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Providers Overview

Kubiya Workflow SDK supports multiple types of providers to enable flexible workflow orchestration and integration with various AI systems. This section covers two main categories of providers:

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Provider Categories

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Agent Servers

Agent servers are complete orchestration engines that handle the full lifecycle of workflow execution. They provide:

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Core Capabilities

• Workflow Generation: AI-powered creation of workflows from natural language
• Intelligent Execution: Context-aware step-by-step execution with error handling
• Multi-Agent Orchestration: Coordination of specialized agents for complex tasks
• Real-time Streaming: Live feedback and progress updates during execution

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Available Agent Servers

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Architecture Overview

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MCP Providers

MCP (Model Context Protocol) providers enable integration with AI assistants and development tools. They bridge the gap between external AI systems and Kubiya’s workflow capabilities.

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What is MCP?

The Model Context Protocol is an open protocol that standardizes how AI assistants connect to external tools and data sources. It enables:

• Tool Discovery: AI agents discover available workflow capabilities
• Context Sharing: Share relevant workflow context with AI assistants
• Action Execution: AI can trigger and monitor workflow executions
• Real-time Integration: Stream workflow results back to AI interfaces

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Available MCP Providers

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MCP Integration Flow

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Choosing the Right Provider

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Execution Models

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Serverless Containerized Execution

Both provider types leverage Kubiya’s containerized execution model:

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Example: Multi-Language Pipeline

from kubiya_workflow_sdk import Workflow, Step

# Create a workflow that uses multiple technologies
workflow = Workflow(
    name="multi-tech-pipeline",
    runner="kubiya-hosted"
)

# Data extraction with Python
workflow.add_step(Step(
    name="extract-data",
    image="python:3.11",
    packages=["requests", "pandas"],
    code="""
    import requests
    import pandas as pd
    
    # Extract data from API
    response = requests.get('https://api.example.com/data')
    df = pd.DataFrame(response.json())
    df.to_csv('extracted_data.csv')
    """
))

# Analysis with R
workflow.add_step(Step(
    name="statistical-analysis",
    image="r-base:latest",
    code="""
    library(tidyverse)
    
    # Perform statistical analysis
    data <- read.csv('extracted_data.csv')
    analysis <- summary(lm(y ~ x, data))
    write.csv(analysis$coefficients, 'analysis_results.csv')
    """
))

# Visualization with Node.js
workflow.add_step(Step(
    name="create-dashboard",
    image="node:20",
    packages=["d3", "express"],
    code="""
    const express = require('express');
    const d3 = require('d3');
    
    // Create interactive dashboard
    const app = express();
    // Build visualization from analysis results
    """
))

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Next Steps