# Build brief — a focused alternative to Wireflow

> **Verdict:** Partly, if you narrow it · **Buildability:** 53/100 · **Category:** Generative Media
> **Source:** https://www.canitbevibecoded.com/wireflow
> Independent editorial assessment from Can It Be Vibe Coded? Not affiliated with, endorsed by, or derived from Wireflow. Verify current pricing and capabilities before acting.

## Context

**Wireflow** — Node canvas for chaining image, video, and audio models into a pipeline you can re-run. It currently costs $24/mo.

Chaining a few model calls is a script, and ComfyUI already gives you a free node canvas. What you will not get in a contained effort is the part after generation: a track and keyframe timeline with hosted rendering, and live multiplayer inside it. The graph is vibecodable. The editor attached to it is not.

This brief describes a focused, single-operator replacement for the part of Wireflow that is genuinely reproducible. It is deliberately narrower than the product it replaces, and it says so in writing. Build the useful core; do not pretend to have rebuilt the rest.

## What you are building

Wire model calls into a graph on a canvas, hit run, then layer and time the generated pieces into a finished cut.

- Build a focused single-user workflow with real persistence, search, and export.
- A responsive interface with real empty, loading, success, and error states.

## Requirements

### Functional

- Node 20.
- Somewhere to run it that stays up while jobs finish.
- A GPU only if you self-host the models instead.

### Data and integrations

- Fal.ai API key or another hosted model provider.

Each of these needs a real account, credential, or quota. Set them up before writing feature code.

### Non-functional

- Accessibility: semantic markup, labelled controls, visible focus, and reduced-motion support.
- Security: server-side secrets, validated input, and no credentials in the client bundle.
- Reliability: retries with backoff on external calls, and a clear failure state when a provider is down.
- Portability: the operator can export their data and leave without losing it.

## Implementation brief

Build me a local visual pipeline runner for AI media generation. Requirements:

- One Vite + React app and one Express server in the same repo, SQLite via
  better-sqlite3, no auth, no accounts, runs on localhost.
- Canvas on React Flow: drag nodes from a palette, wire outputs to inputs,
  pan and zoom, autosave the graph to SQLite on every change.
- Six node types only: Text input, Image import (upload to ./storage), Prompt
  template (interpolates {{upstream}} values), Image generate, Video generate,
  Preview. Generate nodes call fal.ai through @fal-ai/client, FAL_KEY in .env.
- Model config lives in models.json: per model, its fal path and which fields
  are wired ports versus typed-in settings. Ship exactly two entries,
  fal-ai/flux/dev and fal-ai/kling-video/v1/standard/text-to-video.
- Run = topological sort, execute layer by layer, store each node's output in
  SQLite keyed by node id plus a hash of its resolved inputs. Re-running only
  re-executes nodes whose input hash changed.
- Generate nodes NEVER run on edit. Only the Run button or a per-node run
  button submits a job. This rule is the whole point, do not add reactive
  auto-execution anywhere.
- Submit through fal's queue API and poll by request id, persisting the request
  id BEFORE the first poll, so a finished job survives a server restart.
- README covers where FAL_KEY goes, that every run spends real money at
  fal.ai, and how to add a third model to models.json.

Excluded on purpose: multiplayer, credits and billing, iterators or fan-out
over arrays, video assembly, hosted deploys, and any provider besides fal.ai.

## Delivery standard

- Inspect the repository first, then write a short implementation plan before writing code.
- Deliver the smallest complete end-to-end workflow first; every primary control must work against persisted data.
- Use real validation and storage; never substitute fake dashboards, decorative controls, hard-coded success states, or mock integrations.
- Include responsive layouts plus genuine empty, loading, success, validation, and failure states.
- Keep secrets server-side in environment variables, provide .env.example, and never commit credentials or user data.
- Add structured logs around every external call and return actionable errors without leaking sensitive details.
- Write unit tests for the core logic and one automated test of the main user journey.
- Finish with a README covering setup, architecture, data location, backups, tests, deployment, and known limitations.

## Acceptance criteria

- [ ] A clean install starts the app using only the README and .env.example.
- [ ] The primary journey works from first visit through saved result, reload, edit, export, and deletion where applicable.
- [ ] Invalid input, missing configuration, provider failure, and an empty database each have a usable state.
- [ ] The interface works at 390px and 1440px, is keyboard navigable, and shows visible focus on every control.
- [ ] Tests, type checking, linting, and a production build all pass with no ignored failures.
- [ ] No part of the interface implies a live integration, security guarantee, or scale capability that was not actually built and verified.

## Non-goals

Do not build these, and do not claim to have replaced them:

- A compositor and keyframe timeline, with hosted rendering.
- Live multiplayer on the canvas and inside the editor.
- Hundreds of model schemas kept current for you.
- Paid steps that never fire by accident, and jobs that survive a closed tab.
- The last 20 percent is sync, migration fidelity, speed, and edge cases.
- Permissions, presence, and shared workflows are difficult to simplify.

## What you still own after launch

- Secure credentials, rotate secrets, and handle provider rate limits.
- Run migrations, backups, restores, and dependency updates.
- Test the critical journey after every model, API, or hosting change.
- Monitor failures and fix the edge cases a first prompt will miss.
- Maintain every third-party integration as APIs and OAuth rules change.

## Risk

**Operational risk.** The code is achievable; dependable data, integrations, and ongoing operations are the real cost.

Editorial confidence in this assessment: high. No independent one-shot implementation is linked yet.

## Prior art

Working open-source software you can read, fork, or borrow from before starting:

- [ComfyUI](https://github.com/comfyanonymous/ComfyUI) — free node graph for image and video models, with a large shared-workflow scene around it · the honest starting point
- [n8n](https://github.com/n8n-io/n8n) — self-hostable visual workflow engine, weaker on media
- [Flowise](https://github.com/FlowiseAI/Flowise) — drag-and-drop LLM chains, self-hosted

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