Writing and content decision
Laper
A small team can realistically build the core screenplay editor, parser, AI-call flow, and CRDT collaboration, but reproducing Laper's full hosted product (branded assistant models, generation credits, high-speed pipelines, community/publishing features, and polished UX) would take much more time and resources.
Visit website↗Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All Laper alternatives, with the arithmetic →
What a replacement has to do
- Edit screenplay text in an editor → parse screenplay into scenes/characters/beats → call an LLM-based analysis endpoint to return structure/notes → show notes inline and update structured views → persist document and sync collaborators.
What it still won’t have
- Proprietary generation/prompting optimizations and branded assistant models (e.g., Laper IMAX/16mm)
- Built-in credit/asset generation pipeline and high-speed dedicated generation channels
- Product polish, docs, and frequent shipping cadence (the site claims ~20 improvements/week)
- Integrated community/publishing features and hosted originals library
What remains hard
- Product polish and ongoing maintenance
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 6 seats.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal AI-assisted screenwriting editor using React (Next.js), a Postgres database, a Node/Express API, and a CRDT library (Yjs) for realtime sync. Implement: 1) a Fountain/FDX parser that extracts scenes/characters/beats and a formatter that preserves industry layout; 2) a rich editor UI that applies screenplay formatting and supports inline comments; 3) an AI orchestration endpoint that accepts a selected scene or range, calls a configurable LLM API (OpenAI/Anthropic) and returns structured notes (scene diagnosis, character pressure, beats); 4) persistent project/version storage and import/export (Fountain/FDX); 5) CRDT-based realtime collaboration and simple access controls. Out of scope: built-in image/video generation, custom LLM training, a paid subscription system, and a public publishing library. Include error handling for network/LLM failures, retries, and unit/integration tests for parser, API, and CRDT sync.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 4 cited sources+3
- Price verified on pricing page+3
- Evidence score63
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 4
Every page the run actually retrieved.
- official productLaper — official product
- official pricingLaper Pricing
- official docsLaper Docs
- open sourceKirilllive/tuesday-js
Integrity checks
What held up, and what did not.
