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

# Lumos vs Awesome-LLM-RAG

*GraphCanon updated Aug 22, 2026*

## Verdict

Pick Lumos if lumos is a RAG LLM co-pilot that operates within Chrome and leverages local language models to support web browsing with automation; pick Awesome-LLM-RAG if awesome-LLM-RAG is a curated list specific to advanced retrieval augmented generation (RAG) techniques for Large Language Models.

[Lumos](https://github.com/andrewnguonly/Lumos) reports 1.5k GitHub stars, 112 forks, and 24 open issues, last pushed Jan 26, 2025. [Awesome-LLM-RAG](https://github.com/jxzhangjhu/Awesome-LLM-RAG) has 1.3k stars, 94 forks, and 13 open issues, last pushed Jul 22, 2026. Figures are from public GitHub metadata via [Lumos's repository](https://github.com/andrewnguonly/Lumos) and [Awesome-LLM-RAG's repository](https://github.com/jxzhangjhu/Awesome-LLM-RAG).

| | [Lumos](/tools/andrewnguonly-lumos.md) | [Awesome-LLM-RAG](/tools/jxzhangjhu-awesome-llm-rag.md) |
| --- | --- | --- |
| Tagline | A RAG LLM co-pilot for browsing the web | a curated list of advanced retrieval augmented generation (RAG) in Large Language Models |
| Stars | 1,516 | 1,343 |
| Forks | 112 | 94 |
| Open issues | 24 | 13 |
| Language | TypeScript | - |
| Adopt for | Lumos is a RAG LLM co-pilot that operates within Chrome and leverages local language models to support web browsing with automation. | Awesome-LLM-RAG is a curated list specific to advanced retrieval augmented generation (RAG) techniques for Large Language Models. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | - |
| Categories | Data & Retrieval, LLM Frameworks | Data & Retrieval, LLM Frameworks |

## Trust and health

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

| | [Lumos](/tools/andrewnguonly-lumos.md) | [Awesome-LLM-RAG](/tools/jxzhangjhu-awesome-llm-rag.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Steady (60%) |
| Days since push | 564d | 31d |
| Open issues (now) | 24 | 13 |
| Stars delta | +1 (30d) | +4 (30d) |
| Open issues delta | 0 (30d) | +4 (30d) |
| Full report | [trust report](/tools/andrewnguonly-lumos/trust.md) | [trust report](/tools/jxzhangjhu-awesome-llm-rag/trust.md) |

## Decision facts: Lumos

- **Requirements:** Ensure that your environment supports Docker to run the Ollama server as required for Lumos operation.; Your development workflow should be prepared to integrate a Chrome extension, considering you need to load unpacked extensions into Chrome.
- **Adopt for:** Lumos is a RAG LLM co-pilot that operates within Chrome and leverages local language models to support web browsing with automation.
- **License detail:** MIT

## 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.

## Choose when

### Choose Lumos if…

- Requirements: Ensure that your environment supports Docker to run the Ollama server as required for Lumos operation.; Your development workflow should be prepared to integrate a Chrome extension, considering you need to load unpacked extensions into Chrome..
- Tags unique to Lumos: chrome-extension, langchain, langchain-js, ollama.
- Use Lumos when your task involves heavy web navigation and requires interaction with the latest local machine learning models directly from a browser.

### Choose Awesome-LLM-RAG if…

- Tags unique to Awesome-LLM-RAG: embeddings, large language models, rag, rag-embeddings.
- 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 recently updated (last pushed Jul 22, 2026).

## When NOT to use Lumos

- Avoid using Lumos when your primary task does not involve browsing the web or requires a standalone application interface that does not need browser augmentation.
- Do not use this tool if you are looking for support in languages other than TypeScript, as it is specifically built around this language.

## 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.

## Common questions

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

Lumos: A RAG LLM co-pilot for browsing the web. Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language Models. See the comparison table for live GitHub stats and shared categories.

### When should I choose Lumos over Awesome-LLM-RAG?

Choose Lumos over Awesome-LLM-RAG when Requirements: Ensure that your environment supports Docker to run the Ollama server as required for Lumos operation.; Your development workflow should be prepared to integrate a Chrome extension, considering you need to load unpacked extensions into Chrome.; Tags unique to Lumos: chrome-extension, langchain, langchain-js, ollama; Use Lumos when your task involves heavy web navigation and requires interaction with the latest local machine learning models directly from a browser.

### When should I choose Awesome-LLM-RAG over Lumos?

Choose Awesome-LLM-RAG over Lumos when Tags unique to Awesome-LLM-RAG: embeddings, large language models, rag, rag-embeddings; 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 recently updated (last pushed Jul 22, 2026).

### When should I avoid Lumos?

Avoid using Lumos when your primary task does not involve browsing the web or requires a standalone application interface that does not need browser augmentation. Do not use this tool if you are looking for support in languages other than TypeScript, as it is specifically built around this language.

### 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.

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

Lumos has more GitHub stars (1,516 vs 1,343). Stars measure visibility, not whether either tool fits your constraints.

### Are Lumos and Awesome-LLM-RAG open source?

Yes - both are open-source projects on GitHub.

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

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

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

Lumos: Dormant. Awesome-LLM-RAG: 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 Lumos and Awesome-LLM-RAG?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=andrewnguonly-lumos`](/api/graphcanon/graph?tool=andrewnguonly-lumos)
- 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/_