Building a VideoAgent-Style Multi-Agent System: Intent Parsing, Graph Planning, and Tool Routing for Video Editing Tasks
Not a research paper. A working multi-agent video editing system you can build today—intent parsing, graph planning, tool routing, all wired to FFmpeg and Whisper.
Why it matters
Practical agent-as-capability tutorial showing how to architect multi-agent systems for real video workflows. Demonstrates the shift from single-model chat to orchestrated tool pipelines—a pattern founders need to understand for production AI products.
The key facts
11 to knowMulti-agent architecture: intent parser, agent library, tool router, graph planner, textual-gradient optimizer
Integrated tools: FFmpeg, Whisper, scene detection, keyframe sampling, captioning, cross-modal indexing, beat-synced editing
Capabilities: video question-answering, summarization, artifact generation from natural-language instructions
API-key-free implementation (runnable locally)
Graph-based execution planning with repair mechanism
Intent parser + graph planner + tool router architecture
Integration with FFmpeg, Whisper, scene detection, keyframe sampling, captioning, cross-modal indexing
Beat-synced editing from natural-language instructions
API-key-free, runnable implementation
Textual-gradient optimizer for execution graph repair
Outputs: video Q&A, summaries, edited artifacts
Go to the source
MarkTechPostmarktechpost.com
Publisher excerpt: In this tutorial, we reconstruct the VideoAgent workflow as a runnable, API-key-free multi-agent pipeline. We build an intent parser, an agent library, a tool router, a graph planner, and a textual-gradient optimizer that repairs the execution graph. We wire these planning components to FFmpeg,…