fal-workflow

name	fal-workflow
description	Author and execute multi-step media pipelines on fal.ai. Two modes: (A) declarative workflow JSON files for the fal.ai workflow runtime ("create workflow", "chain models", "image to video pipeline"); (B) imperative genmedia CLI orchestration when scripting locally ("design pipeline", "fan-out generation", "frame bridging", "narrated video", "dataset generation").

fal.ai Workflows: Two Modes

Runtime: Two modes. Mode A authors .json files for the fal.ai workflow runtime (executed in the cloud, no local CLI needed). Mode B drives the genmedia CLI for local orchestration. See the genmedia skill for Mode B command syntax; run genmedia init once if not yet installed.

This skill covers two complementary ways to build multi-step media pipelines on fal.ai. They share endpoints and concepts but differ in deliverable.

Mode	Deliverable	When to use
A. Workflow JSON	A portable .json file the fal.ai workflow runtime executes	The user wants a reusable, shareable workflow asset; the pipeline graph is fixed at design time
B. genmedia CLI orchestration	A sequence of genmedia run / status / upload calls (often scripted)	Local scripting, exploratory pipelines, dataset jobs, branching logic, or pipelines that need conditional steps

If unsure: prefer Mode A when the result is a deliverable for someone else to run; Mode B when you are running it yourself or steps need runtime decisions.

---

Mode A: Workflow JSON authoring

Generate 100% working, production-ready fal.ai workflow JSON files. Workflows chain multiple AI models together via a declarative graph.

JSON-mode references:

• MODELS.md, model configurations for JSON nodes
• PATTERNS.md, reusable JSON workflow patterns
• EXAMPLES.md, code snippets and partial examples
• WORKFLOWS.md, full JSON workflows (debugging reference; use only when user reports errors)

Mode B: genmedia CLI orchestration

Plan and execute a sequence of genmedia calls with clear inputs, outputs, dependencies, and quality checks. Use this when a single model call is not enough and the orchestration happens in your shell, not on fal.ai.

CLI-mode references:

• pipeline-patterns.md, fan-out, sequential composition, frame bridging, multi-modal assembly, variation matrices
• node-rules.md, per-role rules (planner / generator / editor / utility / QA / manifest)
• utility-endpoints.md, utility endpoint catalog (resize, composite, mask, audio, subtitle, etc.)
• recipes.md, end-to-end recipes (cinematic video, product campaign, character continuity, narrated documentary, dataset, social batch)

For default endpoint choices in Mode B, consult fal-models-catalog. Always run genmedia schema <endpoint_id> --json before executing and genmedia pricing <endpoint_id> --json when cost matters.

---

Mode A: Workflow JSON authoring (continued)

Core Architecture

Valid Node Types

⚠️ ONLY TWO VALID NODE TYPES EXIST:

Type	Purpose
"run"	Execute a model/app
"display"	Output results to user

❌ INVALID: type: "input" - This does NOT exist! Input is defined ONLY in schema.input.

Minimal Working Example

{
 "name": "my-workflow",
 "title": "My Workflow",
 "contents": {
 "name": "workflow",
 "nodes": {
 "output": {
 "type": "display",
 "id": "output",
 "depends": ["node-image"],
 "input": {},
 "fields": { "image": "$node-image.images.0.url" }
 },
 "node-image": {
 "type": "run",
 "id": "node-image",
 "depends": ["input"],
 "app": "fal-ai/flux/dev",
 "input": { "prompt": "$input.prompt" }
 }
 },
 "output": { "image": "$node-image.images.0.url" },
 "schema": {
 "input": {
 "prompt": {
 "name": "prompt",
 "label": "Prompt",
 "type": "string",
 "required": true,
 "modelId": "node-image"
 }
 },
 "output": {
 "image": { "name": "image", "label": "Generated Image", "type": "string" }
 }
 },
 "version": "1",
 "metadata": {
 "input": { "position": { "x": 0, "y": 0 } },
 "description": "Simple text to image workflow"
 }
 },
 "is_public": true,
 "user_id": "",
 "user_nickname": "",
 "created_at": ""
}

Reference Syntax

Reference	Use Case	Example
$input.field	Input value	$input.prompt
$node.output	LLM text output	$node-llm.output
$node.images.0.url	First image URL	$node-img.images.0.url
$node.image.url	Single image URL	$node-upscale.image.url
$node.video.url	Video URL	$node-vid.video.url
$node.audio_file.url	Audio URL	$node-music.audio_file.url
$node.frame.url	Extracted frame	$node-extract.frame.url

CRITICAL: No String Interpolation

⚠️ NEVER mix text with variables! Variable MUST be the ENTIRE value.

// ❌ WRONG - WILL BREAK
"prompt": "Create image of $input.subject in $input.style"

// ✅ CORRECT - Variable is the ENTIRE value
"prompt": "$input.prompt"
"prompt": "$node-llm.output"

To combine values: Use fal-ai/text-concat or fal-ai/workflow-utilities/merge-text. See Model Reference.

---

Critical Rules

C1: Dependencies Must Match References

// ❌ WRONG
"node-b": {
 "depends": [],
 "input": { "data": "$node-a.output" }
}

// ✅ CORRECT
"node-b": {
 "depends": ["node-a"],
 "input": { "data": "$node-a.output" }
}

C2: ID Must Match Object Key

// ❌ WRONG
"my-node": { "id": "different-id" }

// ✅ CORRECT
"my-node": { "id": "my-node" }

C3: Use Correct LLM Type

• openrouter/router → Text only, no image_urls
• openrouter/router/vision → ONLY when analyzing images

C4: Schema modelId Required

"schema": {
 "input": {
 "field": { "modelId": "first-consuming-node" }
 }
}

C5: Output Depends on All Referenced Nodes

"output": {
 "depends": ["node-a", "node-b", "node-c"],
 "fields": {
 "a": "$node-a.video",
 "b": "$node-b.images.0.url"
 }
}

---

---

Quick Reference Card

Output References

Model Type	Output Reference
LLM	$node.output
Text Concat	$node.results
Merge Text	$node.text
Image Gen (array)	$node.images.0.url
Image Process (single)	$node.image.url
Video	$node.video.url
Music	$node.audio_file.url
Frame Extract	$node.frame.url

Use genmedia models "<query>" --json or genmedia models --category <cat> --json to discover current models. See references/MODELS.md for workflow code templates.

