python -c "import kubiya_workflow_sdk; print(kubiya_workflow_sdk.__version__)"

export KUBIYA_API_KEY="your-api-key-here"

KUBIYA_API_KEY=your-api-key-here
KUBIYA_API_URL=https://api.kubiya.ai  # Optional, defaults to production

# For ADK provider
pip install kubiya-workflow-sdk[adk]

# For FastMCP provider
pip install kubiya-workflow-sdk[mcp]

# All providers
pip install kubiya-workflow-sdk[all]

# Install with development dependencies
pip install kubiya-workflow-sdk[dev]

FROM python:3.11-slim

WORKDIR /app

# Install SDK
RUN pip install kubiya-workflow-sdk

# Copy your workflow files
COPY . .

# Run your workflow
CMD ["python", "workflow.py"]

from kubiya_workflow_sdk import Client

# Initialize with API key
client = Client(
    api_key="your-api-key",
    api_url="https://api.kubiya.ai"  # Optional, defaults to production
)

from kubiya_workflow_sdk import Client, Config

# Detailed configuration
config = Config(
    api_key="your-api-key",
    api_url="https://api.kubiya.ai",
    timeout=30,  # Request timeout in seconds
    retry_count=3,  # Number of retries
    retry_delay=1.0,  # Delay between retries
    verify_ssl=True,  # SSL verification
    proxy=None,  # HTTP proxy if needed
)

client = Client(config=config)

export KUBIYA_API_KEY="your-api-key"
export KUBIYA_API_URL="https://api.kubiya.ai"
export KUBIYA_TIMEOUT="30"
export KUBIYA_RETRY_COUNT="3"

client = Client()  # Reads from environment

from kubiya_workflow_sdk import Workflow, Step

# Object-oriented approach
workflow = Workflow(
    name="data-pipeline",
    description="Process daily data",
    steps=[
        Step(name="fetch", image="alpine", command="wget data.csv"),
        Step(name="process", image="python:3.11", script="process.py")
    ]
)

# Execute
result = client.execute_workflow(workflow)

from kubiya_workflow_sdk.dsl import workflow

# Fluent interface
wf = (
    workflow("deployment-pipeline")
    .description("Deploy application")
    .step("test", "npm test", image="node:18")
    .step("build", "npm run build")
    .step("deploy", "kubectl apply -f k8s/")
    .build()
)

from kubiya_workflow_sdk.providers import get_provider

# ADK provider for AI generation
adk = get_provider("adk", api_key="...")

# Generate workflow
result = await adk.compose(
    task="Create a CI/CD pipeline for Python app",
    mode="act"  # or "plan" for preview
)

# Real-time execution updates
for event in client.execute_workflow(workflow, stream=True):
    print(f"{event.type}: {event.data}")

from kubiya_workflow_sdk.mcp import MCPServer

# Create MCP-compatible server
server = MCPServer(
    name="my-workflows",
    version="1.0.0"
)

# Register workflows as tools
server.register_workflow(workflow)
server.run(port=8080)

# Direct API key
client = Client(api_key="key_...")

# From environment
os.environ["KUBIYA_API_KEY"] = "key_..."
client = Client()

# From file
with open(".kubiya-key", "r") as f:
    api_key = f.read().strip()
client = Client(api_key=api_key)

# Specify organization
client = Client(
    api_key="key_...",
    org_name="my-company"  # For multi-org accounts
)

# docker-compose.yml
version: '3.8'
services:
  kubiya-local:
    image: kubiya/sdk:latest
    volumes:
      - .:/workspace
      - /var/run/docker.sock:/var/run/docker.sock
    environment:
      - KUBIYA_MODE=local

# Example: Apply the runner manifest
kubectl apply -f kubiya-runner-manifest.yaml

# Or using Helm
helm install my-runner kubiya/runner -f values.yaml

# HTTP proxy
client = Client(
    api_key="...",
    proxy="http://proxy.company.com:8080"
)

# SOCKS proxy
client = Client(
    api_key="...",
    proxy="socks5://proxy.company.com:1080"
)

# With authentication
client = Client(
    api_key="...",
    proxy="http://user:pass@proxy.company.com:8080"
)

# Disable SSL verification (not recommended)
client = Client(
    api_key="...",
    verify_ssl=False
)

# Custom CA bundle
client = Client(
    api_key="...",
    ca_bundle="/path/to/ca-bundle.crt"
)