Python SDK

The official Python SDK for Runcrate. Supports both synchronous and asynchronous usage with full type safety via Pydantic.

Installation

pip install runcrate-sdk

Requires Python 3.9+.

Quick Start

from runcrate import Runcrate

client = Runcrate(api_key="rc_live_YOUR_API_KEY")

# Chat completion
response = client.models.chat_completion(
    model="deepseek-ai/DeepSeek-V3",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

# List GPU instances
instances = client.instances.list()
for inst in instances:
    print(f"{inst.name} — {inst.status}")

client.close()

Or use as a context manager:

with Runcrate(api_key="rc_live_YOUR_API_KEY") as client:
    balance = client.billing.get_balance()
    print(f"Credits: ${balance.credits_balance}")

Configuration

client = Runcrate(
    api_key="rc_live_...",                    # or RUNCRATE_API_KEY env var
    base_url="https://runcrate.ai",           # infrastructure API
    inference_url="https://api.runcrate.ai",  # model inference API
    timeout=30,                               # seconds
    max_retries=3,                            # retry on 429/5xx
    environment="production",                 # optional — target a specific environment
)

Environments

API keys are workspace-scoped. By default, requests target the workspace’s default environment (usually main). To target a different environment, pass environment at client construction:

# Default environment (e.g., main)
client = Runcrate(api_key="rc_live_...")

# Specific environment
staging = Runcrate(api_key="rc_live_...", environment="staging")
prod    = Runcrate(api_key="rc_live_...", environment="production")

# Each client only sees resources in its own environment
staging.instances.list()   # only staging instances
prod.instances.list()      # only production instances

What’s environment-scoped: instances, crates, storage volumes. What’s workspace-wide: SSH keys, billing, API keys, templates.

Async Support

from runcrate import AsyncRuncrate

async with AsyncRuncrate(api_key="rc_live_...") as client:
    instances = await client.instances.list()
    balance = await client.billing.get_balance()

Every method available on Runcrate has an async equivalent on AsyncRuncrate.

Model Inference

All inference methods hit api.runcrate.ai.

Chat Completions

response = client.models.chat_completion(
    model="deepseek-ai/DeepSeek-V3",
    messages=[{"role": "user", "content": "Explain quantum computing"}],
    max_tokens=500,
    temperature=0.7,
)
print(response.choices[0].message.content)

Streaming

stream = client.models.chat_completion(
    model="deepseek-ai/DeepSeek-V3",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True,
)
for chunk in stream:
    delta = chunk["choices"][0]["delta"]
    print(delta.get("content", ""), end="", flush=True)

Image Generation

image = client.models.generate_image(
    model="black-forest-labs/FLUX.1-schnell",
    prompt="A futuristic cityscape at sunset",
    width=1024,
    height=768,
)

# Save directly
image.data[0].save("output.png")

Model-Specific Parameters

All image methods accept extra keyword arguments that get passed through to the provider. Different models support different parameters:

# Seed for reproducibility
image = client.models.generate_image(
    model="black-forest-labs/FLUX.1-schnell",
    prompt="A cat in space",
    seed=42,
    num_inference_steps=4,
    guidance=3.5,
)

# Image editing — pass a file path, URL, or base64 string
image = client.models.generate_image(
    model="black-forest-labs/FLUX.1-kontext-pro",
    prompt="Make the sky purple",
    image="./photo.png",             # file path (auto base64-encoded)
)

# Image editing with URL
image = client.models.generate_image(
    model="Wan-AI/Wan2.6-Image-Edit",
    prompt="Remove the background",
    image="https://example.com/photo.png",  # URL (passed as-is)
)

# ControlNet / Canny
image = client.models.generate_image(
    model="some-controlnet-model",
    prompt="A building",
    control_image="./edges.png",
    strength=0.8,
)

The image, start_image, mask, and control_image fields accept three formats:

File path — "./photo.png" (auto-detected, read and base64-encoded)
URL — "https://..." (passed through as-is)
Base64 string — raw base64 data (passed through as-is)

Video Generation

# Submit, poll, and save in one call
job = client.models.generate_video_and_save(
    "output.mp4",
    model="google/veo-3.0",
    prompt="A drone flying over mountains",
    duration=8,
    on_status=lambda j: print(f"Status: {j.status}"),
)

Or manage the lifecycle manually:

job = client.models.generate_video(
    model="google/veo-3.0",
    prompt="Ocean waves at sunset",
)

# Poll until done
import time
while job.status not in ("completed", "failed"):
    time.sleep(5)
    job = client.models.get_video_status(job.id)

# Download
video_bytes = client.models.download_video(job.id)
with open("video.mp4", "wb") as f:
    f.write(video_bytes)

Extra parameters (e.g., seed, negative_prompt, image for image-to-video models) are passed through:

job = client.models.generate_video(
    model="some-img2vid-model",
    prompt="Animate this scene",
    image="./first_frame.png",   # image-to-video
    seed=42,
    negative_prompt="blurry",
)

Text-to-Speech

audio = client.models.text_to_speech(
    model="hexgrad/Kokoro-82M",
    input="Hello from Runcrate!",
    voice="af_heart",
)
with open("speech.mp3", "wb") as f:
    f.write(audio)

Extra parameters like speed or language are passed through:

audio = client.models.text_to_speech(
    model="hexgrad/Kokoro-82M",
    input="Hello!",
    voice="af_heart",
    speed=1.5,           # model-specific
    language="en",       # model-specific
)

