LOOM

Inspiration

Animation pre-production is messy, nonlinear, and cognitively heavy. Creative directors and independent animators constantly juggle ideation, scripting, design, and storyboarding—often across disconnected tools. During early research conversations with professional creators, a recurring pattern emerged: AI tools generated too much output. Narrative and visual consistency drifted across stages. Version tracking was chaotic. Creative control often felt diluted rather than enhanced. What stood out most was this insight: Creators didn’t want more automation. They want coordination.

That became the foundation of LOOM

What it does

Today, creative directors and independent animators rely on fragmented AI tools—one for ideation, another for scripting, another for image generation. This creates coordination chaos, version confusion, and broken narrative and visual continuity.

LOOM fixes that.

It introduces a stage-aware multi-agent architecture, where a Core Agent acts like a project manager and orchestrates four specialized agents—Ideation, Scripting, Design, and Art—across dedicated visual boards that mirror real studio workflows.

Instead of scattered chat logs, creators get structured, lineage-aware boards for ideation, scripting, design, and storyboarding. They can branch ideas, refine specific elements, track versions, and maintain visual and narrative consistency from concept to storyboard.

How we built it

1. Architecture

we use a centralized multi-agent system:

Core Agent → Project manager Ideation Agent → Divergent creativity Scripting Agent → Narrative structure Design Agent → Visual coherence Art Agent → Styleframes & storyboards

The Core Agent handles:

Stage detection Task delegation Result validation Cross-stage consistency Version lineage

Specialized agents never directly talk to each other — the Core acts as orchestrator.

Challenges we ran into

LLMs sometimes misinterpreted vague instructions.

Example: “I want the story.”

Is that: A concept? (Ideation) A full script? (Scripting) A beat outline?

We improved this with: Stage-aware prompts Explicit task specifications

Validation checks before publishing outputs

Accomplishments that we're proud of

In controlled testing environment we were able to achieve the following:

Improved coordination across stages Better narrative and visual consistency Reduced revision frequency Faster task completion But the most meaningful outcome wasn’t speed. It was this: It felt like they were directing a team — not fighting a tool.

What we learned

Pre-production follows iterative strands:

Ideation → Scripting → Design → Storyboard

With constant back-and-forth loops This isn’t a pipeline problem — it’s a coordination problem.

A single LLM managing all responsibilities accumulates: Context overload Instruction drift Cross-stage inconsistency Dividing responsibility reduces entropy.

What's next for LOOM

Real-time collaborative multi-user support Cost effective Animation generation.

Built With

• docker
• gemini3
• langgraph
• nextjs
• postgresql
• python
• s3
• travily