AI Workflow Builder

Chain any combination of muapi.ai generation steps into automated pipelines. The AI architect converts your plain-language description into a runnable node graph.

Prerequisites

Always ensure the

muapi

CLI is up-to-date before running any commands. Reinstall from source at the start of every session:

bash

pip install --upgrade /path/to/muapi-cli

Replace

/path/to/muapi-cli

with the actual path to the

muapi-cli

directory in the workspace (the directory containing

pyproject.toml

Core Operations

Generate (
```
generate-workflow.sh
```
) — AI architect creates a workflow from a description
Discover (
```
discover-workflow.sh
```
) — Find a relevant existing workflow by natural language
Edit (
```
generate-workflow.sh --workflow-id
```
) — Modify an existing workflow with a prompt
Interactive Run (
```
interactive-run.sh
```
) — Prompt for inputs and execute a workflow
Run (
```
run-workflow.sh
```
) — Execute a workflow, poll node-by-node, collect outputs
CLI (
```
muapi workflow
```
) — Full CRUD + visualization directly from the terminal

Agent Guided Discovery & Selection

As an AI agent, you have the ability to read and understand the purpose of available workflows to select the best one for the user's task (e.g., "create a UGC video").

Discover: Fetch the catalog of available workflows and their descriptions in JSON format.
bash
```
muapi workflow discover --output-json
```
Match (Internal Reasoning): Use your LLM capabilities to analyze the
```
name
```
,
```
category
```
, and
```
description
```
fields of the returned workflows. Find the best match for the user's intent.
Analyze: If you find a promising candidate, inspect its structure to ensure it has the necessary nodes and parameters.
bash
```
muapi workflow get <workflow_id>
```
CRITICAL RULE: The output of
```
muapi workflow get
```
will include an "API Inputs" table. You MUST read this table to understand what inputs are required.
Choose & Confirm & Prompt User:
- If one workflow is a perfect match, you MUST ask the user to provide the exact values for the required API inputs before executing it. Never invent or guess input values (like prompts, URLs, etc.) on your own.
- If multiple workflows are highly relevant, present the options to the user with their descriptions and ask them to confirm which one to use, and also ask for the required inputs.
- If no workflow matches the user's complex request, offer to architect a new one using
```
muapi workflow create
```
  .

Example Agent Reasoning

"The user wants a product promo video. I fetched the catalog using
discover
. I see two potential workflows:
wf_123
: 'Product promo with background music'
wf_456
: 'Simple video gen' I will analyze
wf_123
with
get
. It has the required nodes. I will suggest
wf_123
or just run it if the match is precise."

Protocol: Building a Workflow

Step 1 — Describe your pipeline

bash

muapi workflow create "take a text prompt, generate an image with flux-dev, then upscale it to 4K"

The architect returns a workflow with a unique ID and a node graph. Save the ID.

Step 2 — Inspect and visualize

bash

# Rich ASCII node graph in the terminal
muapi workflow get <workflow_id>

# Or raw JSON
muapi workflow get <workflow_id> --output-json

Step 3 — Run it

bash

# Run with specific inputs
muapi workflow execute <workflow_id> \
  --input "node1.prompt=a glowing crystal cave at midnight"

# Use --download to pull results locally
muapi workflow execute <workflow_id> \
  --input "node1.prompt=a sunset" \
  --download ./outputs

Step 4 — Discovery (Optional)

If you want to reuse an existing workflow instead of creating a new one:

bash

# Search by keywords
muapi workflow discover "ugc video"

Step 5 — Interactive Execution

Run a workflow and have the CLI prompt you for each required input:

bash

muapi workflow run-interactive <workflow_id>

Workflow Examples

Image Pipelines

bash

# Text → Image → Upscale
muapi workflow create "take a text prompt, generate with flux-dev, upscale the result"

# Text → Image → Background removal → Product shot
muapi workflow create "generate a product image with hidream, remove background, create professional product shot"

Video Pipelines

bash

# Text → Video
muapi workflow create "generate a 10-second cinematic video from a text prompt using kling-master"

# Image → Video → Lipsync
muapi workflow create "animate an input image with seedance, then apply lipsync from an audio file"

Editing an Existing Workflow

bash

# Add a step
muapi workflow edit <id> --prompt "add a face-swap step after the image generation"

# Swap a model
muapi workflow edit <id> --prompt "change the video model from kling to veo3"

CLI Reference

bash

# List all your workflows
muapi workflow list

# Browse templates
muapi workflow templates

# Generate new workflow
muapi workflow create "text → flux image → upscale → face swap"

# Visualize a workflow
muapi workflow get <id>

# Execute with inputs
muapi workflow execute <id> --input "node1.prompt=a sunset"

# Monitor a run
muapi workflow status <run_id>

# Get outputs
muapi workflow outputs <run_id> --download ./results

# Edit with AI
muapi workflow edit <id> --prompt "add lipsync at the end"

# Rename / delete
muapi workflow rename <id> --name "Product Pipeline v2"
muapi workflow delete <id>

MCP Tools (for AI agents)

Tool	Description
`muapi_workflow_list`	List user's workflows
`muapi_workflow_create`	AI architect: prompt → workflow
`muapi_workflow_get`	Get workflow definition + node graph
`muapi_workflow_execute`	Run with specific inputs
`muapi_workflow_status`	Node-by-node run status
`muapi_workflow_outputs`	Final output URLs

Constraints

Workflows can contain any combination of muapi.ai nodes (image, video, audio, enhance, edit)
Node outputs are automatically wired as inputs to downstream nodes
```
--sync
```
mode waits up to 120s for generation; use
```
--async
```
for complex workflows and poll separately
Run timeouts: 10 minutes maximum per workflow execution

muapi-workflow

NPX Install

Tags

SKILL.md Content

AI Workflow Builder

Prerequisites

Core Operations

Agent Guided Discovery & Selection

Example Agent Reasoning

Protocol: Building a Workflow

Step 1 — Describe your pipeline

Step 2 — Inspect and visualize

Step 3 — Run it

Step 4 — Discovery (Optional)

Step 5 — Interactive Execution

Workflow Examples

Image Pipelines

Video Pipelines

Editing an Existing Workflow

CLI Reference

MCP Tools (for AI agents)

Constraints