by avabbbb
Open-source agent-native visual production workspace where humans and coding agents edit the same live canvas — local-first, BYOK image/video models.
# Add to your Claude Code skills
git clone https://github.com/avabbbb/FlovartLast scanned: 9/16/2026
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}See how Flovart compares with popular alternatives.
Flovart is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by avabbbb. Open-source agent-native visual production workspace where humans and coding agents edit the same live canvas — local-first, BYOK image/video models. It has 138 GitHub stars.
Yes. Flovart passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/avabbbb/Flovart" and add it to your Claude Code skills directory (see the Installation section above).
Flovart is primarily written in TypeScript. It is open-source under avabbbb on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh Flovart against similar tools.
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Jump to: Quick start · Feature tour · Why Flovart? · Core capabilities · Bring your own models · Compatibility · Architecture · Local-first and security · Roadmap · Contributing
The Releases page may contain test or preview artifacts; it is not a claim that every host or provider is Stable.
Run from a source checkout while the versioned CLI package remains a release gate:
git clone https://github.com/avabbbb/Flovart.git
cd Flovart
npm install
npm run flovart:cli -- start --source --web --open
npm run flovart:cli -- status --json
Then ask your local agent: “Open Flovart and work on this Workflow.”
Your agent learns Flovart through the Agent Integration Skill at .agents/skills/flovart/ (mirrored for Claude under .claude/skills/flovart/ and bundled for WorkBuddy under integrations/workbuddy/flovart/skills/flovart/). On a source checkout the harness auto-discovers it; the packaged CLI also ships it so flovart ensure can register it. This is a thin adapter — it teaches the agent the 5 operations below; it is not a Production Skill / Marketplace feature.
The normal agent loop is status, workflow.inspect, workflow.selection.get, workflow.apply and workflow.node.run. Connection setup and diagnostics use ensure and doctor; development-only browser checks are covered in the Getting Started guide.
Every clip below is a real recording of the running app — one operation, start to finish, with no composited frames and no mockups. Each is cut to the action.
Agent-native, not screen-scraping. These are operations on the same Workflow an agent drives. The hero above and the CLI capture under Architecture are agent-driven end to end, with no human input; the tour below is that same surface driven by hand, because that is the path a new user follows first. Node creation, connections, selection, viewport, node moves and resizes, and the node tools are all exposed to agents as typed operations — with revision and idempotency boundaries rather than coordinates on a screen.
Video and audio tools run ffmpeg.wasm in the browser; their core is pre-warmed before the recorded action, so the clip shows the operation rather than the one-off ~30MB wasm download — clip length is therefore not the wait time on a first run. Registration, method and limits: DEMO_RECORDING.md.
Four local operations on the same generated fixture plate, each producing a real result node. None of them contacts a model service.
Also local, also without a model service: these run through the in-browser ffmpeg core, and each one creates its own result node (audio/video split creates two).