---
title: "llm-app vs rag-fusion"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/pathwaycom-llm-app-vs-raudaschl-rag-fusion"
tools: ["pathwaycom-llm-app", "raudaschl-rag-fusion"]
---

# llm-app vs rag-fusion

*GraphCanon updated Jul 12, 2026*

## 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.

[llm-app](https://pathway.com/developers/templates/) reports 59k GitHub stars, 1.4k forks, and 10 open issues, last pushed Jul 5, 2026. [rag-fusion](https://github.com/Raudaschl/rag-fusion) has 946 stars, 113 forks, and 0 open issues, last pushed Apr 26, 2026. Figures are from public GitHub metadata via [llm-app's repository](https://github.com/pathwaycom/llm-app) and [rag-fusion's repository](https://github.com/Raudaschl/rag-fusion).

| | [llm-app](/tools/pathwaycom-llm-app.md) | [rag-fusion](/tools/raudaschl-rag-fusion.md) |
| --- | --- | --- |
| Tagline | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | RAG-Fusion: multi-query generation + Reciprocal Rank Fusion for better retrieval-augmented generation. Includes evaluation harness with NFCorpus/BEIR. |
| Stars | 59,068 | 946 |
| Forks | 1,432 | 113 |
| Open issues | 10 | 0 |
| Language | Jupyter Notebook | Python |
| Adopt for | 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 | - | - |
| Runtime | - | - |
| License | MIT | MIT |
| Categories | Data & Retrieval, LLM Frameworks, Vector Databases | Data & Retrieval, LLM Frameworks, Vector Databases |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [llm-app](/tools/pathwaycom-llm-app.md) | [rag-fusion](/tools/raudaschl-rag-fusion.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Steady (60%) |
| Days since push | 5d | 75d |
| Open issues (now) | 10 | 0 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/pathwaycom-llm-app/trust.md) | [trust report](/tools/raudaschl-rag-fusion/trust.md) |

## Decision facts: llm-app

- **Requirements:** Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.
- **Adopt for:** 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

## Choose when

### 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.

### 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 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 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.

## 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](/tools/pathwaycom-llm-app/alternatives) and [rag-fusion alternatives](/tools/raudaschl-rag-fusion/alternatives) ([llm-app markdown twin](/tools/pathwaycom-llm-app/alternatives.md), [rag-fusion markdown twin](/tools/raudaschl-rag-fusion/alternatives.md)), 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](/compare/pathwaycom-llm-app-vs-raudaschl-rag-fusion.md) 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](/tools/pathwaycom-llm-app/trust); [rag-fusion trust report](/tools/raudaschl-rag-fusion/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=pathwaycom-llm-app`](/api/graphcanon/graph?tool=pathwaycom-llm-app)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_