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VideoAgent: All-in-One Framework for Video Understanding and Editing

VideoAgent is an all-in-one multi-agent framework that overcomes the limitations of existing automated video systems by integrating shot planning, cross-modal retrieval, and a multi-agent orchestration system to achieve professional-quality, coherent long-video understanding and editing with high success rates and reduced costs.

Original authors: Hengji Zhou, Lingxuan Huang, Jian Wang, Bing Zhou, Si Wu, Lianghao Xia, Chao Huang

Published 2026-06-23

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CC BY 4.0

Original authors: Hengji Zhou, Lingxuan Huang, Jian Wang, Bing Zhou, Si Wu, Lianghao Xia, Chao Huang

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you want to make a movie, but you don't have a director, a scriptwriter, an editor, or even a camera crew. You just have a pile of raw footage, some music, and a vague idea in your head. Usually, you'd need to hire a team of experts to turn that mess into a coherent story.

VideoAgent is like hiring a super-smart, automated "Movie Boss" that does all that work for you. It's a new computer system designed to understand your natural language instructions (like "Make a funny video about Spider-Man dancing to this song") and actually build the video for you.

Here is how it works, broken down into simple parts:

1. The Problem: The "One-Tool" Limitation

Think of current video editing AI tools like a Swiss Army knife that only has a screwdriver. They are great at one specific thing (like cutting a clip or adding a subtitle), but they can't plan a whole movie. They also get confused by long videos, like trying to read a whole novel but only understanding one sentence at a time. They can't connect the dots to tell a story that flows from beginning to end.

2. The Solution: A Team of Specialists (The "Agents")

VideoAgent doesn't try to be one giant brain. Instead, it acts like a film production studio with over 30 different specialists (called "Agents") working together.

The Scriptwriter Agent: Reads your idea and breaks it down into a shot-by-shot plan (like a storyboard).
The Librarian Agent: Scans your huge library of video clips to find the exact scene that matches the scriptwriter's description.
The Rhythm Agent: Listens to the music and tells the editor exactly when to cut the video to the beat.
The Voice Agent: Can clone a voice or change the language of what someone is saying.

3. The Magic Glue: "Textual-Gradient Graph Optimization"

This is the fancy term for the system's brain. Imagine you are trying to build a complex Lego structure, but you keep getting the instructions wrong.

Old Way: You guess the instructions, build it, realize it's wrong, and start over from scratch.
VideoAgent's Way: It builds a draft plan, then a "Quality Control Bot" checks it. If the plan is missing a step or has a loop that makes no sense, the bot sends a specific message back: "Hey, you forgot to connect the audio to the video, and you have a loop here."
The system then uses this feedback to "tweak" the plan, not by starting over, but by making small, smart adjustments until the plan is perfect. It's like editing a document by getting specific comments from a teacher rather than rewriting the whole essay every time.

4. How It Makes a Video (The Workflow)

Let's say you ask VideoAgent to "Make a music video of Spider-Man dancing to this beat."

Planning: The "Shot Planning Agent" reads your request and the music. It writes a script: "Shot 1: Spider-Man jumping. Shot 2: Close up of his mask. Shot 3: Dancing to the beat drop."
Retrieval: The "Retrieval Agent" goes into your video library. Instead of just looking for the word "Spider-Man," it understands the vibe. It finds the specific clip where he jumps, matching the energy of your script.
Trimming: The "Trimming Agent" cuts those clips to the exact length needed so they hit the musical beats perfectly.
Orchestration: The "Graph Orchestration" brain connects all these steps. It makes sure the audio is extracted before the rhythm is detected, and the rhythm is detected before the video is cut. It builds the assembly line.

5. The Results: Almost Human Quality

The researchers tested this system against other AI tools and even human editors.

Success Rate: It successfully built the complex "assembly line" of tools needed to make a video 87% to 95% of the time.
Cost: Because it's so efficient and doesn't waste time guessing, it costs 60% less to run than other methods.
Quality: When humans watched the videos, they rated them almost as high as videos made by professional human editors (only about 4% lower). The videos were coherent, the audio matched the video, and the stories made sense.

Summary

VideoAgent is a framework that turns a chaotic pile of video clips and a simple text prompt into a polished, professional-looking video. It does this by acting as a conductor for a massive orchestra of specialized AI tools, constantly checking and refining its own plan to ensure the final movie tells a coherent story. It bridges the gap between "I have an idea" and "Here is my finished movie."

