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Comparison

AutoRAG vs llm-app

Verdict

Pick AutoRAG when autoRAG is primarily Python; llm-app is Jupyter Notebook; pick llm-app when llm-app is primarily Jupyter Notebook; AutoRAG is Python.

Markdown twin · AutoRAG alternatives · llm-app alternatives

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AutoRAG logo

AutoRAG

Marker-Inc-Korea/AutoRAG

★ 4.9kpushed Jul 2, 2026
vs
llm-app logo

llm-app

pathwaycom/llm-app

★ 59kpushed Jul 5, 2026

Trust & integrity

SignalAutoRAGllm-app
Maintenance
Active (9d since push)
As of 1d · github_public_v1
Very active (5d since push)
As of 1d · github_public_v1
Provenance
Not a fork · Organization account
As of 1d · github_public_v1
Not a fork · Organization account
As of 1d · github_public_v1
Security (OSV)
No lockfile
As of 1d · none
No lockfile
As of 1d · none
AutoRAG trust reportllm-app trust reportTrust methodology

Tagline

AutoRAG
AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation
llm-app
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.

Stars

AutoRAG
4.9k
llm-app
59k

Forks

AutoRAG
407
llm-app
1.4k

Open issues

AutoRAG
171
llm-app
10

Language

AutoRAG
Python
llm-app
Jupyter Notebook

Adopt for

AutoRAG
-
llm-app
llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz

Persona

AutoRAG
-
llm-app
-

Runtime

AutoRAG
-
llm-app
-

License

AutoRAG
Apache-2.0
llm-app
MIT

Last pushed

AutoRAG
Jul 2, 2026
llm-app
Jul 5, 2026

Categories

AutoRAG
Data & Retrieval, LLM Frameworks, Vector Databases
llm-app
Data & Retrieval, LLM Frameworks, Vector Databases

Trust and health

Maintenance

AutoRAG
Active (82%)
llm-app
Very active (96%)

Days since push

AutoRAG
9d
llm-app
5d

Open issues (now)

AutoRAG
171
llm-app
10

Full report

AutoRAG
Trust report
llm-app
Trust report

Choose AutoRAG if…

  • AutoRAG is primarily Python; llm-app is Jupyter Notebook.
  • License: AutoRAG is Apache-2.0, llm-app is MIT.
  • Tags unique to AutoRAG: analysis, automl, benchmarking, document-parser.

When NOT to use AutoRAG

  • 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.
  • Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
GitHubDetails

Choose llm-app if…

  • llm-app is primarily Jupyter Notebook; AutoRAG is Python.
  • License: llm-app is MIT, AutoRAG is Apache-2.0.
  • Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others..
  • Tags unique to llm-app: chatbot, hugging-face, retrieval-augmented-generation, vector-database.
  • - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs.

When NOT to use llm-app

  • - You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app.
  • - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app.
GitHubDetails

Explore

AutoRAG trust report →llm-app 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: AutoRAG 4.9k · llm-app 59k (synced Jul 11, 2026).

Common questions

What is the difference between AutoRAG and llm-app?
AutoRAG: AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation. llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. See the comparison table for live GitHub stats and shared categories.
When should I choose AutoRAG over llm-app?
Choose AutoRAG over llm-app when AutoRAG is primarily Python; llm-app is Jupyter Notebook; License: AutoRAG is Apache-2.0, llm-app is MIT; Tags unique to AutoRAG: analysis, automl, benchmarking, document-parser.
When should I choose llm-app over AutoRAG?
Choose llm-app over AutoRAG when llm-app is primarily Jupyter Notebook; AutoRAG is Python; License: llm-app is MIT, AutoRAG is Apache-2.0; Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.; Tags unique to llm-app: chatbot, hugging-face, retrieval-augmented-generation, vector-database; - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs.
When should I avoid AutoRAG?
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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
When should I avoid llm-app?
- You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app. - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app.
Is AutoRAG or llm-app more popular on GitHub?
llm-app has more GitHub stars (59,068 vs 4,862). Stars measure visibility, not whether either tool fits your constraints.
Are AutoRAG and llm-app open source?
Yes - both are open-source projects on GitHub (AutoRAG: Apache-2.0, llm-app: MIT).
Where can I find alternatives to AutoRAG or llm-app?
GraphCanon lists graph-backed alternatives at AutoRAG alternatives and llm-app alternatives (AutoRAG markdown twin, llm-app 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, AutoRAG or llm-app?
AutoRAG: Active. llm-app: 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 AutoRAG and llm-app?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: AutoRAG trust report; llm-app trust report.