Skill Detail

Use Flowise for visual agent workflow orchestration

Compose, test, and hand off visual AI-agent flows in Flowise when the work needs inspectable orchestration instead of ad hoc prompting.

Templates & WorkflowsMulti-Framework
Templates & Workflows Multi-Framework Security Reviewed
⭐ 53.3k GitHub stars ⬇ 11.9k/wk npm
COPY SKILL INSTRUCTIONS (OPTIONAL)
npx skills add agentskillexchange/skills --skill use-flowise-for-visual-agent-workflow-orchestration Copy
Uses the third-party skills CLI, not an ASE-owned installer. Check your agent’s compatibility. This copies instructions; complete the upstream tool setup below separately.
At a glance
Tools required
Node.js 20+, npm or Docker, Flowise workspace, model provider credentials
Install & setup
Install with npm install -g flowise, start with npx flowise start, then open http://localhost:3000. For Docker Compose, clone the repository, copy docker/.env.example to .env, and run docker compose up -d from the docker folder.
Author
FlowiseAI
Publisher
Organization
Last updated
Jun 3, 2026
Quick brief

Use Flowise when an operator needs to build an AI agent, tool chain, or retrieval workflow on a visual canvas that a team can inspect before deployment. A user should invoke this instead of using Flowise normally when the agent task is to assemble a concrete flow, connect tools and data sources, test the path, and return an auditable orchestration design. The boundary is visual agent workflow composition and supervised handoff, not a broad product card for every Flowise cloud, hosting, or template feature.

How it works

What this skill actually does

Inputs and prerequisites: Node.js 20+, npm or Docker, Flowise workspace, model provider credentials.

Setup notes: Install with `npm install -g flowise`, start with `npx flowise start`, then open `http://localhost:3000`. For Docker Compose, clone the repository, copy `docker/.env.example` to `.env`, and run `docker compose up -d` from the `docker` folder.

Source and verification boundary: use https://docs.flowiseai.com as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.

Framework fit: publish this as a Multi-Framework workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.