GraphCanon updated 3d · GitHub synced 3d
Decision brief
Ask-Anything is an end-to-end video chatbot framework leveraging LLMs like ChatGPT, miniGPT4, StableLM for enhanced video understanding.
Good fit when
- When you need advanced video and image processing with large language models for captioning and QA tasks
- If your projects require handling both long videos and detailed task annotations with optimized performance
Avoid when
- Avoid if only text-based interactions are needed, as Ask-Anything focuses on video understanding
- Not suitable for real-time applications requiring ultra-fast inference without compromising on accuracy
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Steady (31d since push)
- As of 3d
- Provenance
- Not a fork · Organization account
- As of 3d
- Security (OSV)
- No lockfile
- As of 1mo
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Install
pip install Ask-AnythingHow it fits your stack(5)
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Overview
A repository focused on end-to-end chatbots that leverage large language models (LLMs) to process and interact with videos. Supports various LLMs including ChatGPT, miniGPT4, StableLM, and MOSS.
Capability facts
- Languages
- python
Source: github.language · Aug 18, 2026
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README
🦜 VideoChat Family: Ask-Anything
|
[VideoChat-7B-8Bit] End2End ChatBOT for video and image. [InternVideo2-Chat-8B-HD]
中文 README 及 中文交流群 | Paper
⭐️: We are also working on a updated version, stay tuned!
:fire: Updates
- 2026/07/17: 🚀🚀 We release VideoChat3, a fully open, efficient 4B Video MLLM for general, long-form, and streaming video understanding. VideoChat3 improves 18/19 offline and 10/11 streaming metrics over Qwen3-VL-4B. We release the model weights, code, training recipes, and complete datasets. Check out our paper and homepage!
- 2025/01/18: We release videochat-flash and videochat-tpo to extend MLLMs' capabilities on both long and accurate video understanding. videochat-flash sets new records in mutiple video benchmarks (for both short and long videos), improving code usability by leveaging LLaVA and others. videochat-tpo exploits classical vision task annotations (e.g. tracking) to optimize MLLMs in a DPO manner, enhancing MLLMs' performance and enabling capabilities in tracking, segmentation, and more.
- 2024/06/25: We release the branch of videochat2 using
vllm, speed up the inference of videochat2. - 2024/06/19: 🎉🎉 Our VideoChat2 achieves the best performances among the open-sourced VideoLLMs on MLVU, a multi-task long video understanding benchmark.
- 2024/06/13: Fix some bug and give testing scripts/
- 2024/06/07: :fire::fire::fire: We release VideoChat2_HD, which is fine-tuned with high-resolution data and is capable of handling more diverse tasks. It showcases better performance on different benchmarks, especially for detailed captioning. Furthermore, it achieves 54.8% on Video-MME, the best score among 7B MLLMs. Have a try! 🏃🏻♀️🏃🏻
- 2024/06/06: We release **
For agents
This page has a .md twin and JSON over the API.