Skytells Python SDK

Run AI models with a single function call.

Official Python SDK for the Skytells AI platform. Run image, video, audio, music, text, and code models with a single function call.

Zero dependencies — uses Python's stdlib urllib for HTTP.

pip install skytells

Quick Start

First, you'll need to get an API key from the Skytells Dashboard.

Then, you can use the SDK to run a model and get the output.

from skytells import SkytellsClient

client = SkytellsClient("sk-your-api-key")

# Run a model and get output
prediction = client.run("truefusion", input={"prompt": "An astronaut riding a rainbow unicorn"})
print(prediction.output)  # "https://..."

# Clean up when done
prediction.delete()

Generating a song using Beatfusion:

prediction = client.run("beatfusion-2.0", input={
    "prompt": "Rap, hip-hop",
    "lyrics": "Let me introduce the voice you hear, Beatfusion by Skytells making it clear.."
})
print(prediction.output)

Browse the full model catalog at skytells.ai/explore/models.

Skytells is committed to the responsible and ethical use of AI. All models are subject to our usage policies and comply with applicable laws and regulations. Learn more at learn.skytells.ai.

Installation

pip install skytells

Requires Python 3.8+. No external dependencies.

Usage

Client Configuration

from skytells import SkytellsClient

client = SkytellsClient(
    "sk-your-api-key",
    base_url="https://api.skytells.ai/v1",   # optional override
    timeout=30000,                             # ms (default: 60000)
    headers={"X-Custom-Header": "value"},      # extra headers
    retry={"retries": 3, "retry_delay": 1000}, # retry config
)

Run a Model

prediction = client.run("truefusion", input={"prompt": "A sunset over mountains"})
print(prediction.output)   # str or list[str]
print(prediction.id)
print(prediction.status)

Progress Tracking

def on_progress(p):
    print(f"Status: {p['status']}, Progress: {p.get('metrics', {}).get('progress', 'n/a')}")

prediction = client.run(
    "truefusion",
    input={"prompt": "A cat"},
    on_progress=on_progress,
)

Background Predictions

# Create without waiting
response = client.predictions.create({
    "model": "truefusion-ultra",
    "input": {"prompt": "A dog"},
})
print(response["id"], response["status"])  # "pending"

# Wait for completion
result = client.wait(response)
print(result["output"])

# Or poll manually
result = client.predictions.get(response["id"])

Queue & Dispatch

client.queue({"model": "truefusion-ultra", "input": {"prompt": "Cat"}})
client.queue({"model": "flux-pro", "input": {"prompt": "Dog"}})
client.queue({"model": "flux-edge", "input": {"prompt": "Bird"}})

results = client.dispatch()
for pred in results:
    print(pred["id"], pred["status"])

Prediction Lifecycle

# Cancel a running prediction
client.cancel_prediction("pred_abc123")

# Delete a prediction and its assets
client.delete_prediction("pred_abc123")

# Stream endpoint
stream_info = client.stream_prediction("pred_abc123")
print(stream_info["urls"]["stream"])

Models API

# List all models
models = client.models.list()
for model in models:
    print(model["name"], model["type"])

# With schemas
models = client.models.list(fields=["input_schema"])

# Fetch a single model
model = client.models.get("flux-pro")
print(model["name"], model["pricing"])

model = client.models.get("flux-pro", fields=["input_schema", "output_schema"])
print(model["input_schema"])

Predictions API

# List predictions with filters
result = client.predictions.list(model="flux-pro", since="2026-01-01", page=2)
for pred in result["data"]:
    print(pred["id"], pred["status"])

print(result["pagination"])

The Prediction Object

client.run() returns a Prediction instance:

Attribute / Method	Description
prediction.id	Unique prediction ID
prediction.status	Current status string
prediction.output	Raw output (str, list[str], or None)
prediction.outputs()	Normalised output (unwraps single-element arrays)
prediction.raw()	Full response dict
prediction.cancel()	Cancel the prediction
prediction.delete()	Delete the prediction and its assets
prediction.response	Full response dict (same as raw())

outputs() behaviour

output value	outputs() returns
None	None
"https://..."	"https://..."
["https://..."]	"https://..." (unwrapped)
["a", "b"]	["a", "b"]

Async Client

import asyncio
from skytells import AsyncSkytellsClient

async def main():
    client = AsyncSkytellsClient("sk-your-api-key")
    prediction = await client.run("flux-pro", input={"prompt": "A cat"})
    print(prediction.output)
    await prediction.delete()

asyncio.run(main())

Error Handling

from skytells import SkytellsError

try:
    prediction = client.run("flux-pro", input={"prompt": "test"})
except SkytellsError as e:
    print(e.error_id)    # e.g. "UNAUTHORIZED", "MODEL_NOT_FOUND"
    print(e.http_status) # e.g. 401, 404
    print(e.details)     # human-readable detail string
    print(str(e))        # error message

Error IDs

Error ID	Source	Meaning
UNAUTHORIZED	API	Invalid or missing API key
INVALID_PARAMETER	API	A request parameter failed validation
INVALID_DATE_FORMAT	API	Date string is not YYYY-MM-DD
INVALID_DATE_RANGE	API	since is after until
MODEL_NOT_FOUND	API	Model slug does not exist
INTERNAL_ERROR	API	Unexpected server-side error
INVALID_INPUT	API	Model input validation failed
INSUFFICIENT_CREDITS	API	Account has no credits
ACCOUNT_SUSPENDED	API	Account has been suspended
PAYMENT_REQUIRED	API	Payment is required to continue
SECURITY_VIOLATION	API	Request violated a security policy
RATE_LIMIT_EXCEEDED	API	Too many requests
INFRASTRUCTURE_ERROR	API	Platform infrastructure failure
PREDICTION_FAILED	Client	Prediction completed with a failure status
WAIT_TIMEOUT	Client	wait() exceeded max_wait
REQUEST_TIMEOUT	Client	HTTP request timed out
NETWORK_ERROR	Client	Network-level failure (no HTTP response)
SERVER_ERROR	Client	Non-JSON or unexpected 5xx response
INVALID_JSON	Client	Response body could not be parsed as JSON

For a full reference of API-level and prediction-level errors, see the Skytells Error Reference.

Client Options

Option	Type	Default	Description
api_key	str	None	Your Skytells API key (sk-...)
base_url	str	https://api.skytells.ai/v1	API base URL
timeout	int	60000	Request timeout (ms)
headers	dict	{}	Extra headers for every request
retry	dict	{}	Retry config (retries, retry_delay, retry_on)

Type Reference

All types are importable from skytells.types:

from skytells.types import (
    PredictionStatus,
    PredictionType,
    PredictionSource,
    ModelType,
    ModelPrivacy,
    PricingUnit,
    ApiErrorId,
)

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License

MIT — see LICENSE.