---

Input Schema

"schema": {
 "input": {
 "text_field": {
 "name": "text_field",
 "label": "Display Label",
 "type": "string",
 "description": "Help text",
 "required": true,
 "modelId": "consuming-node"
 },
 "image_urls": {
 "name": "image_urls",
 "type": { "kind": "list", "elementType": "string" },
 "required": true,
 "modelId": "node-id"
 }
 }
}

---

Pre-Output Checklist

Before outputting any workflow, verify:

• ⚠️ All nodes have type: "run" or type: "display" ONLY (NO type: "input"!)
• ⚠️ No string interpolation - variable MUST be ENTIRE value
• Every $node.xxx has matching depends entry
• Every node id matches object key
• Input schema has modelId for each field
• Output depends on ALL referenced nodes
• Correct LLM type (router vs router/vision)

---

Authoring a workflow JSON

Author the JSON file by hand following the structure shown above. There is no script wrapper; the agent writes the file directly. Validate before delivery:

1. Every node id matches its object key.
2. Every $node.xxx reference appears in depends.
3. No string interpolation; variables are entire values.
4. Schema input has modelId for each field.
5. Output node depends includes every node it references.

For each model used, inspect the schema first:

genmedia schema <endpoint_id> --json

Then write the corresponding input block in the workflow JSON.

---

Troubleshooting

Invalid Node Type Error (MOST COMMON)

Error: unexpected value; permitted: 'run', 'display', field required

Cause: You created a node with type: "input" which does NOT exist. Solution: Remove ANY node with type: "input". Define input fields ONLY in schema.input.

Dependency Error

Error: Node references $node-x but doesn't depend on it

Solution: Add the referenced node to the depends array.

ID Mismatch Error

Error: Node key "my-node" doesn't match id "different-id"

Solution: Ensure the object key matches the id field exactly.

LLM Vision Error

Error: image_urls provided but using text-only router

Solution: Switch to openrouter/router/vision when analyzing images.

---

Finding Model Schemas

Every model's input/output schema:

https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=[endpoint_id]

Example:

https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=fal-ai/nano-banana-pro

---

Mode B: genmedia CLI orchestration

When the deliverable is local execution rather than a portable JSON file, plan a pipeline of genmedia calls.

Inputs to collect

Ask only for missing information that changes the pipeline:

• Final deliverable: image set, video, clips, audio, subtitles, dataset, social batch, product campaign, storyboard, style exploration.
• Source assets: product images, character references, first frames, video, audio, logo, transcript, brand guide.
• Runtime limits: quality target, cost sensitivity, number of variants, duration, aspect ratios, deadline.
• Continuity requirements: product identity, character face, scene layout, voice, color grade.
• Model preference: ask only when quality/speed/cost tradeoffs are not clear from the brief.

Core CLI workflow

1. Write a short pipeline graph before running anything.

input assets -> planner -> generation nodes -> utility nodes -> QA -> final outputs

2. Resolve endpoints for each role. Check known endpoint IDs first via fal-models-catalog.

genmedia models --endpoint_id <endpoint_id> --json

Use text search only as fallback discovery for roles not covered:

genmedia models "image generation product photography" --json
genmedia docs "fal.ai workflow utility endpoints" --json

3. Inspect every endpoint before use.

genmedia schema <endpoint_id> --json
genmedia pricing <endpoint_id> --json

4. Upload local files once and reuse returned URLs.

genmedia upload ./input.png --json

5. Run each node with JSON output. Use --async for slow generation; poll with genmedia status.

genmedia run <endpoint_id> --<field> "<value>" --json
genmedia run <endpoint_id> --<field> "<value>" --async --json
genmedia status <endpoint_id> <request_id> --download "./outputs/workflow/{request_id}_{index}.{ext}" --json

6. For downstream nodes, pass the media URL from the previous result. Only upload local intermediate files when no URL is available.

7. Download final assets with templates that cannot collide:

--download "./outputs/workflow/{request_id}_{index}.{ext}"

8. Return a compact manifest (see node-rules.md for the manifest schema).

CLI pipeline rules

• One node, one transformation.
• Fan out independent generation / crop / upscale / subtitle / variation lanes.
• Sequential chains only when node B genuinely needs node A output.
• Prefer reference / edit / image-to-video over independent text-only generations when continuity matters.
• Use utility endpoints for deterministic work (crop, resize, grid, composite, audio merge, subtitle, speed change, compression).
• Record endpoint, schema-relevant parameters, request ID, and output path for every node.
• On 422 errors: read validation_errors, re-inspect schema, fix the exact field.

CLI quality gate

Before returning, verify:

• The pipeline graph matches the requested deliverable.
• No generation model was chosen from memory alone.
• All local source files were uploaded before use.
• Final files were saved through --download.
• Utility endpoints used exact schema fields.
• Continuity anchors were repeated where identity or product fidelity matters.
• Each node output is either accepted, retried, or marked with a defect.

If the workflow becomes too complex, stop expanding and ask the user to choose between faster iteration, higher fidelity, or broader variation.