From: aidotengineer
This article explores the application of large language models (LLMs) in generating code, specifically for automated video editing. The discussion centers on Va’s first open-source video editing agent, developed in collaboration between Diffusion Studio and Reskill [01:06].
The Need for Automated Video Editing
The initiative for this agent arose from a need for an automatic tool to edit videos for Reskill, a platform focused on personalized learning [01:12]. Traditional tools like FFmpeg presented limitations, leading to the search for more intuitive and flexible alternatives [01:22]. While Remotion offered some solutions, it had issues with unreliable server-side rendering [01:30]. The core library from Diffusion Studio was chosen due to its favorable API, which eliminated the need for a separate rendering backend [01:35].
Leveraging LLMs for Code Generation
The core library enables complex compositions through a JavaScript/TypeScript-based programmatic interface [01:46]. This capability is crucial because it allows LLMs to generate the necessary code to run the video editing processes [01:54].
The core concept behind this approach is to empower the LLM to write its own actions in code [02:00]. This method is preferred because:
Code is the best possible way to express actions performed by a computer [02:05].
Furthermore, multiple research papers have demonstrated that LLM tool calling is significantly more effective when implemented in code compared to JSON [02:13].
Agent Architecture and Workflow
The current architecture of the video editing agent involves an LLM that initiates a browser session using Playwright and connects to an operator UI [02:27]. This web application serves as a video editing UI specifically designed for AI agents, rendering video directly in the browser via the WebCodecs API [02:35]. Helper functions facilitate file transfers between Python and the browser using the Chromium DevTools protocol [02:46].
The typical flow for the agent involves three main tools [03:00]:
- Video Editing Tool: Generates code based on a user prompt and executes it in the browser [03:08].
- Doc Search Tool: Utilizes RCK to retrieve relevant information if additional context is required [03:17].
- Visual Feedback Tool: After each execution step, compositions are sampled (currently at one frame per second) and fed to this tool [03:25]. The visual feedback tool acts as a “generator and discriminator,” similar to the GAN architecture [03:33].
Once the visual feedback tool provides a “green light,” the agent proceeds to render the composition [03:44].
llm.txt for Agent Control
The system also ships with llm.txt, which functions similarly to robots.txt but for agents [03:55]. This file, combined with specific template prompts, can significantly enhance video editing capabilities [04:00].
Future Development and Remote Capabilities
While the first version of the agent is implemented in Python, a TypeScript implementation is currently underway [04:37]. The project adheres to the saying, “any applications that can be written in typescript will be written in typescript” [04:49].
The setup is flexible, allowing the agent to connect to a remote browser session via WebSocket, with each agent receiving a separate, GPU-accelerated browser session behind a load balancer [04:14].