Transcription

with open("recording.wav", "rb") as f:
    result = client.models.transcribe(
        model="openai/whisper-1",
        file=f,
        filename="recording.wav",
        language="en",              # hint language
        response_format="srt",     # text, json, srt, vtt
    )
print(result.text)

Infrastructure Management

GPU Instances

# List instances
instances = client.instances.list()
instances = client.instances.list(search="training")

# Browse available GPU types
types = client.instances.list_types(gpu_type="A100")
for t in types:
    print(f"{t.id} — {t.gpu_type} x{t.gpu_count} — ${t.hourly_rate}/hr")

# Create an instance
instance = client.instances.create(
    name="training-run",
    ssh_key_id="your-key-id",
    gpu_type="A100",
    gpu_count=1,
    startup_commands=["pip install torch"],
)
print(f"Created: {instance.id} — {instance.status}")

# Check status
status = client.instances.get_status(instance.id)
print(f"Status: {status.status}, IP: {status.ip}")

# Terminate
client.instances.terminate(instance.id)

SSH Keys

# List keys
keys = client.ssh_keys.list()

# Add a key
key = client.ssh_keys.create(
    name="my-laptop",
    public_key="ssh-ed25519 AAAA...",
)

# Delete a key
client.ssh_keys.delete(key.id)

Storage

Storage volumes are environment-scoped. Your workspace’s storage provider (AWS S3, Wasabi, or Backblaze B2) must be configured in the dashboard first — the SDK picks it up automatically.

# List available regions (with friendly names)
regions = client.storage.list_regions()
for r in regions:
    print(f"{r.name} ({r.provider})")

# List volumes in the current environment
volumes = client.storage.list()

# Get a specific volume
volume = client.storage.get("volume-id")

# Create a 100GB volume
volume = client.storage.create(
    name="datasets",
    size_gb=100,
    region="us-east-1",
)

# Resize (increase capacity only)
client.storage.resize(volume.id, size_gb=200)

# Delete (refunds unused prepaid days pro-rata)
result = client.storage.delete(volume.id)
print(f"Refunded ${result.refund_amount}")

Billing: $0.03/GB/month, charged weekly in advance. Deletion refunds the unused portion of the current billing week.

Billing

# Check balance
balance = client.billing.get_balance()
print(f"Credits: ${balance.credits_balance}")

# List transactions
txns = client.billing.list_transactions(limit=20)
for t in txns:
    print(f"{t.type}: ${t.amount}")
print(f"Has more: {txns.has_more}")

# Usage summary
usage = client.billing.usage(from_date="2025-01-01", to_date="2025-01-31")
print(f"Total cost: ${usage.total_cost}")

Templates

# List templates with search
templates = client.templates.list(search="finetune", category="ml")
for t in templates:
    print(f"{t.name} — {t.category}")
print(f"Total: {templates.total}")

Error Handling

All API errors include the actual error message from the server — never a generic fallback.

from runcrate import (
    NotFoundError,
    AuthenticationError,
    RateLimitError,
    InsufficientCreditsError,
    BadRequestError,
    UnprocessableEntityError,
)

try:
    client.instances.get("nonexistent")
except NotFoundError as e:
    print(f"Not found: {e.message}")
except AuthenticationError:
    print("Invalid API key")
except RateLimitError:
    print("Rate limited — retry later")
except InsufficientCreditsError as e:
    print(f"Not enough credits: {e.message}")
except UnprocessableEntityError as e:
    print(f"Validation error: {e.message}")  # e.g. invalid model params
except BadRequestError as e:
    print(f"Bad request: {e.message}")

Every error exposes:

e.message — human-readable error description from the API
e.status_code — HTTP status code
e.code — machine-readable error code (e.g. not_found, rate_limited)
e.details — additional details (when available)

Error Hierarchy

Exception	Status Code	Description
`BadRequestError`	400	Invalid parameters
`AuthenticationError`	401	Invalid or missing API key
`InsufficientCreditsError`	402	Not enough credits
`PermissionDeniedError`	403	Insufficient permissions
`NotFoundError`	404	Resource not found
`ConflictError`	409	Resource conflict
`UnprocessableEntityError`	422	Validation error (e.g. invalid model params)
`RateLimitError`	429	Rate limit exceeded
`InternalServerError`	500	Server error
`ConnectionError`	—	Network failure
`TimeoutError`	—	Request timed out

Welcome

SDKs

CLI

Inference

Compute

Storage

Dedicated Clusters

Billing

Account

FAQ

Installation

Quick Start

Configuration

Environments

Async Support

Model Inference

Chat Completions

Streaming

Image Generation

Model-Specific Parameters

Video Generation

Text-to-Speech

Transcription

Infrastructure Management

GPU Instances

SSH Keys

Storage

Billing

Templates

Error Handling

Error Hierarchy

​Installation

​Quick Start

​Configuration

​Environments

​Async Support

​Model Inference

​Chat Completions

​Streaming

​Image Generation

​Model-Specific Parameters

​Video Generation

​Text-to-Speech

​Transcription

​Infrastructure Management

​GPU Instances

​SSH Keys

​Storage

​Billing

​Templates

​Error Handling

​Error Hierarchy

Installation

Quick Start

Configuration

Environments

Async Support

Model Inference

Chat Completions

Streaming

Image Generation

Model-Specific Parameters

Video Generation

Text-to-Speech

Transcription

Infrastructure Management

GPU Instances

SSH Keys

Storage

Billing

Templates

Error Handling

Error Hierarchy