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Comparison

llm-app vs rag-fusion

Verdict

Pick llm-app when llm-app is primarily Jupyter Notebook; rag-fusion is Python; pick rag-fusion when rag-fusion is primarily Python; llm-app is Jupyter Notebook.

Markdown twin · llm-app alternatives · rag-fusion alternatives

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llm-app logo

llm-app

pathwaycom/llm-app

★ 59kpushed Jul 5, 2026
vs
rag-fusion logo

rag-fusion

Raudaschl/rag-fusion

★ 946pushed Apr 26, 2026

Trust & integrity

Signalllm-apprag-fusion
Maintenance
Very active (5d since push)
As of 1d · github_public_v1
Steady (75d since push)
As of 1d · github_public_v1
Provenance
Not a fork · Organization account
As of 1d · github_public_v1
Not a fork · Personal account
As of 1d · github_public_v1
Security (OSV)
No lockfile
As of 1d · none
No lockfile
As of 1d · none
llm-app trust reportrag-fusion trust reportTrust methodology

Tagline

llm-app
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.
rag-fusion
RAG-Fusion: multi-query generation + Reciprocal Rank Fusion for better retrieval-augmented generation. Includes evaluation harness with NFCorpus/BEIR.

Stars

llm-app
59k
rag-fusion
946

Forks

llm-app
1.4k
rag-fusion
113

Open issues

llm-app
10
rag-fusion
0

Language

llm-app
Jupyter Notebook
rag-fusion
Python

Adopt for

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
rag-fusion
-

Persona

llm-app
-
rag-fusion
-

Runtime

llm-app
-
rag-fusion
-

License

llm-app
MIT
rag-fusion
MIT

Last pushed

llm-app
Jul 5, 2026
rag-fusion
Apr 26, 2026

Categories

llm-app
Data & Retrieval, LLM Frameworks, Vector Databases
rag-fusion
Data & Retrieval, LLM Frameworks, Vector Databases

Trust and health

Maintenance

llm-app
Very active (96%)
rag-fusion
Steady (60%)

Days since push

llm-app
5d
rag-fusion
75d

Open issues (now)

llm-app
10
rag-fusion
0

Owner type

llm-app
Organization
rag-fusion
User

Full report

llm-app
Trust report
rag-fusion
Trust report

Choose llm-app if…

  • llm-app is primarily Jupyter Notebook; rag-fusion is Python.
  • 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, llm, 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

Choose rag-fusion if…

  • rag-fusion is primarily Python; llm-app is Jupyter Notebook.
  • Tags unique to rag-fusion: chromadb, information-retrieval, openai, python.
  • Leaner open-issue backlog (0).

When NOT to use rag-fusion

  • 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

Explore

llm-app trust report →rag-fusion 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: llm-app 59k · rag-fusion 946 (synced Jul 11, 2026).

Common questions

What is the difference between llm-app and rag-fusion?
llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. rag-fusion: RAG-Fusion: multi-query generation + Reciprocal Rank Fusion for better retrieval-augmented generation. Includes evaluation harness with NFCorpus/BEIR.. See the comparison table for live GitHub stats and shared categories.
When should I choose llm-app over rag-fusion?
Choose llm-app over rag-fusion when llm-app is primarily Jupyter Notebook; rag-fusion is Python; 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, llm, 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 choose rag-fusion over llm-app?
Choose rag-fusion over llm-app when rag-fusion is primarily Python; llm-app is Jupyter Notebook; Tags unique to rag-fusion: chromadb, information-retrieval, openai, python; Leaner open-issue backlog (0).
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.
When should I avoid rag-fusion?
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.
Is llm-app or rag-fusion more popular on GitHub?
llm-app has more GitHub stars (59,068 vs 946). Stars measure visibility, not whether either tool fits your constraints.
Are llm-app and rag-fusion open source?
Yes - both are open-source projects on GitHub (llm-app: MIT, rag-fusion: MIT).
Where can I find alternatives to llm-app or rag-fusion?
GraphCanon lists graph-backed alternatives at llm-app alternatives and rag-fusion alternatives (llm-app markdown twin, rag-fusion 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, llm-app or rag-fusion?
llm-app: Very active. rag-fusion: Steady. 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 llm-app and rag-fusion?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llm-app trust report; rag-fusion trust report.