CoFlow

CoFlow is an agent-native media canvas for Codex.

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It combines an infinite tldraw whiteboard with Codex skills and MCP tools so you can point at visual context, describe the change, generate a new image or video, and write the result back onto the canvas with local assets and version lineage.

Design Philosophy

CoFlow starts from a simple premise: pair Codex, one of the strongest AI agents available to developers today, with an infinite whiteboard canvas.

Codex provides the execution harness: it reads bounded visual context, turns open-ended intent into actionable prompts, chooses the right model and provider route, saves generated assets locally, and writes results back with lineage. The canvas preserves the free-form side of creative work: spatial thinking, references, annotations, alternate versions, and branching ideas, without reducing the workflow to a rigid form or provider panel.

What CoFlow Is

CoFlow is not a provider form, a lightweight Canva clone, or a static image board.

The canvas is the visual context surface:

• select or frame source images and videos;
• add arrows, boxes, notes, and spatial annotations;
• let Codex read bounded canvas context through MCP;
• generate through Codex native image tools or external providers;
• write generated media back as native tldraw image/video objects;
• preserve prompts, model/provider metadata, local paths, and lineage links.

The core workflow is:

select or frame media on canvas
→ describe the edit/generation request in Codex
→ CoFlow skills read bounded context
→ Codex chooses the right generation route
→ generated media is inserted back onto the canvas
→ linked versions remain traceable

Current Status

CoFlow is in a Phase 1 RC state focused on the image/video writeback loop.

Working today:

• tldraw-based infinite canvas;
• native image and video asset writeback;
• prompt-only image generation writeback without accidental lineage links;
• reference-based image/video workflow boundaries;
• Atlas Cloud provider execution for supported external image/video models;
• provider/model onboarding and status tools;
• multi-page canvas persistence;
• local .coflow/ asset and metadata store;
• Codex plugin manifest, skills, and MCP server.

Not claimed yet:

• full 3D canvas preview/editing;
• hosted multi-user collaboration;
• a polished consumer SaaS UI.

Repository Layout

The active plugin/runtime lives in:

coflow/

Important files:

coflow/.codex-plugin/plugin.json  # Codex plugin manifest
coflow/.mcp.json                  # MCP server config
coflow/mcp-server.mjs             # Codex-facing MCP tools
coflow/server.mjs                 # local canvas server
coflow/src/                       # tldraw canvas app
coflow/skills/                    # CoFlow Codex skills
coflow/lib/                       # provider/runtime helpers
coflow/tests/                     # regression tests

Generated assets and local runtime state are stored under the current workspace's .coflow/ and are ignored by git.

Quick Start

cd coflow
npm install
npm run build
npm run serve

Then open:

http://127.0.0.1:5176/

For plugin development, the local personal marketplace can point ~/plugins/coflow at coflow, then install with:

codex plugin add coflow@personal

After reinstalling a local plugin version, start a new Codex thread or restart Codex so new skills and MCP tools are picked up.

Provider Setup

Default image behavior uses Codex built-in GPT Image 2 for image generation and image editing/reference work when available.

Default video behavior uses Atlas Cloud Seedance 2.0 for text-to-video and reference/video editing routes.

Create an Atlas Cloud API key with this invite link:

Atlas Cloud API keys

Then add the key to a local env file:

ATLASCLOUD_API_KEY=...

Supported local env file locations:

.env.local
coflow/.env.local

Do not commit API keys or paste secrets into chat.

Supported Models

CoFlow uses friendly model names in user-facing docs and skills. Internal provider model ids stay in runtime configuration and diagnostics.

Defaults:

• Image generation/editing: Codex built-in GPT Image 2
• Video generation/editing: Atlas Cloud Seedance 2.0

Atlas Cloud image options:

• GPT Image 2
• Nano Banana 2
• Nano Banana 2 Lite
• Nano Banana Pro
• Seedream 5.0 Pro
• Seedream 5.0 Lite
• Seedream 4.5
• Wan 2.7
• Grok Imagine Image
• Qwen Image 2.0

Atlas Cloud video options:

• Seedance 2.0
• Seedance 2.0 Mini
• Kling V3.0 Turbo / Standard / Pro / 4K
• Kling O3 Standard / Pro / 4K
• Wan 2.7
• HappyHorse 1.1
• Grok Imagine Video
• Grok Imagine Video v1.5

Codex Skills

Core plugin skills:

• coflow-open opens the local canvas.
• coflow-provider-setup reads or changes image/video provider defaults.
• coflow-model-list summarizes configured model support.
• coflow-image handles image generation and image editing from canvas context.
• coflow-video handles text-to-video and reference/video revision workflows.

Development Checks

Run from coflow/:

npm test
npm run build

Plugin manifest validation:

python3 ~/.codex/skills/.system/plugin-creator/scripts/validate_plugin.py coflow

Design Principles

• The canvas is visual context and writeback surface.
• Codex owns intent understanding, skill routing, and provider orchestration.
• Use native tldraw assets/shapes/bindings before inventing custom records.
• Prompt-only generation should not create fake lineage links.
• Reference-based generation should preserve source relationships.
• Provider setup is not blanket upload permission; asset sharing is task-scoped.
• Local-first storage should make generated media and metadata inspectable.

License

MIT