Building a VideoAgent-Style Multi-Agent System: Intent Parsing, Graph Planning, and Tool Routing for Video Editing Tasks

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-processing tools, including FFmpeg, Whisper-based transcription, scene detection, keyframe sampling, captioning, cross-modal indexing, retrieval, trimming, beat-synced editing, and final rendering. By the end of the tutorial, we have a complete multi-agent video system that can answer questions about a video, summarize its content, generate a news-style overview, and produce edited video artifacts from natural-language instructions.

We begin by configuring the VideoAgent runtime, the optional LLM backend, the working directory, the package installation flow, and lightweight dependency fallbacks. We create a shared helper layer for shell commands, pip installs, file paths, and environment detection so the notebook runs smoothly in Colab or locally. We also define a unified LLM wrapper that supports OpenAI, DeepSeek, Anthropic, and Gemini while safely falling back to deterministic execution when no API key is available.

We define the full intent space, user inputs, specialized agent registry, terminal agents, and output producers that constitute the system’s core planning vocabulary. We parse each natural-language instruction into required video capabilities such as transcription, summarization, retrieval, rendering, or beat-synced editing. We then route tools and construct an initial agent graph, either through an LLM-generated plan or a deterministic terminal-only plan that the optimizer later repairs.

We implement the graph analysis and optimization logic that checks for edges, topological order, connected components, missing inputs, intent coverage, and compatibility. We compute the structural and alignment losses using τ, κ, and χ so the system can measure whether the current graph is executable and semantically complete. We then apply textual-gradient-style repairs by inserting missing producer agents and finally execute the optimized graph via a shared blackboard of intermediate outputs.

We build the lower-level video and multimodal processing tools that extract audio, transcribe speech, detect rhythm, identify scenes, sample keyframes, and generate captions. We use FFmpeg, Whisper, CLIP-style embeddings, histogram-based scene detection, and robust fallback logic so the pipeline remains runnable even when heavier models are unavailable. We also create a cross-modal index that aligns captions, keyframes, transcript segments, and embeddings for later retrieval-based video editing.