---
title: "Awesome-LLM-RAG vs rag-time"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/jxzhangjhu-awesome-llm-rag-vs-microsoft-rag-time"
tools: ["jxzhangjhu-awesome-llm-rag", "microsoft-rag-time"]
---

# Awesome-LLM-RAG vs rag-time

*GraphCanon updated Aug 23, 2026*

## Verdict

Pick Awesome-LLM-RAG if awesome-LLM-RAG is a curated list specific to advanced retrieval augmented generation (RAG) techniques for Large Language Models; pick rag-time if rAG Time is tailored for those looking to systematically learn and apply Retrieval-Augmented Generation techniques in a structured 5-week program.

[Awesome-LLM-RAG](https://github.com/jxzhangjhu/Awesome-LLM-RAG) reports 1.3k GitHub stars, 94 forks, and 13 open issues, last pushed Jul 22, 2026. [rag-time](https://github.com/microsoft/rag-time) has 898 stars, 320 forks, and 4 open issues, last pushed Jun 17, 2025. Figures are from public GitHub metadata via [Awesome-LLM-RAG's repository](https://github.com/jxzhangjhu/Awesome-LLM-RAG) and [rag-time's repository](https://github.com/microsoft/rag-time).

| | [Awesome-LLM-RAG](/tools/jxzhangjhu-awesome-llm-rag.md) | [rag-time](/tools/microsoft-rag-time.md) |
| --- | --- | --- |
| Tagline | a curated list of advanced retrieval augmented generation (RAG) in Large Language Models | RAG Time: A 5-week Learning Journey to Mastering RAG |
| Stars | 1,343 | 898 |
| Forks | 94 | 320 |
| Open issues | 13 | 4 |
| Language | - | Jupyter Notebook |
| Adopt for | Awesome-LLM-RAG is a curated list specific to advanced retrieval augmented generation (RAG) techniques for Large Language Models. | RAG Time is tailored for those looking to systematically learn and apply Retrieval-Augmented Generation techniques in a structured 5-week program. |
| Persona | - | - |
| Runtime | - | - |
| License | - | The MIT License provides freedom to use, copy, modify and distribute the software provided that copyright and license information are retained. |
| Categories | Data & Retrieval, LLM Frameworks | Data & Retrieval, LLM Frameworks, Model Training |

## Trust and health

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

| | [Awesome-LLM-RAG](/tools/jxzhangjhu-awesome-llm-rag.md) | [rag-time](/tools/microsoft-rag-time.md) |
| --- | --- | --- |
| Maintenance | Steady (60%) | Dormant (18%) |
| Days since push | 31d | 431d |
| Open issues (now) | 13 | 4 |
| Stars delta | +4 (30d) | +5 (30d) |
| Open issues delta | +4 (30d) | 0 (30d) |
| Owner type | User | Organization |
| Full report | [trust report](/tools/jxzhangjhu-awesome-llm-rag/trust.md) | [trust report](/tools/microsoft-rag-time/trust.md) |

## Decision facts: Awesome-LLM-RAG

- **Adopt for:** Awesome-LLM-RAG is a curated list specific to advanced retrieval augmented generation (RAG) techniques for Large Language Models.

## Decision facts: rag-time

- **Requirements:** Min 8 GB RAM
- **Adopt for:** RAG Time is tailored for those looking to systematically learn and apply Retrieval-Augmented Generation techniques in a structured 5-week program.
- **License detail:** The MIT License provides freedom to use, copy, modify and distribute the software provided that copyright and license information are retained.

## Choose when

### Choose Awesome-LLM-RAG if…

- Tags unique to Awesome-LLM-RAG: embeddings, large language models, rag-embeddings, retrieval-augmented-generation.
- When you are focusing on the detailed implementation and utilization of RAG in large language models, as Awesome-LLM-RAG provides a deep dive into advanced RAG approaches.
- More GitHub stars (1.3k vs 898) - visibility, not fit.

### Choose rag-time if…

- Requirements: Min 8 GB RAM.
- Tags unique to rag-time: ai, generative-ai, hybrid-search, indexing.
- Also covers Model Training.
- When you need a detailed, week-by-week learning path specifically focused on the nuances of RAG techniques, from basics to advanced applications.

## When NOT to use Awesome-LLM-RAG

- If you are looking for introductory material on LLM frameworks broadly; Awesome-LLM-RAG does not cover basics of large language models but rather focuses on advanced topics.
- Not recommended if your interest is in broad categories like general vector databases or data retrieval without a focus on RAG within LLMs, as the content is highly specialized.

## When NOT to use rag-time

- If you prefer ad-hoc or self-directed learning without a structured timeline. Other tools may offer more flexible formats, which can be preferable if adhering to strict schedules is not ideal.
- When your focus is solely on either indexing or generation models and not the integration of both for RAG. In this case, specialized resources for just indexing or model training might suffice.

## Common questions

### What is the difference between Awesome-LLM-RAG and rag-time?

Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language Models. rag-time: RAG Time: A 5-week Learning Journey to Mastering RAG. See the comparison table for live GitHub stats and shared categories.

### When should I choose Awesome-LLM-RAG over rag-time?

Choose Awesome-LLM-RAG over rag-time when Tags unique to Awesome-LLM-RAG: embeddings, large language models, rag-embeddings, retrieval-augmented-generation; When you are focusing on the detailed implementation and utilization of RAG in large language models, as Awesome-LLM-RAG provides a deep dive into advanced RAG approaches; More GitHub stars (1.3k vs 898) - visibility, not fit.

### When should I choose rag-time over Awesome-LLM-RAG?

Choose rag-time over Awesome-LLM-RAG when Requirements: Min 8 GB RAM; Tags unique to rag-time: ai, generative-ai, hybrid-search, indexing; Also covers Model Training; When you need a detailed, week-by-week learning path specifically focused on the nuances of RAG techniques, from basics to advanced applications.

### When should I avoid Awesome-LLM-RAG?

If you are looking for introductory material on LLM frameworks broadly; Awesome-LLM-RAG does not cover basics of large language models but rather focuses on advanced topics. Not recommended if your interest is in broad categories like general vector databases or data retrieval without a focus on RAG within LLMs, as the content is highly specialized.

### When should I avoid rag-time?

If you prefer ad-hoc or self-directed learning without a structured timeline. Other tools may offer more flexible formats, which can be preferable if adhering to strict schedules is not ideal. When your focus is solely on either indexing or generation models and not the integration of both for RAG. In this case, specialized resources for just indexing or model training might suffice.

### Is Awesome-LLM-RAG or rag-time more popular on GitHub?

Awesome-LLM-RAG has more GitHub stars (1,343 vs 898). Stars measure visibility, not whether either tool fits your constraints.

### Are Awesome-LLM-RAG and rag-time open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to Awesome-LLM-RAG or rag-time?

GraphCanon lists graph-backed alternatives at [Awesome-LLM-RAG alternatives](/tools/jxzhangjhu-awesome-llm-rag/alternatives) and [rag-time alternatives](/tools/microsoft-rag-time/alternatives) ([Awesome-LLM-RAG markdown twin](/tools/jxzhangjhu-awesome-llm-rag/alternatives.md), [rag-time markdown twin](/tools/microsoft-rag-time/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/jxzhangjhu-awesome-llm-rag-vs-microsoft-rag-time.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Awesome-LLM-RAG or rag-time?

Awesome-LLM-RAG: Steady. rag-time: Dormant. 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 Awesome-LLM-RAG and rag-time?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=jxzhangjhu-awesome-llm-rag`](/api/graphcanon/graph?tool=jxzhangjhu-awesome-llm-rag)
- 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/_