Comparison
Lumos vs Awesome-LLM-RAG
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.
Markdown twin · Lumos alternatives · Awesome-LLM-RAG alternatives
GraphCanon updated Sep 20, 2026
9views this month
vs
Trust & integrity
| Signal | Lumos | Awesome-LLM-RAG |
|---|---|---|
| Maintenance | Dormant (601d since push) As of Sep 20, 2026 · github_public_v1 | Steady (60d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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
Stars
- Lumos
- 1.5k
- Awesome-LLM-RAG
- 1.3k
Forks
- Lumos
- 111
- Awesome-LLM-RAG
- 95
Open issues
- Lumos
- 24
- Awesome-LLM-RAG
- 15
Language
- Lumos
- TypeScript
- Awesome-LLM-RAG
- -
Adopt for
- Lumos
- 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
- Awesome-LLM-RAG is a curated list specific to advanced retrieval augmented generation (RAG) techniques for Large Language Models.
Persona
- Lumos
- -
- Awesome-LLM-RAG
- -
Runtime
- Lumos
- -
- Awesome-LLM-RAG
- -
License
- Lumos
- MIT
- Awesome-LLM-RAG
- -
Last pushed
- Lumos
- Jan 26, 2025
- Awesome-LLM-RAG
- Jul 22, 2026
Categories
- Lumos
- Data & Retrieval, LLM Frameworks
- Awesome-LLM-RAG
- Data & Retrieval, LLM Frameworks
Trust and health
Maintenance
- Lumos
- Dormant (18%)
- Awesome-LLM-RAG
- Steady (60%)
Days since push
- Lumos
- 601d
- Awesome-LLM-RAG
- 60d
Open issues (now)
- Lumos
- 24
- Awesome-LLM-RAG
- 15
Stars delta
- Lumos
- -3 (30d)
- Awesome-LLM-RAG
- +9 (30d)
Open issues delta
- Lumos
- 0 (30d)
- Awesome-LLM-RAG
- +6 (30d)
Full report
- Lumos
- Trust report
- Awesome-LLM-RAG
- Trust report
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (andrewnguonly/Lumos) · observed Sep 20, 2026
- GitHub forks (andrewnguonly/Lumos) · observed Sep 20, 2026
- Last push (andrewnguonly/Lumos) · observed Jan 26, 2025
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (jxzhangjhu/Awesome-LLM-RAG) · observed Sep 20, 2026
- GitHub forks (jxzhangjhu/Awesome-LLM-RAG) · observed Sep 20, 2026
- Last push (jxzhangjhu/Awesome-LLM-RAG) · observed Jul 22, 2026
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Lumos 1.5k · Awesome-LLM-RAG 1.3k (synced Sep 20, 2026).
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 (1,513 vs 1,348). 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 and Awesome-LLM-RAG alternatives (Lumos markdown twin, Awesome-LLM-RAG markdown twin), 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 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; Awesome-LLM-RAG trust report.