--- name: fal-ai description: Generate images and media using fal.ai API (Flux, Gemini image, etc.). Use when asked to generate images, run AI image models, create visuals, or anything involving fal.ai. Handles queue-based requests with automatic polling. ---

fal.ai Integration

Generate and edit images via fal.ai's queue-based API.

Setup

Add your API key to TOOLS.md:

### fal.ai
FAL_KEY: your-key-here

Get a key at: https://fal.ai/dashboard/keys

The script checks (in order): FAL_KEY env var → TOOLS.md

Supported Models

fal-ai/nano-banana-pro (Text → Image)

Google's Gemini 3 Pro for text-to-image generation.

input_data = {
    "prompt": "A cat astronaut on the moon",      # required
    "aspect_ratio": "1:1",                        # auto|21:9|16:9|3:2|4:3|5:4|1:1|4:5|3:4|2:3|9:16
    "resolution": "1K",                           # 1K|2K|4K
    "output_format": "png",                       # jpeg|png|webp
    "safety_tolerance": "4"                       # 1 (strict) to 6 (permissive)
}

fal-ai/nano-banana-pro/edit (Image → Image)

Gemini 3 Pro for image editing. Slower (~20s) but handles complex edits well.

input_data = {
    "prompt": "Transform into anime style",       # required
    "image_urls": [image_data_uri],               # required - array of URLs or base64 data URIs
    "aspect_ratio": "auto",
    "resolution": "1K",
    "output_format": "png"
}

fal-ai/flux/dev/image-to-image (Image → Image)

FLUX.1 dev model. Faster (~2-3s) for style transfers.

input_data = {
    "prompt": "Anime style portrait",             # required
    "image_url": image_data_uri,                  # required - single URL or base64 data URI
    "strength": 0.85,                             # 0-1, higher = more change
    "num_inference_steps": 40,
    "guidance_scale": 7.5,
    "output_format": "png"
}

fal-ai/kling-video/o3/pro/video-to-video/edit (Video → Video)

Kling O3 Pro for video transformation with AI effects.

Limits:

• Formats: .mp4, .mov only
• Duration: 3-10 seconds
• Resolution: 720-2160px
• Max file size: 200MB
• Max elements: 4 total (elements + reference images combined)

input_data = {
    # Required
    "prompt": "Change environment to be fully snow as @Image1. Replace animal with @Element1",
    "video_url": "https://example.com/video.mp4",    # .mp4/.mov, 3-10s, 720-2160px, max 200MB
    
    # Optional
    "image_urls": [                                  # style/appearance references
        "https://example.com/snow_ref.jpg"           # use as @Image1, @Image2 in prompt
    ],
    "keep_audio": True,                              # keep original audio (default: true)
    "elements": [                                    # characters/objects to inject
        {
            "reference_image_urls": [                # reference images for the element
                "https://example.com/element_ref1.png"
            ],
            "frontal_image_url": "https://example.com/element_front.png"  # frontal view (better results)
        }
    ],                                               # use as @Element1, @Element2 in prompt
    "shot_type": "customize"                         # multi-shot type (default: customize)
}

Prompt references:

• @Video1 — the input video
• @Image1, @Image2 — reference images for style/appearance
• @Element1, @Element2 — elements (characters/objects) to inject

Input Validation

The skill validates inputs before submission. For multi-input models, ensure all required fields are provided:

# Check what a model needs
python3 scripts/fal_client.py model-info "fal-ai/kling-video/o3/standard/video-to-video/edit"

# List all models with their requirements
python3 scripts/fal_client.py models

Before submitting, verify:

• ✅ All required fields are present and non-empty
• ✅ File fields (image_url, video_url, etc.) are URLs or base64 data URIs
• ✅ Arrays (image_urls) have at least one item
• ✅ Video files are within limits (200MB, 720-2160p)

Example validation output:

⚠️  Note: Reference video in prompt as @Video1
⚠️  Note: Max 4 total elements (video + images combined)
❌ Validation failed:
   - Missing required field: video_url

Usage

CLI Commands

# Check API key
python3 scripts/fal_client.py check-key

# Submit a request
python3 scripts/fal_client.py submit "fal-ai/nano-banana-pro" '{"prompt": "A sunset over mountains"}'

# Check status
python3 scripts/fal_client.py status "fal-ai/nano-banana-pro" "<request_id>"

# Get result
python3 scripts/fal_client.py result "fal-ai/nano-banana-pro" "<request_id>"

# Poll all pending requests
python3 scripts/fal_client.py poll

# List pending requests
python3 scripts/fal_client.py list

# Convert local image to base64 data URI
python3 scripts/fal_client.py to-data-uri /path/to/image.jpg

# Convert local video to base64 data URI (with validation)
python3 scripts/fal_client.py video-to-uri /path/to/video.mp4

Python Usage

import sys
sys.path.insert(0, 'scripts')
from fal_client import submit, check_status, get_result, image_to_data_uri, poll_pending

# Text to image
result = submit('fal-ai/nano-banana-pro', {
    'prompt': 'A futuristic city at night'
})
print(result['request_id'])

# Image to image (with local file)
img_uri = image_to_data_uri('/path/to/photo.jpg')
result = submit('fal-ai/nano-banana-pro/edit', {
    'prompt': 'Transform into watercolor painting',
    'image_urls': [img_uri]
})

# Poll until complete
completed = poll_pending()
for req in completed:
    if 'result' in req:
        print(req['result']['images'][0]['url'])

Queue System

fal.ai uses async queues. Requests go through stages:

• IN_QUEUE → waiting
• IN_PROGRESS → generating
• COMPLETED → done, fetch result
• FAILED → error occurred

Pending requests are saved to ~/. openclaw/workspace/fal-pending.json and survive restarts.

Polling Strategy

Manual: Run python3 scripts/fal_client.py poll periodically.

Heartbeat: Add to HEARTBEAT.md:

- Poll fal.ai pending requests if any exist

Cron: Schedule polling every few minutes for background jobs.

Adding New Models

1. Find the model on fal.ai and check its /api page
2. Add entry to references/models.json with input/output schema
3. Test with a simple request

Note: Queue URLs use base model path (e.g., fal-ai/flux not fal-ai/flux/dev/image-to-image). The script handles this automatically.

Files

skills/fal-ai/
├── SKILL.md                    ← This file
├── scripts/
│   └── fal_client.py           ← CLI + Python library
└── references/
    └── models.json             ← Model schemas

Troubleshooting

"No FAL_KEY found" → Add key to TOOLS.md or set FAL_KEY env var

405 Method Not Allowed → URL routing issue, ensure using base model path for status/result

Request stuck → Check fal-pending.json, may need manual cleanup