# VideoRAG: Retrieval-Augmented Generation for Long-Context Video Understanding

> VideoRAG is a KDD 2026 research system that applies retrieval-augmented generation to video files of any length, using a dual-channel architecture that combines graph-driven knowledge indexing with hierarchical context encoding. The repository also includes Vimo, a desktop application built on the VideoRAG framework that lets users query video collections through natural language.

**HKUDS/VideoRAG** — [KDD'2026] "VideoRAG: Chat with Your Videos"

- Repository: https://github.com/HKUDS/VideoRAG
- Website: https://arxiv.org/abs/2502.01549
- Stars: 3,388 · Forks: 476
- Language: Python
- License: NOASSERTION
- Published: 2026-09-10 · Updated: 2026-09-10 · Language: en
- Canonical page: https://hysenlabs.com/projects/hkuds-videorag

## What VideoRAG Addresses and Who It Serves

Most retrieval-augmented generation systems work over text documents. VideoRAG extends the RAG approach to video files, including videos that run for hours. The problem it solves is that conventional video understanding models have limited context windows and cannot answer questions about content that spans an entire lecture series, documentary, or entertainment archive.

The README identifies three audiences: general users who want to 'chat with videos' through the Vimo desktop app, power users who need to analyze multiple videos simultaneously and export insights, and researchers who want access to the VideoRAG algorithm, the LongerVideos benchmark, and the evaluation results. The paper was accepted to KDD 2026 and is available as arXiv preprint 2502.01549.

The stated capability is processing videos of any length, including content up to hundreds of hours, on a single RTX 3090 GPU with 24GB of VRAM.

## The Dual-Channel Architecture

VideoRAG uses a dual-channel design that the README describes as combining two mechanisms. The first is graph-driven knowledge indexing, which builds multi-modal knowledge graphs to represent structured video understanding. The second is hierarchical context encoding, which preserves spatiotemporal visual patterns across long sequences.

Two additional components support retrieval: adaptive retrieval, which uses dynamic retrieval mechanisms optimized for video content, and cross-video understanding, which models semantic relationships across multiple videos. The README describes these as the four technical highlights of the VideoRAG algorithm.

On the Video-MME long video benchmark, VideoRAG achieved 60.2% accuracy compared to MiniCPM-o with subtitles at 56.3% and without subtitles at 52.2%. MiniCPM-V with subtitles reached 56.3% and without subtitles 51.8%. The README notes that scores may show slight fluctuations across runs due to the instability of LLM generation.

The README describes the indexing approach as distilling long videos into 'concise knowledge representations' and aligning textual queries with visual and audio content through multi-modal retrieval.

## The LongerVideos Benchmark

The repository introduces LongerVideos, a benchmark for evaluating long-context video understanding that the authors created specifically for this work. It contains 164 videos with a total duration of approximately 134.6 hours, organized into 22 collections across three video types:

- Lectures: 12 collections, 135 videos, 376 queries, approximately 64.3 hours
- Documentaries: 5 collections, 12 videos, 114 queries, approximately 28.5 hours
- Entertainment: 5 collections, 17 videos, 112 queries, approximately 41.9 hours

The benchmark covers 602 question-answer queries in total. Evaluation scripts and reproduction instructions are in the VideoRAG-algorithm/reproduce subdirectory. The benchmark is designed to test performance on the kinds of long-form content where conventional video understanding models fail due to context length constraints.

## Vimo: The Desktop Interface Built on VideoRAG

Vimo is a desktop application that wraps the VideoRAG backend in a graphical interface. The README describes it as supporting drag-and-drop video upload, natural language questions, and multi-format support (MP4, MKV, AVI, and others). Cross-platform availability is listed as a goal, covering macOS, Windows, and Linux.

At the time of publication, the README states that a beta release for macOS Apple Silicon was being prepared, with Windows and Linux coming later. There are no GitHub releases in the repository; the beta was not yet published when this was written.

