Building a VideoAgent-Style Multi-Agent System: Intent Parsing, Graph Planning, and Tool Routing for Video Editing Tasks
In this tutorial, we build a runnable reconstruction of the VideoAgent workflow, focusing on the core agentic pipeline behind video understanding, retrieval, editing, and remaking. We start by configuring a lightweight environment that works without API keys. We define an intent parser, an agent library, a tool router, a graph planner, and a textual-gradient optimizer that repairs missing dependencies in the execution graph. We also connect these planning components to practical video-processin
Get smart on it
This tutorial describes how to build a multi-agent system for video editing that works by parsing natural language instructions, planning a sequence of tasks, and routing them to appropriate tools. The system combines components like intent parsing, graph planning, and tool routing to handle video understanding, retrieval, and editing tasks. The tutorial walks through configuring a runtime environment, defining an LLM wrapper that supports multiple AI providers, and connecting planning components to practical video-processing tools including transcription, scene detection, beat-synchronized editing, and rendering. By the end, the system can answer questions about videos, summarize content, and produce edited videos from natural language instructions.
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