fal-ai

name	fal-ai
description	fal.ai API integration with managed API key authentication. Run AI models for image generation, video generation, audio processing, and more. Use this skill when users want to generate images (Flux, SDXL), create videos (Minimax), upscale images, transcribe audio, or run other AI models on fal.ai. For other third party apps, use the api-gateway skill (https://clawhub.ai/byungkyu/api-gateway).

fal.ai

Access the fal.ai queue API with managed API key authentication. Run 1000+ AI models including image generation (Flux, SDXL), video generation (Minimax), image upscaling, text-to-speech, and more.

Quick Start

# Generate an image with Flux Schnell
python <<'EOF'
import urllib.request, os, json

data = json.dumps({
    "prompt": "a tiny cute cat",
    "image_size": "square_hd",
    "num_images": 1
}).encode()

req = urllib.request.Request('https://gateway.maton.ai/fal-ai/fal-ai/flux/schnell', data=data, method='POST')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Content-Type', 'application/json')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF

Base URL

https://gateway.maton.ai/fal-ai/{native-api-path}

The gateway proxies requests to queue.fal.run. For model inference, paths follow the pattern:

/fal-ai/fal-ai/{model-id}
/fal-ai/fal-ai/{model-id}/requests/{request_id}/status
/fal-ai/fal-ai/{model-id}/requests/{request_id}
/fal-ai/fal-ai/{model-id}/requests/{request_id}/cancel

Authentication

All requests require the Maton API key in the Authorization header:

Authorization: Bearer $MATON_API_KEY

Environment Variable: Set your API key as MATON_API_KEY:

export MATON_API_KEY="YOUR_API_KEY"

Getting Your API Key

1. Sign in or create an account at maton.ai
2. Go to maton.ai/settings
3. Copy your API key

Connection Management

Manage your fal.ai API key connections at https://ctrl.maton.ai.

List Connections

python <<'EOF'
import urllib.request, os, json
req = urllib.request.Request('https://ctrl.maton.ai/connections?app=fal-ai&status=ACTIVE')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF

Create Connection

python <<'EOF'
import urllib.request, os, json
data = json.dumps({'app': 'fal-ai'}).encode()
req = urllib.request.Request('https://ctrl.maton.ai/connections', data=data, method='POST')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Content-Type', 'application/json')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF

Response:

{
  "connection": {
    "connection_id": "7355bd0b-8aaf-4c58-9122-a1e3d454414d",
    "status": "PENDING",
    "url": "https://connect.maton.ai/?session_token=...",
    "app": "fal-ai",
    "method": "API_KEY"
  }
}

Open the returned url in a browser to enter your fal.ai API key.

Get Connection

python <<'EOF'
import urllib.request, os, json
req = urllib.request.Request('https://ctrl.maton.ai/connections/{connection_id}')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF

Delete Connection

python <<'EOF'
import urllib.request, os, json
req = urllib.request.Request('https://ctrl.maton.ai/connections/{connection_id}', method='DELETE')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF

API Reference

Queue API

The fal.ai queue API provides asynchronous model inference with status polling.

Submit Request

Submit a request to run a model. Returns immediately with a request ID.

POST /fal-ai/fal-ai/{model-id}
Content-Type: application/json

{
  "prompt": "model-specific parameters",
  ...
}

Response:

{
  "status": "IN_QUEUE",
  "request_id": "3229f185-a99a-48c0-a292-e25bf9baaeba",
  "response_url": "https://queue.fal.run/fal-ai/flux/requests/3229f185-a99a-48c0-a292-e25bf9baaeba",
  "status_url": "https://queue.fal.run/fal-ai/flux/requests/3229f185-a99a-48c0-a292-e25bf9baaeba/status",
  "cancel_url": "https://queue.fal.run/fal-ai/flux/requests/3229f185-a99a-48c0-a292-e25bf9baaeba/cancel",
  "queue_position": 0
}

Check Status

Poll for request status until completion.

