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How to turn a plain-language idea into a runnable AI workflow with Loom Flow

Describe a multi-step AI process in one sentence and get a workflow on the canvas you can run.

Short answer: Run git clone https://github.com/banmu123/LoomFlow.git && cd LoomFlow && bash scripts/init-env.sh && docker compose up -d in a terminal; then open http://localhost:5000, sign in as admin with the default password, and change the password right away; go to Admin, then Model Settings, then Add Model, and enter your model ID, base URL, and API key. All steps.

Credentials. The default admin login is admin with password 123456, so change it on first sign-in and before exposing the server.

Who it is forAn indie developer or content creator who has a recurring AI task in mind but does not want to hand-build nodes first.

The problemSetting up an AI pipeline usually means learning a visual tool, placing nodes, and wiring them before anything runs.

The outcomeA generated workflow on the canvas, tuned by hand and executed once with a visible result.

What you need first

  • A machine with Docker and Docker Compose installed, plus git and a terminal (the README says 1 GB RAM is enough)
  • An API key for an LLM provider such as DeepSeek, or any OpenAI-compatible endpoint, added later through the admin UI
  • A web browser to open http://localhost:5000

Steps

  1. Run git clone https://github.com/banmu123/LoomFlow.git && cd LoomFlow && bash scripts/init-env.sh && docker compose up -d in a terminal
  2. Open http://localhost:5000, sign in as admin with the default password, and change the password right away
  3. Go to Admin, then Model Settings, then Add Model, and enter your model ID, base URL, and API key
  4. In the chat, describe your process in plain language, for example: input a product name, generate selling-point copy, then generate a promo video script
  5. Review the generated nodes on the visual canvas, adjust parameters, and run the workflow

Prompt for your agent

Set up LoomFlow from https://github.com/banmu123/LoomFlow using `bash scripts/init-env.sh` and `docker compose up -d`, then describe the process <process description> in the chat so it generates a workflow. Expected output: a workflow on the canvas with Start and End nodes and the steps from the description. Verify that http://localhost:5000 loads and that the generated workflow appears on the canvas before you report success.

Caveat. The README says the model is added in the admin UI and not through environment keys, so you need your own provider API key before the generated workflow can run.

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