VideoAgent
Natural-language video understanding, editing, and creative remaking in one framework.
- Source repo
- HKUDS/VideoAgent
- Stars
- ★ 1.9k
- Last updated
- 2mo ago
- License
- MIT
- Primary language
- Python
- FA score
- 29/100 · Major gaps
At a glance
- How it runs
- Works with
- Portable with changesOpenAI API · Claude API (Partial support)
- Cost
- Free software; you pay for model usage
- Setup effort
- High · needs real infrastructure
- You'll need
- Typical use
- A researcher analyzing a podcast or video corpus who needs question answering and summaries can use the Video QA/Summarization path after preparing Whisper.
- Not a fit if
- Users without an 8GB GPU or on unsupported operating systems
- Teams wanting zero-config setup; multiple models and API keys must be configured
- Source review
- 29/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
VideoAgent is an MIT-licensed framework for video understanding, editing, and remaking. It processes requests through intent analysis, intent-to-agent mapping, graph-powered workflow construction, and a documented two-step self-evaluation feedback loop. Its Storyboard Agent analyzes pre-captioned video banks and turns requests into fine-grained, visually and semantically aligned queries for retrieval. The documented feature set includes video Q&A, summarization, movie edits, commentary videos, video overviews, meme remaking, music videos, and cross-lingual adaptations. It runs locally through a Python command-line entry point, `main.py`, and relies on downloaded speech, voice-conversion, transcription, and multimodal models.
After python main.py starts, the user enters a request at User Requirement:. The system decomposes it into explicit and implicit sub-intents, uses an intent-to-agent mapping to select capabilities, and creates an executable graph-based workflow with an execution sequence; self-evaluation feedback iteratively refines planning. The Storyboard Agent analyzes a pre-captioned video-material bank and produces fine-grained visual queries for retrieval. Depending on the task, the framework can perform video Q&A or summarization and produce movie edits, commentary videos, video overviews, meme videos, music videos, and cross-lingual comedy adaptations. Provider settings are stored in environment/config/config.yml, including API keys and base URLs for DeepSeek, Claude, GPT, and Gemini.
- A researcher analyzing a podcast or video corpus who needs question answering and summaries can use the Video QA/Summarization path after preparing Whisper.
- A creator who wants to rewrite speech in an existing video while retaining the original visuals and speaker voice can use the documented video remixing, TTS, or SVC-related workflows.
- A producer with a stand-up script, reference script, and footage who needs a completed comedy video with timing, audience reactions, and matched footage can use the documented stand-up scenario.
- A bilingual comedy creator adapting English stand-up into Chinese crosstalk, or Chinese crosstalk into English stand-up, can use the Cross Talk or Talk Show scenarios.
- A music-video maker using DiffSinger, seed-vc, Whisper, and ImageBind for an AI music video can use the MAD SVC scenario.
- A content team that needs to retrieve clips matching textual descriptions from a video bank before producing a commentary video or overview can use the Storyboard Agent retrieval flow.
How do you install or deploy this agent?
The documented environment is Linux or Windows with 8GB GPU memory. Run:
git clone https://github.com/HKUDS/VideoAgent.git
conda create --name videoagent python=3.10
conda activate videoagent
conda install -y -c conda-forge pynini==2.1.5 ffmpeg
pip install -r requirements.txtDownload the models needed for the intended feature: the README supplies huggingface-cli download or wget commands for CosyVoice, fish-speech, seed-vc, DiffSinger, Whisper, and ImageBind; ImageBind setup also calls for git lfs install. Edit environment/config/config.yml with the relevant deepseek_api_key, claude_api_key, gpt_api_key, and gemini_api_key, plus their base URLs. The README explicitly states that Claude is required for the Agentic Graph Router.
How do you use this agent?
After downloading models and configuring environment/config/config.yml, run:
python main.pyEnter a video task when the console displays User Requirement:. Documented examples include rewriting dialogue in an existing video while retaining the original speaker voice and visuals, or turning a stand-up script, reference script, and footage into a complete video. Model requirements vary by scenario: Video QA/Summarization lists Whisper, while Cross Talk, Talk Show, Comm, and News list CosyVoice, Whisper, and ImageBind.
What are this agent's strengths and limitations?
- One framework documents video Q&A, summarization, editing, commentary, overviews, meme remaking, music videos, and cross-lingual adaptation instead of a single video task.
- Its graph-powered workflow construction combines user-intent mapping with a documented two-step self-evaluation loop for multi-step video requests.
