Ask-Anything (VideoChat Family): A Repository of Video Chat Models, Not a Single Tool
[CVPR2024 Highlight][VideoChatGPT] ChatGPT with video understanding! And many more supported LMs such as miniGPT4, StableLM, and MOSS.
At a glance
- What is it?
- OpenGVLab/Ask-Anything is an MIT-licensed Python monorepo holding the VideoChat model line, from the 2023 ChatGPT-based prototypes to VideoChat2 and MVBench. It is a research distribution with several incompatible entry points, not one installable product.
- Who is it for?
- Adopt Ask-Anything if you need the VideoChat2 weights, the MVBench benchmark, or the 2M instruction samples for video tuning, and you are comfortable working inside a research repository where each model has its own directory and its own setup. Do not adopt it if you want a single supported product with one install command, or if you need a maintained chat application rather than a model release.
- Can I use it commercially?
- Yes. MIT is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 61 days ago.
- What is it written in?
- Mainly Python, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
What Ask-Anything actually is, and who the repository is for
The name suggests a product. The repository is closer to an archive of releases. The README describes it as the VideoChat family, and the top level is a set of sibling directories: video_chat, video_chat2, video_chat_text, video_chat_with_ChatGPT, video_chat_with_MOSS, video_chat_with_StableLM, and video_miniGPT4. Each one is a different model or a different way of wiring a language model to video input.
The intended audience is research engineers and graduate students who want to run or fine-tune a video question answering model, or who want the MVBench benchmark and the 2M instruction samples the README says are released for tuning. It is not aimed at application developers looking for a hosted API or a drop-in library.
The practical consequence is that there is no single thing to install. Choosing Ask-Anything means first choosing a subdirectory, and the README's Getting Started section does exactly that, listing End2End, ChatGPT, and the other variants as separate build paths rather than as options of one entry point.
How the VideoChat variants differ in mechanism
The repository splits along a real architectural line, and the README states it plainly. The early variants are described as explicit communication with an external language model: video_chat_with_ChatGPT talks to ChatGPT and is marked as sensitive with time, while video_chat_text/video_miniGPT4 is described as implicit communication with Vicuna and not sensitive with time. That distinction matters because the ChatGPT route depends on a hosted model that can change under you, and the Vicuna route depends on weights you host yourself.
VideoChat2, in the video_chat2 directory, is the later and structurally different line. The README says it is built on UMT and Vicuna-v0, and that it ships with MVBench, a benchmark for video understanding, plus 2M instruction samples. So the mechanism shifts from prompting a frozen external model to fine-tuning a multimodal model on released instruction data.
The repository also records a vllm branch, described in the 2024/06/25 update as speeding up VideoChat2 inference. That is a branch, not a flag in the main tree, which is worth knowing before you go looking for a serving option in the default checkout.
Installing Ask-Anything: pick a subdirectory first
The README does not present one install command for the whole repository. Its Getting Started section links out to per-variant instructions, and the top level contains no setup.py or pyproject.toml among the entries listed, so the install lives inside each subdirectory. The README does not give a clone command either, so the first step is simply to obtain the repository and look at what is actually there. The listing should show the directories named in the README: video_chat, video_chat2, video_chat_text, video_chat_with_ChatGPT, video_chat_with_MOSS, video_chat_with_StableLM, video_miniGPT4, plus example, LICENSE, README.md and README_cn.md. From there you descend into the variant you want, because each carries its own environment and weight instructions.
The repository also ships two sample clips, so you do not need to supply your own video for a first attempt. The example directory contains hitting_baseball.mp4 and yoga.mp4. For the VideoChat2 line specifically, the README points to demo_mistral.ipynb and demo_mistral_hd.ipynb under video_chat2/demo as the notebooks used for testing EgoSchema and Video-MME, which makes them the closest thing to an official starting point for that model. The README does not give a pip requirements line for the repository as a whole, so treat any environment file as belonging to the subdirectory you chose.
Where Ask-Anything is the wrong tool
The clearest limitation is that this is a moving research repository with several generations living side by side. The README itself carries a note that an updated version is being worked on, and the 2026/07/17 update announces VideoChat3 in a different repository, MCG-NJU/VideoChat3, with weights, code, training recipes and datasets released there. Anyone arriving at Ask-Anything expecting the newest VideoChat model will find the announcement pointing somewhere else.