The technical architecture for Vimo is a Python backend that runs the VideoRAG server and an Electron frontend that provides the desktop UI. The overview in the README describes three steps: set up the Python backend environment, launch the Electron frontend, then query your videos. To get the code:

```bash
git clone https://github.com/HKUDS/VideoRAG
cd VideoRAG
# Backend setup is in VideoRAG-algorithm/
# Desktop app setup is in Vimo-desktop/
```

The VideoRAG-algorithm subdirectory documents Conda environment creation, model checkpoint download, dependency installation, and evaluation scripts for reproducing the LongerVideos results. The Vimo-desktop subdirectory holds the complete desktop application installation steps. The top-level README does not reproduce these commands directly.

## LangChain and LlamaIndex as a Comparison

LangChain and LlamaIndex are general-purpose RAG frameworks that retrieve from text document collections. The core difference from VideoRAG is the modality: both frameworks handle text natively but require additional processing to work with video content, and neither provides the temporal and spatiotemporal graph indexing that VideoRAG uses for long video sequences. A standard text RAG pipeline applied to video transcripts would lose the visual context entirely and produce answers based only on speech content.

VideoRAG's graph-driven approach is specifically designed to encode what happens visually over time, not only what is said. This makes it more suitable for questions like 'what was shown on the slide at minute 45 of this lecture' or 'which documentary segment showed the Arctic expedition,' which text-only retrieval cannot answer from transcripts alone.

LangChain and LlamaIndex are more broadly supported and have larger communities. VideoRAG is a research system with an academic paper rather than a production-ready library, which means less documentation, fewer integrations, and a higher setup burden.

## Maintenance Status and Licensing

The last push to the repository was on 2026-03-18. The license is listed as NOASSERTION in the repository metadata; the LICENSE file exists but its content is not detailed in the README.

The repository structure has two main subdirectories: VideoRAG-algorithm/ holds the research implementation and evaluation code, and Vimo-desktop/ holds the Electron desktop application. The cover.png at the root appears to be a project illustration.

The README includes a citation block for the paper: the title is 'VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos,' authored by Xubin Ren, Lingrui Xu, Long Xia, Shuaiqiang Wang, Dawei Yin, and Chao Huang, published on arXiv in 2025. The KDD 2026 tag in the repository description confirms the paper was accepted at the ACM SIGKDD conference.

For developers who want to contribute or integrate, the README mentions a Discord community and links to a YouTube demo video of Vimo in action.

## Conclusion

Researchers and developers working on long-context video understanding will find VideoRAG's LongerVideos benchmark and dual-channel architecture useful as a baseline and starting point. The Vimo desktop app is in beta for macOS Apple Silicon at the time of publication; Windows and Linux builds are described as coming soon. The last push was on 2026-03-18, so check the repository for any updates before building. Verify that the VideoRAG-algorithm setup completes correctly with your CUDA version and that the RTX 3090 (24GB) GPU requirement matches your hardware before committing to the research workflow.

## FAQ

### What is VideoRAG?

VideoRAG is a retrieval-augmented generation system for long-context video understanding. It uses a dual-channel architecture combining graph-driven knowledge indexing and hierarchical context encoding to process videos of any length and answer natural language questions about their content.

### What GPU does VideoRAG require?

The README states that VideoRAG can process hundreds of hours of video on a single RTX 3090 with 24GB of VRAM. The VideoRAG-algorithm subdirectory contains the full hardware and software setup instructions including Conda environment creation.

### Is Vimo available for Windows?

At the time the README was written, Vimo was preparing a beta release for macOS Apple Silicon. The README describes Windows and Linux versions as coming soon. There are no GitHub releases in the repository; check the repository directly for current availability.

## Sources

- [HKUDS/VideoRAG on GitHub](https://github.com/HKUDS/VideoRAG)
- [Issues](https://github.com/HKUDS/VideoRAG/issues)
- [Project website](https://arxiv.org/abs/2502.01549)
- [README](https://github.com/HKUDS/VideoRAG/blob/main/README.md)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/hkuds-videorag