---
title: "RAG-FiT vs llm-app"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/intellabs-rag-fit-vs-pathwaycom-llm-app"
tools: ["intellabs-rag-fit", "pathwaycom-llm-app"]
---

# RAG-FiT vs llm-app

*GraphCanon updated Jul 11, 2026*

## Verdict

Pick RAG-FiT when rAG-FiT is primarily Python; llm-app is Jupyter Notebook; pick llm-app when llm-app is primarily Jupyter Notebook; RAG-FiT is Python.

[RAG-FiT](https://intellabs.github.io/RAG-FiT/) reports 772 GitHub stars, 61 forks, and 1 open issues, last pushed Jun 8, 2026. [llm-app](https://pathway.com/developers/templates/) has 59k stars, 1.4k forks, and 10 open issues, last pushed Jul 5, 2026. Figures are from public GitHub metadata via [RAG-FiT's repository](https://github.com/IntelLabs/RAG-FiT) and [llm-app's repository](https://github.com/pathwaycom/llm-app).

| | [RAG-FiT](/tools/intellabs-rag-fit.md) | [llm-app](/tools/pathwaycom-llm-app.md) |
| --- | --- | --- |
| Tagline | Framework for enhancing LLMs for RAG tasks using fine-tuning. | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. |
| Stars | 772 | 59,068 |
| Forks | 61 | 1,432 |
| Open issues | 1 | 10 |
| Language | Python | Jupyter Notebook |
| 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 | Apache-2.0 | MIT |
| Categories | LLM Frameworks, Data & Retrieval, Evaluation & Observability | LLM Frameworks, Vector Databases, Data & Retrieval |

## Trust and health

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

| | [RAG-FiT](/tools/intellabs-rag-fit.md) | [llm-app](/tools/pathwaycom-llm-app.md) |
| --- | --- | --- |
| Maintenance | Steady (60%) | Very active (96%) |
| Days since push | 32d | 5d |
| Open issues (now) | 1 | 10 |
| Full report | [trust report](/tools/intellabs-rag-fit/trust.md) | [trust report](/tools/pathwaycom-llm-app/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 RAG-FiT if…

- RAG-FiT is primarily Python; llm-app is Jupyter Notebook.
- License: RAG-FiT is Apache-2.0, llm-app is MIT.
- Tags unique to RAG-FiT: evaluation, fine-tuning, nlp, question-answering.
- Also covers Evaluation & Observability.

### Choose llm-app if…

- llm-app is primarily Jupyter Notebook; RAG-FiT is Python.
- License: llm-app is MIT, RAG-FiT 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: vector-database, hugging-face, retrieval-augmented-generation, chatbot.
- Also covers Vector Databases.
- - 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 RAG-FiT

- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
- Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.

## 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.

## Common questions

### What is the difference between RAG-FiT and llm-app?

RAG-FiT: Framework for enhancing LLMs for RAG tasks using fine-tuning.. 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 RAG-FiT over llm-app?

Choose RAG-FiT over llm-app when RAG-FiT is primarily Python; llm-app is Jupyter Notebook; License: RAG-FiT is Apache-2.0, llm-app is MIT; Tags unique to RAG-FiT: evaluation, fine-tuning, nlp, question-answering; Also covers Evaluation & Observability.

### When should I choose llm-app over RAG-FiT?

Choose llm-app over RAG-FiT when llm-app is primarily Jupyter Notebook; RAG-FiT is Python; License: llm-app is MIT, RAG-FiT 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: vector-database, hugging-face, retrieval-augmented-generation, chatbot; Also covers Vector Databases; - 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 RAG-FiT?

LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.

### 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 RAG-FiT or llm-app more popular on GitHub?

llm-app has more GitHub stars (59,068 vs 772). Stars measure visibility, not whether either tool fits your constraints.

### Are RAG-FiT and llm-app open source?

Yes - both are open-source projects on GitHub (RAG-FiT: Apache-2.0, llm-app: MIT).

### Where can I find alternatives to RAG-FiT or llm-app?

GraphCanon lists graph-backed alternatives at [RAG-FiT alternatives](/tools/intellabs-rag-fit/alternatives) and [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) ([RAG-FiT markdown twin](/tools/intellabs-rag-fit/alternatives.md), [llm-app markdown twin](/tools/pathwaycom-llm-app/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/intellabs-rag-fit-vs-pathwaycom-llm-app.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, RAG-FiT or llm-app?

RAG-FiT: Steady. 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 RAG-FiT and llm-app?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [RAG-FiT trust report](/tools/intellabs-rag-fit/trust); [llm-app trust report](/tools/pathwaycom-llm-app/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=intellabs-rag-fit`](/api/graphcanon/graph?tool=intellabs-rag-fit)
- 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/_