case study · multi-agent system
AI Reel Pipeline case study
Producing short-form vertical video by hand is slow and repetitive. → LangGraph agents script, plan and render vertical reels end-to-end with Remotion.
- 01 · The problem
The problem
Producing short-form vertical video by hand is slow and repetitive.
- 02 · My role
My role
Designed and built at Aevia.
- 03 · Architecture
Architecture
Topic
one topic in, finished reel out
→ Supervisor
Supervisor
LangGraph · routes the workers
→ Script + Hook
Script + Hook
beat-by-beat script, scroll-stopping first beat
→ Visual prompt + Asset
Visual prompt + Asset
generates the images
→ Editing agents
Editing agents
parallel: camera, transitions, pacing, grade, text, voiceover, music, SFX, beat sync
→ QC
QC
validates everything; render runs only if it passes
→ Remotion render
Remotion render
video defined in React
→ Vertical reel
Vertical reel
1080×1920 MP4 · 30 fps · voiceover + animated captions
- 04 · Key decisions
Key decisions
LangGraph: an explicit graph with a supervisor, parallel branches, merge steps and a conditional QC → render edge. State is shared and inspectable.
Remotion: the video is defined in React code, so motion, transitions and captions are fully programmatic and repeatable, with no manual editing.
- 05 · Challenges
Challenges
Timing: fixed short beats clashed with the real voiceover length, so captions were unreadable. Beat duration is now driven by the voiceover length.
Parallel agents overloaded the LLM endpoint, so calls now retry with backoff.
- 06 · Result
Result
The full pipeline runs end-to-end on a server and renders a finished reel from a single topic. Private repo, case study only.
- 07 · Stack
Stack
- LangGraph
- Multi-agent
- LLM APIs
- Remotion