kubiya-sdk 0.1.4

Kubiya SDK

Kubiya SDK

Kubiya SDK is a powerful Python library for creating, managing, and executing workflows and tools. It provides a flexible and intuitive interface for defining complex workflows, integrating various tools, and managing their execution.

Table of Contents

• Installation
• Quick Start
• Key Concepts
• Creating Workflows
• Defining Tools
• Executing Workflows
• Visualization
• API Reference
• Examples
• Contributing
• License

Installation

To install the Kubiya SDK, use pip:

pip install kubiya-sdk

Quick Start

Here's a simple example to get you started with Kubiya SDK:

from kubiya_sdk.workflows import StatefulWorkflow
from kubiya_sdk.tools import register_tool

# Define a simple tool
@register_tool(name="Multiplier", description="Multiplies a number by 2")
def multiply_by_two(number: int) -> int:
    return number * 2

# Create a workflow
workflow = StatefulWorkflow("SimpleWorkflow")

@workflow.step("step1")
def step1(state):
    return {"result": state["input"] * 2}

@workflow.step("step2")
def step2(state):
    return {"final_result": state["result"] + 1}

workflow.add_edge("step1", "step2")

# Run the workflow
result = await workflow.run({"input": 5})
print(result)

Key Concepts

• Workflows: Sequences of steps that process and transform data.
• Steps: Individual units of work within a workflow.
• Tools: Reusable functions that can be integrated into workflows.
• State: The data passed between steps in a workflow.

Creating Workflows

Workflows in Kubiya SDK are created using the StatefulWorkflow class:

from kubiya_sdk.workflows import StatefulWorkflow

workflow = StatefulWorkflow("MyWorkflow")

@workflow.step("step_name")
def step_function(state):
    # Process state and return new state
    return {"new_key": "new_value"}

# Connect steps
workflow.add_edge("step1", "step2")

Conditional Workflows

You can add conditions to your workflows:

workflow.add_condition("step1", "state['result'] > 10", "step3")

Defining Tools

Tools are defined using the @register_tool decorator:

from kubiya_sdk.tools import register_tool

@register_tool(name="MyTool", description="Description of my tool")
def my_tool(arg1: int, arg2: str) -> dict:
    # Tool logic here
    return {"result": arg1 + len(arg2)}

Executing Workflows

Workflows can be executed asynchronously:

result = await workflow.run({"input": "initial_value"})

Visualization

Kubiya SDK provides Mermaid diagram generation for workflows:

mermaid_diagram = workflow.to_mermaid()
print(mermaid_diagram)

This will generate a Mermaid diagram string that can be rendered as follows:

graph TD
    step1["🚀 Step 1"] -->|"Result > 10"| step3["🔧 Step 3"]
    step1 -->|"Result <= 10"| step2["🔧 Step 2"]
    step2 --> END["🏁 End"]
    step3 --> END
    classDef startStyle fill:#A2C4C9,stroke:#005F73,stroke-width:2px,color:#000000;
    classDef endStyle fill:#FFB6B9,stroke:#D7263D,stroke-width:2px,color:#000000;
    classDef stepStyle fill:#EDEDED,stroke:#495057,stroke-width:2px,color:#000000;
    class step1 startStyle;
    class END endStyle;

API Reference

For detailed API documentation, please refer to our API Reference.

Examples

Complex Workflow Example

Here's an example of a more complex workflow that demonstrates various features of the Kubiya SDK:

from kubiya_sdk.workflows import StatefulWorkflow
from kubiya_sdk.tools import register_tool

@register_tool(name="DataFetcher", description="Fetches data from an API")
async def fetch_data(api_url: str) -> dict:
    # Simulated API call
    return {"data": f"Data from {api_url}"}

@register_tool(name="DataProcessor", description="Processes fetched data")
def process_data(data: str) -> dict:
    return {"processed_data": f"Processed: {data}"}

workflow = StatefulWorkflow("ComplexWorkflow")

@workflow.step("fetch_step")
async def fetch_step(state):
    tool = workflow.get_tool("DataFetcher")
    result = await tool.execute(api_url=state["api_url"])
    return {"fetched_data": result["data"]}

@workflow.step("process_step")
def process_step(state):
    tool = workflow.get_tool("DataProcessor")
    result = tool.execute(data=state["fetched_data"])
    return {"processed_data": result["processed_data"]}

@workflow.step("decision_step")
def decision_step(state):
    data_length = len(state["processed_data"])
    return {"data_length": data_length}

@workflow.step("short_data_step")
def short_data_step(state):
    return {"result": f"Short data: {state['processed_data']}"}

@workflow.step("long_data_step")
def long_data_step(state):
    return {"result": f"Long data: {state['processed_data'][:50]}..."}

workflow.add_edge("fetch_step", "process_step")
workflow.add_edge("process_step", "decision_step")
workflow.add_condition("decision_step", "state['data_length'] < 50", "short_data_step")
workflow.add_condition("decision_step", "state['data_length'] >= 50", "long_data_step")

# Execution
result = await workflow.run({"api_url": "https://api.example.com/data"})
print(result)

This example demonstrates:

• Tool registration and usage
• Multiple workflow steps
• Conditional branching based on state
• Asynchronous operations

Contributing

We welcome contributions to the Kubiya SDK! Please see our Contributing Guidelines for more information on how to get started.

License

Kubiya SDK is released under the MIT License.