- The Storyboard Agent explicitly covers the retrieval stage by converting a pre-captioned material bank into visually and semantically aligned fine-grained queries.
- Feature-specific model requirements let adopters download only the models relevant to their intended workflows.
- The README explicitly requires Claude for the Agentic Graph Router, so operation is not fully independent of external model providers.
- Different features also call for DeepSeek, GPT, or Gemini configuration and for models such as CosyVoice, fish-speech, seed-vc, DiffSinger, Whisper, and ImageBind, increasing setup complexity.
- The documented runtime requires Linux or Windows, 8GB GPU memory, Python 3.10, FFmpeg, and Pynini 2.1.5.
- The supplied material documents only the
python main.pycommand-line entry point; it provides no evidence of an HTTP API, container deployment, or hosted service. - Demo audio and video assets are described as internet-sourced and for research and demonstration purposes, so commercial adopters need to review content rights independently.
How does this agent compare with similar options?
The README compares VideoAgent with Director, Funclip, NarratoAI, and NotebookLM. In that table, VideoAgent is marked for beat-synced edits, storytelling video, video overview, meme remaking, song remixes, cross-lingual adaptations, video Q&A, and sound-effects tools, while the named alternatives cover only subsets of those rows.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| VideoAgent This agent | 29 · Major gaps | CLIFree + model costs | ★ 1.9k | 2mo ago | Python | — |
| Qwen Audio Agent | 59 · Major gaps | CLIFree + model costs | ★ 2.8k | today | JavaScript | — |
| Big-AGI | 57 · Major gaps | Self-hosted serviceFreemium | ★ 7.1k | 1d ago | TypeScript | OpenAI API · Claude API |
| AgentCall join-meeting Skill | 46 · Major gaps | Agent plugin / skillFreemium | ★ 159 | 11d ago | Python | Codex · Claude Code |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows: the repository provides no documentation on permission management, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. All trust-related criteria are absent, hence score 0.
Evidence shows: README and pyproject.toml are largely consistent in dependencies and feature descriptions, but there are inconsistencies such as pyproject.toml declaring license as Apache while actual LICENSE is MIT, and README claims Windows support but dependencies include Linux-only packages. Dependency availability: many dependencies are from external sources, but no version locking or mirrors are provided, posing availability risks. Failure messages: no documentation on error handling or user prompts.
Evidence shows: README identifies target users (video creators, researchers) and multiple use cases (video understanding, editing, remaking), but capability boundaries are vague, with no clear limitations or failure conditions. Trigger precision: users input natural language, but no details on how to precisely control or avoid false triggers. Environment fit: environment requirements (GPU memory, OS) and installation steps are provided, but compatibility details for different OS or hardware are not specified.
Evidence shows: README provides clear table of contents and feature overview, installation notes are detailed, but version changelog and clear maintenance responsibility are missing. Naming stability: project name and version are defined in pyproject.toml, but no naming conventions or stability guarantees are stated. Examples and FAQ: multiple demos and examples are provided, but FAQ is missing. Known limitations: only content copyright issues are mentioned in README, not technical limitations. License: LICENSE file is MIT, but pyproject.toml declares Apache, inconsistency.
Evidence shows: output usability: README showcases various video output types and demos, but no output format or quality guarantees. Marginal value: project offers unique features (e.g., cross-lingual adaptation, meme video remaking), but no detailed comparison with existing tools. Cost-benefit: requires downloading multiple large models and dependencies, but no resource consumption or performance benchmarks.
Evidence shows: performance claims in README (e.g., success rate 0.95) lack specific experimental data or reproduction steps, lacking traceability. Cross-source corroboration: only relies on README and pyproject.toml, no third-party verification. Fact-inference separation: README mixes feature descriptions and performance claims, not clearly distinguishing facts from inferences.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- License inconsistency: pyproject.toml declares Apache, but LICENSE file is MIT, which may cause legal confusion.
- Dependency security not assessed: many dependencies from external sources, no version locking or security audit, posing supply chain risks.
- Performance claims lack evidence: README claims success rate 0.95 etc., but no experimental data or reproduction steps, not verifiable.
- Data flow and permissions opaque: no explanation of how user data, API keys, or permissions are handled, posing privacy and security risks.
FAQ
Which model-provider credentials are required?
Can I download only a subset of local models?
How do I start it and submit a task?
python main.py and enter the request at the User Requirement: console prompt.