GET /fal-ai/fal-ai/{model-id}/requests/{request_id}/status

Response (IN_PROGRESS):

{
  "status": "IN_PROGRESS",
  "request_id": "3229f185-a99a-48c0-a292-e25bf9baaeba"
}

Response (COMPLETED):

{
  "status": "COMPLETED",
  "request_id": "3229f185-a99a-48c0-a292-e25bf9baaeba",
  "metrics": {
    "inference_time": 0.3334658145904541
  }
}

Get Result

Retrieve the completed result.

GET /fal-ai/fal-ai/{model-id}/requests/{request_id}

Response (image generation):

{
  "images": [
    {
      "url": "https://v3b.fal.media/files/...",
      "width": 1024,
      "height": 1024,
      "content_type": "image/jpeg"
    }
  ],
  "timings": {
    "inference": 0.1587670766748488
  },
  "seed": 761506470,
  "prompt": "a tiny cute cat"
}

Cancel Request

Cancel a queued or in-progress request.

PUT /fal-ai/fal-ai/{model-id}/requests/{request_id}/cancel

Popular Models

Flux Schnell (Fast Image Generation)

python <<'EOF'
import urllib.request, os, json

data = json.dumps({
    "prompt": "a serene mountain landscape at sunset",
    "image_size": "landscape_16_9",
    "num_images": 1,
    "num_inference_steps": 4
}).encode()

req = urllib.request.Request('https://gateway.maton.ai/fal-ai/fal-ai/flux/schnell', data=data, method='POST')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Content-Type', 'application/json')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF

Parameters:

• prompt (required): Text description of the image
• image_size: square_hd, square, portrait_4_3, portrait_16_9, landscape_4_3, landscape_16_9
• num_images: Number of images to generate (default: 1)
• num_inference_steps: Number of steps (default: 4)
• seed: Random seed for reproducibility

Fast SDXL (Stable Diffusion XL)

python <<'EOF'
import urllib.request, os, json

data = json.dumps({
    "prompt": "a futuristic city skyline at night",
    "negative_prompt": "blurry, low quality",
    "image_size": "landscape_16_9",
    "num_images": 1
}).encode()

req = urllib.request.Request('https://gateway.maton.ai/fal-ai/fal-ai/fast-sdxl', data=data, method='POST')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Content-Type', 'application/json')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF

Parameters:

• prompt (required): Text description
• negative_prompt: What to avoid in the image
• image_size: Output dimensions
• num_images: Number of images
• guidance_scale: CFG scale (default: 7.5)
• num_inference_steps: Number of steps

Clarity Upscaler (Image Upscaling)

python <<'EOF'
import urllib.request, os, json

data = json.dumps({
    "image_url": "https://example.com/image.jpg",
    "scale": 2
}).encode()

req = urllib.request.Request('https://gateway.maton.ai/fal-ai/fal-ai/clarity-upscaler', data=data, method='POST')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Content-Type', 'application/json')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF

Parameters:

• image_url (required): URL of the image to upscale
• scale: Upscale factor (2, 4)

Minimax Video Generation

python <<'EOF'
import urllib.request, os, json

data = json.dumps({
    "prompt": "A cat playing with a ball in slow motion"
}).encode()

req = urllib.request.Request('https://gateway.maton.ai/fal-ai/fal-ai/minimax/video-01', data=data, method='POST')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Content-Type', 'application/json')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF

F5-TTS (Text-to-Speech)

python <<'EOF'
import urllib.request, os, json

data = json.dumps({
    "gen_text": "Hello world, this is a test of fal ai text to speech."
}).encode()

req = urllib.request.Request('https://gateway.maton.ai/fal-ai/fal-ai/f5-tts', data=data, method='POST')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Content-Type', 'application/json')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF

Request Status Values

Status	Description
IN_QUEUE	Request received, waiting for runner
IN_PROGRESS	Model is processing the request
COMPLETED	Processing finished, result available
FAILED	Processing failed (check error details)

Request Headers

Header	Description
X-Fal-Request-Timeout	Server-side deadline in seconds
X-Fal-Runner-Hint	Session affinity for routing
X-Fal-Queue-Priority	normal (default) or low
X-Fal-No-Retry	Disable automatic retries

Complete Workflow Example

python <<'EOF'
import urllib.request, os, json, time

api_key = os.environ["MATON_API_KEY"]
base_url = "https://gateway.maton.ai/fal-ai"