Technical Summary: VideoAgent

Problem Statement

Video editing has become a cornerstone of digital media creation, yet automated systems currently face two critical limitations that hinder their adoption for professional-quality, long-form content:

Limited Scope and Diversity: Existing approaches are often restricted to short-segment processing or specific domain tasks (e.g., only summarization or only beat-syncing). They lack the capability to handle the diverse range of operations required in real-world scenarios, such as combining narrative structuring, audio synthesis, and visual retrieval.
Lack of Long-Video Understanding and Planning: Current systems struggle with coherent narrative creation over extended durations. They lack the top-down planning capabilities necessary to decompose complex story structures into specific shot sequences, preventing the generation of videos with sufficient length and narrative coherence.

Methodology

The authors propose VideoAgent, an all-in-one agentic framework designed to unify video understanding and editing. The methodology is built upon two primary innovations:

1. Automated Video Shot Creation

To address the challenge of coherent long-form planning, VideoAgent employs a Shot Planning Agent and a Cross-Modal Retrieval system:

Shot Planning: The system generates structured, natural language storyboards ( $S$ ) that define the visual content for each shot. This process involves a global-aware mechanism where the agent first compresses visual context ( $C$ ) from available raw materials ( $M$ ) and then combines this with the user instruction ( $I$ ) to generate a sequence of shot descriptions ( $s_1, \dots, s_{|V|}$ ). This ensures the narrative structure is planned before execution.
Cross-Modal Retrieval: Once storyboards are generated, the system retrieves semantically aligned visual content from extensive libraries. It utilizes cross-modal encoders (e.g., ImageBind, CLIP) to map storyboard text and video keyframes into a unified embedding space, selecting clips with the highest cosine similarity to the storyboard descriptions.
Fine-Grained Trimming: To match narrative rhythm and dialogue, a Trimming Agent uses Vision-Language Models (VLMs) to select precise sub-segments from retrieved raw materials, ensuring temporal alignment with the storyboard's timing requirements.

2. Multi-Agent Graph Orchestration

To manage complex, multi-step editing workflows, VideoAgent introduces a Multi-Agent Orchestration Framework integrating over 30 specialized agents (e.g., audio extraction, rhythm detection, face swapping, voice cloning).

Agent Library: The system categorizes tools into foundational processing utilities (e.g., audio extraction, lip-sync) and creative generation agents (e.g., scriptwriting, meme creation).
Textual-Gradient Graph Optimization: Unlike conventional single-pass graph generation, VideoAgent treats workflow construction as an iterative optimization problem. It employs Textual-Gradient Graph Optimization, where an LLM analyzes the current agent graph's topology and quality metrics (structural validity, intent coverage, edge consistency). The LLM generates "textual gradients" in natural language to iteratively refine the graph—adding missing agents, removing invalid dependencies, or reordering execution sequences—until the workflow satisfies the user's intent and structural constraints.

Key Contributions

All-in-One Agentic Framework: VideoAgent unifies video understanding and editing into a single framework capable of handling diverse genres (e.g., music videos, storytelling, news overviews, meme edits) and production workflows.
Novel Technical Solutions:
- Global-Aware Shot Creation: A mechanism for coherent long-video planning that bridges the gap between high-level narrative instructions and granular visual retrieval.
- Textual-Gradient Graph Optimization: A method for adaptively assembling complex editing pipelines by iteratively refining agent graphs based on explicit quality metrics rather than static heuristics.
Comprehensive Evaluation: The authors introduce the VideoEdit benchmark, featuring high-quality instructions and human-aligned evaluation metrics. They also release a suite of over 30 specialized tool agents.

Experimental Results

Extensive experiments on the VideoEdit benchmark and public datasets (Shot2Story, VideoRepurpose) demonstrate VideoAgent's superiority:

Orchestration Success: VideoAgent achieves success rates between 87% and 95% across various backbone models (e.g., Claude-Sonnet-4, GPT-4o), outperforming existing multimodal LLMs and agentic systems by 2–25%.
Video Understanding: The framework significantly improves retrieval metrics. For instance, using GPT-4o as a backbone, Recall@1 increased from 31.22% to 48.85%, and Intersection over Union (IoU) improved from 18.53% to 26.99%.
Cost Efficiency: By employing strategic shot planning and targeted retrieval, VideoAgent reduces API costs by approximately 60% compared to baselines that process all candidate shots.
Human Evaluation: In a study involving 26 participants rating 18 trending video categories, VideoAgent produced content with quality ratings only 4% below human-created videos, approaching professional-level performance.

Significance

The paper positions VideoAgent as a significant step toward bridging the gap between the widespread demand for content creation and the technical barriers of professional video editing. By demonstrating that an agentic framework can autonomously plan coherent long-form narratives and orchestrate complex, multi-modal workflows, the authors claim to have established a new standard for automated video creation. The work suggests that future video editing systems can move beyond isolated task automation toward holistic, product-level content generation that rivals human creativity in both structure and execution.