Fal Ai

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

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

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

Find the model on fal.ai and check its /api page Add entry to references/models.json with input/output schema 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