# 1. Submit request
data = json.dumps({
    "prompt": "a beautiful sunset over the ocean",
    "image_size": "landscape_16_9",
    "num_images": 1
}).encode()

req = urllib.request.Request(f'{base_url}/fal-ai/flux/schnell', data=data, method='POST')
req.add_header('Authorization', f'Bearer {api_key}')
req.add_header('Content-Type', 'application/json')
submit_response = json.load(urllib.request.urlopen(req))
request_id = submit_response['request_id']
print(f"Submitted: {request_id}")

# 2. Poll for completion
while True:
    req = urllib.request.Request(f'{base_url}/fal-ai/flux/requests/{request_id}/status')
    req.add_header('Authorization', f'Bearer {api_key}')
    status_response = json.load(urllib.request.urlopen(req))
    print(f"Status: {status_response['status']}")

    if status_response['status'] == 'COMPLETED':
        break
    elif status_response['status'] == 'FAILED':
        print("Request failed")
        exit(1)

    time.sleep(1)

# 3. Get result
req = urllib.request.Request(f'{base_url}/fal-ai/flux/requests/{request_id}')
req.add_header('Authorization', f'Bearer {api_key}')
result = json.load(urllib.request.urlopen(req))
(f)
EOF

Code Examples

JavaScript

const submitRequest = async () => {
  // Submit
  const submitRes = await fetch('https://gateway.maton.ai/fal-ai/fal-ai/flux/schnell', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': `Bearer ${process.env.MATON_API_KEY}`
    },
    body: JSON.stringify({
      prompt: 'a tiny cute cat',
      image_size: 'square_hd',
      num_images: 1
    })
  });
  const { request_id } = await submitRes.json();

  // Poll
  let status = 'IN_QUEUE';
  while (status !== 'COMPLETED') {
    await new Promise(r => setTimeout(r, 1000));
    const statusRes = await fetch(
      `https://gateway.maton.ai/fal-ai/fal-ai/flux/requests/${request_id}/status`,
      { headers: { 'Authorization': `Bearer ${process.env.MATON_API_KEY}` } }
    );
    status = ( statusRes.()).;
  }

  
   resultRes =  (
    ,
    { : { :  } }
  );
    resultRes.();
};

Python (requests)

import os
import time
import requests

api_key = os.environ["MATON_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}"}

# Submit
response = requests.post(
    "https://gateway.maton.ai/fal-ai/fal-ai/flux/schnell",
    headers=headers,
    json={"prompt": "a tiny cute cat", "image_size": "square_hd", "num_images": 1}
)
request_id = response.json()["request_id"]

# Poll
while True:
    status = requests.get(
        f"https://gateway.maton.ai/fal-ai/fal-ai/flux/requests/{request_id}/status",
        headers=headers
    ).json()["status"]
    if status == "COMPLETED":
        break
    time.sleep(1)

# Get result
result = requests.get(
    f"https://gateway.maton.ai/fal-ai/fal-ai/flux/requests/{request_id}",
    headers=headers
).json()
print(result["images"][0]["url"])

Notes

• The gateway proxies to queue.fal.run for model inference
• All model requests are queued - poll for status until completion
• Model parameters vary by model - check fal.ai documentation for specifics
• Image URLs from fal.ai CDN are temporary - download or store them
• Video generation models may take longer to complete
• Use webhooks for long-running tasks (add ?fal_webhook=URL to submit request)
• IMPORTANT: When piping curl output to jq, environment variables may not expand correctly. Use Python examples instead.

Error Handling

Status	Meaning
400	Missing fal-ai connection or invalid request
401	Invalid or missing Maton API key
422	Invalid model parameters
429	Rate limited
4xx/5xx	Passthrough error from fal.ai API

Troubleshooting

1. Check connection exists:

python <<'EOF'
import urllib.request, os, json
req = urllib.request.Request('https://ctrl.maton.ai/connections?app=fal-ai&status=ACTIVE')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF

2. Verify path format: Paths must start with /fal-ai/fal-ai/{model-id}

3. Check model exists: Some model IDs include organization prefix (e.g., fal-ai/flux/schnell)

Resources

• fal.ai Documentation
• Model Gallery
• Queue API Reference
• Maton Community
• Maton Support