A second issue is the time sensitivity the README attaches to the ChatGPT variant. A pipeline that depends on a hosted model can break when that model changes, and the repository labels that path as sensitive with time rather than hiding it.
A third is the absence of a unified interface. The README does not document a shared Python API across video_chat, video_chat2 and video_chat_text, and the top level shows no packaging metadata. If your requirement is a stable import with a version number, this repository does not offer one. Finally, the README does not document rollback or a migration path between variants, so upgrading from VideoChat1 to VideoChat2 is a rewrite of your integration, not a version bump.
The alternative to reach for instead
If you want the current generation of this model line, the README's own update points to VideoChat3 at MCG-NJU/VideoChat3, described as a fully open 4B video MLLM for general, long-form and streaming video understanding, with weights, code, training recipes and complete datasets released. The difference in approach is not cosmetic: VideoChat3 is presented as one model with one release, whereas Ask-Anything is a collection in which VideoChat1, VideoChat2 and the ChatGPT, MOSS, StableLM and MiniGPT-4 variants each have their own directory and their own assumptions.
A second alternative lives inside the same organisation. The 2025/01/18 update announces videochat-flash and videochat-tpo as separate repositories, with videochat-flash described as improving code usability by building on LLaVA and others. If your problem is long video, that update is where the repository directs you.
The trade-off is data. Ask-Anything is where the 2M instruction samples and MVBench are documented, so if you need the benchmark or the tuning data rather than the newest weights, staying here is the right call even though the model releases have moved on.
Maintenance, licence and what it costs to keep up
The repository is not archived, and its last push was on 2026-07-17. That push corresponds to the VideoChat3 announcement, which is a pointer to another repository rather than new code in this one, so the date alone overstates how much is happening here.
The licence is MIT, which is permissive and places few conditions on reuse of the code in this repository. That does not automatically settle the model weights. The README links to Hugging Face Spaces for VideoChat-7B-8Bit and InternVideo2-Chat-8B-HD, and to OpenXLab for VideoChat2, and it does not state the terms attached to those weights. Check the model card for whatever you download rather than assuming the repository's MIT licence covers it. This is a description of what the files say, not legal advice.
The upgrade cost is the part most teams underestimate. Because each variant is a separate directory with its own dependencies and its own demo notebooks, moving from one to another means re-establishing the environment and rewriting the calling code. The README gives no deprecation policy and no compatibility statement across video_chat, video_chat2 and video_chat_text, so budget for a migration rather than a package update.
Editorial conclusion
Adopt Ask-Anything if you need the VideoChat2 weights, the MVBench benchmark, or the 2M instruction samples for video tuning, and you are comfortable working inside a research repository where each model has its own directory and its own setup. Do not adopt it if you want a single supported product with one install command, or if you need a maintained chat application rather than a model release. Before committing, check which subdirectory holds the model you actually want, confirm the licence file covers the weights you plan to use, and note that the most recent repository activity points to VideoChat3 in a separate repository rather than to new work here.
Frequently asked questions
What is Ask-Anything from OpenGVLab?
It is an MIT-licensed Python repository that collects the VideoChat family of video chat models, including VideoChat1, VideoChat2 and variants built on ChatGPT, MOSS, StableLM and MiniGPT-4. It also documents MVBench and the 2M instruction samples released for tuning.
Is Ask-Anything one model or several?
It is several. The top level holds separate directories such as video_chat, video_chat2 and video_chat_text, and the README's Getting Started section lists End2End, ChatGPT and the other variants as distinct build paths rather than options of a single tool.
Can I ask anything to ChatGPT through this repository?
The video_chat_with_ChatGPT directory covers explicit communication with ChatGPT, and the README labels that route as sensitive with time because it depends on a hosted model. The video_miniGPT4 variant instead uses implicit communication with Vicuna and is described as not sensitive with time.
How do I install Ask-Anything?
There is no single install for the whole repository. You obtain the repository, look at the top-level directories, and then follow the setup instructions inside the specific variant you want, since there is no packaging metadata at the root.
Is Ask-Anything still being updated?
The last push to this repository was on 2026-07-17, and that update announced VideoChat3 in a separate repository rather than adding new code here. The README also notes that an updated version is being worked on.
Community notes