-rag trust reportAutoRAG trust reportTrust methodology

Tagline

all-in-rag
🔍 检索增强生成 (RAG) 技术全栈指南
AutoRAG
AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation

Stars

all-in-rag
9.4k
AutoRAG
4.9k

Forks

all-in-rag
4.7k
AutoRAG
407

Open issues

all-in-rag
17
AutoRAG
171

Language

all-in-rag
Python
AutoRAG
Python

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
AutoRAG
-

Persona

all-in-rag
-
AutoRAG
-

Runtime

all-in-rag
-
AutoRAG
-

License

all-in-rag
-
AutoRAG
Apache-2.0

Last pushed

all-in-rag
Jun 5, 2026
AutoRAG
Jul 2, 2026

Categories

all-in-rag
Data & Retrieval, LLM Frameworks
AutoRAG
Vector Databases, Data & Retrieval, LLM Frameworks

Trust and health

Maintenance

all-in-rag
Steady (60%)
AutoRAG
Active (82%)

Days since push

all-in-rag
36d
AutoRAG
9d

Open issues (now)

all-in-rag
17
AutoRAG
171

Full report

all-in-rag
Trust report
AutoRAG
Trust report

Shared compatibility

  • Python · all-in-rag: Python runtime · AutoRAG: Python runtime

Choose all-in-rag if…

  • Tags unique to all-in-rag: neo4j, ai, python, embedding.
  • - When you want a comprehensive resource that covers both the theoretical foundations and practical application of RAG.
  • More GitHub stars (9.4k vs 4.9k) - visibility, not fit.

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 AutoRAG if…

  • Tags unique to AutoRAG: automl, evaluation, embeddings, document-parser.
  • Also covers Vector Databases.
  • More recently updated (last pushed Jul 2, 2026).

When NOT to use AutoRAG

  • Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
  • Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
GitHubDetails

Explore

all-in-rag trust report →AutoRAG trust report →Data & Retrieval category →LLM Frameworks category →Vector Databases 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 9.4k · AutoRAG 4.9k (synced Jul 11, 2026).

Common questions

What is the difference between all-in-rag and AutoRAG?
all-in-rag: 🔍 检索增强生成 (RAG) 技术全栈指南. AutoRAG: AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation. See the comparison table for live GitHub stats and shared categories.
When should I choose all-in-rag over AutoRAG?
Choose all-in-rag over AutoRAG when Tags unique to all-in-rag: neo4j, ai, python, embedding; - When you want a comprehensive resource that covers both the theoretical foundations and practical application of RAG; More GitHub stars (9.4k vs 4.9k) - visibility, not fit.
When should I choose AutoRAG over all-in-rag?
Choose AutoRAG over all-in-rag when Tags unique to AutoRAG: automl, evaluation, embeddings, document-parser; Also covers Vector Databases; More recently updated (last pushed Jul 2, 2026).
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 AutoRAG?
Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Is all-in-rag or AutoRAG more popular on GitHub?
all-in-rag has more GitHub stars (9,417 vs 4,862). Stars measure visibility, not whether either tool fits your constraints.
Are all-in-rag and AutoRAG open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to all-in-rag or AutoRAG?
GraphCanon lists graph-backed alternatives at all-in-rag alternatives and AutoRAG alternatives (all-in-rag markdown twin, AutoRAG 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 AutoRAG?
all-in-rag: Steady. AutoRAG: 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 all-in-rag and AutoRAG?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: all-in-rag trust report; AutoRAG trust report.