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

# rag-time vs llm-app

*GraphCanon updated Jul 11, 2026*

## Verdict

Pick rag-time when tags unique to rag-time: ai, binary-quantization, generative-ai, gpt; pick llm-app when requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others..

[rag-time](https://github.com/microsoft/rag-time) reports 894 GitHub stars, 316 forks, and 4 open issues, last pushed Jun 17, 2025. [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-time's repository](https://github.com/microsoft/rag-time) and [llm-app's repository](https://github.com/pathwaycom/llm-app).

| | [rag-time](/tools/microsoft-rag-time.md) | [llm-app](/tools/pathwaycom-llm-app.md) |
| --- | --- | --- |
| Tagline | RAG Time: A 5-week Learning Journey to Mastering RAG | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. |
| Stars | 894 | 59,068 |
| Forks | 316 | 1,432 |
| Open issues | 4 | 10 |
| Language | Jupyter Notebook | 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 | MIT | MIT |
| Categories | LLM Frameworks, Vector Databases, Inference & Serving | LLM Frameworks, Data & Retrieval, Vector Databases |

## Trust and health

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

| | [rag-time](/tools/microsoft-rag-time.md) | [llm-app](/tools/pathwaycom-llm-app.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Very active (96%) |
| Days since push | 388d | 5d |
| Open issues (now) | 4 | 10 |
| Full report | [trust report](/tools/microsoft-rag-time/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-time if…

- Tags unique to rag-time: ai, binary-quantization, generative-ai, gpt.
- Also covers Inference & Serving.
- Leaner open-issue backlog (4).

### Choose llm-app if…

- 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, llm, hugging-face, retrieval-augmented-generation.
- Also covers Data & Retrieval.
- - 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-time

- Last GitHub push was 389 days ago (dormant maintenance, Jun 17, 2025). Validate activity before betting a new project on rag-time.
- 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.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.

## 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-time and llm-app?

rag-time: RAG Time: A 5-week Learning Journey to Mastering RAG. 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-time over llm-app?

Choose rag-time over llm-app when Tags unique to rag-time: ai, binary-quantization, generative-ai, gpt; Also covers Inference & Serving; Leaner open-issue backlog (4).

### When should I choose llm-app over rag-time?

Choose llm-app over rag-time when 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, llm, hugging-face, retrieval-augmented-generation; Also covers Data & Retrieval; - 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-time?

Last GitHub push was 389 days ago (dormant maintenance, Jun 17, 2025). Validate activity before betting a new project on rag-time. 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. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.

### 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-time or llm-app more popular on GitHub?

llm-app has more GitHub stars (59,068 vs 894). Stars measure visibility, not whether either tool fits your constraints.

### Are rag-time and llm-app open source?

Yes - both are open-source projects on GitHub (rag-time: MIT, llm-app: MIT).

### Where can I find alternatives to rag-time or llm-app?

GraphCanon lists graph-backed alternatives at [rag-time alternatives](/tools/microsoft-rag-time/alternatives) and [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) ([rag-time markdown twin](/tools/microsoft-rag-time/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/microsoft-rag-time-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-time or llm-app?

rag-time: Dormant. 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-time and llm-app?

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

---

**Machine-readable endpoints**

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