Comparison
VideoRAG vs Awesome-LLM-RAG
Verdict
Pick VideoRAG if videoRAG is an AI desktop application that allows users to interact with video content through natural language queries, catering to enthusiasts and professionals alike; 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 · VideoRAG alternatives · Awesome-LLM-RAG alternatives
GraphCanon updated 3d
vs
Trust & integrity
| Signal | VideoRAG | Awesome-LLM-RAG |
|---|---|---|
| Maintenance | Slowing (152d since push) As of 1w · github_public_v1 | Steady (31d since push) As of 3d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1w · github_public_v1 | Not a fork · Personal account As of 3d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- VideoRAG
- Chat with Your Videos
- Awesome-LLM-RAG
- a curated list of advanced retrieval augmented generation (RAG) in Large Language Models
Stars
- VideoRAG
- 3.3k
- Awesome-LLM-RAG
- 1.3k
Forks
- VideoRAG
- 467
- Awesome-LLM-RAG
- 94
Open issues
- VideoRAG
- 21
- Awesome-LLM-RAG
- 13
Language
- VideoRAG
- Python
- Awesome-LLM-RAG
- -
Adopt for
- VideoRAG
- VideoRAG is an AI desktop application that allows users to interact with video content through natural language queries, catering to enthusiasts and professionals alike.
- Awesome-LLM-RAG
- Awesome-LLM-RAG is a curated list specific to advanced retrieval augmented generation (RAG) techniques for Large Language Models.
Persona
- VideoRAG
- -
- Awesome-LLM-RAG
- -
Runtime
- VideoRAG
- -
- Awesome-LLM-RAG
- -
License
- VideoRAG
- Other
- Awesome-LLM-RAG
- -
Last pushed
- VideoRAG
- Mar 18, 2026
- Awesome-LLM-RAG
- Jul 22, 2026
Categories
- VideoRAG
- Data & Retrieval, Model Training
- Awesome-LLM-RAG
- Data & Retrieval, LLM Frameworks
Trust and health
Maintenance
- VideoRAG
- Slowing (36%)
- Awesome-LLM-RAG
- Steady (60%)
Days since push
- VideoRAG
- 152d
- Awesome-LLM-RAG
- 31d
Open issues (now)
- VideoRAG
- 21
- Awesome-LLM-RAG
- 13
Stars delta
- VideoRAG
- +104 (30d)
- Awesome-LLM-RAG
- +4 (30d)
Open issues delta
- VideoRAG
- +1 (30d)
- Awesome-LLM-RAG
- +4 (30d)
Owner type
- VideoRAG
- Organization
- Awesome-LLM-RAG
- User
Full report
- VideoRAG
- Trust report
- Awesome-LLM-RAG
- Trust report
Choose VideoRAG if…
- Tags unique to VideoRAG: llms, long-video-understanding, multi-modal-llms.
- Also covers Model Training.
- You have long videos (up to hundreds of hours) and need precise analysis or summaries that require deep understanding of both audio and visual components.
When NOT to use VideoRAG
- You are working with short clips (under one minute) where traditional search methods might be quicker or more straightforward.
- If your needs are strictly for audio transcription or text-based retrieval, VideoRAG's features may offer more complexity than necessary.
- Your video content has strict privacy concerns, as using desktop applications can sometimes pose security and confidentiality risks depending on the user’s context.
Choose Awesome-LLM-RAG if…
- Tags unique to Awesome-LLM-RAG: embeddings, llm, rag-embeddings, retrieval-information.
- Also covers LLM Frameworks.
- 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.
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 (HKUDS/VideoRAG) · observed Aug 18, 2026
- GitHub forks (HKUDS/VideoRAG) · observed Aug 18, 2026
- Last push (HKUDS/VideoRAG) · observed Mar 18, 2026
- License file (Other) · observed Aug 18, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (jxzhangjhu/Awesome-LLM-RAG) · observed Aug 22, 2026
- GitHub forks (jxzhangjhu/Awesome-LLM-RAG) · observed Aug 22, 2026
- Last push (jxzhangjhu/Awesome-LLM-RAG) · observed Jul 22, 2026
- License file (unknown) · observed Aug 22, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: VideoRAG 3.3k · Awesome-LLM-RAG 1.3k (synced Aug 18, 2026).
Common questions
- What is the difference between VideoRAG and Awesome-LLM-RAG?
- VideoRAG: Chat with Your Videos. 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 VideoRAG over Awesome-LLM-RAG?
- Choose VideoRAG over Awesome-LLM-RAG when Tags unique to VideoRAG: llms, long-video-understanding, multi-modal-llms; Also covers Model Training; You have long videos (up to hundreds of hours) and need precise analysis or summaries that require deep understanding of both audio and visual components.
- When should I choose Awesome-LLM-RAG over VideoRAG?
- Choose Awesome-LLM-RAG over VideoRAG when Tags unique to Awesome-LLM-RAG: embeddings, llm, rag-embeddings, retrieval-information; Also covers LLM Frameworks; 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.
- When should I avoid VideoRAG?
- You are working with short clips (under one minute) where traditional search methods might be quicker or more straightforward. If your needs are strictly for audio transcription or text-based retrieval, VideoRAG's features may offer more complexity than necessary. Your video content has strict privacy concerns, as using desktop applications can sometimes pose security and confidentiality risks depending on the user’s context.
- 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 VideoRAG or Awesome-LLM-RAG more popular on GitHub?
- VideoRAG has more GitHub stars (3,288 vs 1,343). Stars measure visibility, not whether either tool fits your constraints.
- Are VideoRAG and Awesome-LLM-RAG open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to VideoRAG or Awesome-LLM-RAG?
- GraphCanon lists graph-backed alternatives at VideoRAG alternatives and Awesome-LLM-RAG alternatives (VideoRAG 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, VideoRAG or Awesome-LLM-RAG?
- VideoRAG: Slowing. 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 VideoRAG and Awesome-LLM-RAG?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: VideoRAG trust report; Awesome-LLM-RAG trust report.