Microsoft Semantic Kernel Integration Guide

This guide will walk you through integrating Microsoft Semantic Kernel with APIpie, enabling you to build intelligent AI agents and multi-agent systems with enterprise-ready capabilities.

What is Microsoft Semantic Kernel?

Microsoft Semantic Kernel is a model-agnostic SDK that empowers developers to build, orchestrate, and deploy AI agents and multi-agent systems. It provides:

Agent Framework: Build modular AI agents with access to tools/plugins, memory, and planning
Multi-Agent Systems: Orchestrate complex workflows with collaborating specialist agents
Plugin Ecosystem: Extend with native code functions, prompt templates, OpenAPI specs, or Model Context Protocol (MCP)
Vector DB Support: Seamlessly integrate with various vector databases for knowledge retrieval
Multimodal Support: Process text, vision, and audio inputs
Process Framework: Model complex business processes with a structured workflow approach
Enterprise Ready: Built for observability, security, and stable APIs

By connecting Semantic Kernel to APIpie, you gain access to a wide range of powerful language models while leveraging Semantic Kernel's sophisticated agent orchestration capabilities.

Integration Steps

1. Create an APIpie Account

Register here: APIpie Registration
Complete the sign-up process.

2. Add Credit

Add Credit: APIpie Subscription
Add credits to your account to enable API access.

3. Generate an API Key

API Key Management: APIpie API Keys
Create a new API key for use with Semantic Kernel.

4. Install Semantic Kernel

Python

pip install semantic-kernel

.NET

dotnet add package Microsoft.SemanticKernel
dotnet add package Microsoft.SemanticKernel.Agents.Core

5. Configure Semantic Kernel for APIpie

Semantic Kernel supports OpenAI-compatible APIs, which makes it easy to integrate with APIpie.

Python

import os
import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

# Set up the kernel
kernel = sk.Kernel()

# Add APIpie as a chat service
api_key = os.environ.get("APIPIE_API_KEY", "your-apipie-key")
service_id = "apipie-chat" 
kernel.add_service(
    OpenAIChatCompletion(
        service_id=service_id,
        ai_model_id="gpt-4o",  # Use any model available on APIpie
        api_key=api_key,
        endpoint="https://apipie.ai/v1"
    )
)

.NET

using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.ChatCompletion;

// Create a kernel builder
var builder = Kernel.CreateBuilder();

// Add APIpie as the chat completion service
builder.AddOpenAIChatCompletion(
    modelId: "gpt-4o",  // Use any model available on APIpie
    apiKey: Environment.GetEnvironmentVariable("APIPIE_API_KEY") ?? "your-apipie-key",
    serviceId: "apipie-chat",
    endpoint: new Uri("https://apipie.ai/v1")
);

// Build the kernel
var kernel = builder.Build();

Key Features

Model Flexibility: Connect to any LLM with built-in support for OpenAI-compatible APIs
Agent Building Blocks: Create agents with specific instructions, tools, and memory
Plugin System: Extend functionality with native code functions, prompt templates, or APIs
Memory & Embeddings: Store and retrieve contextual information for more effective agents
Planning Capabilities: Enable agents to break down complex tasks into steps
Streaming Support: Process responses as they're generated for responsive applications
Multi-Modal Support: Handle text, images, and other data types in agent interactions
Cross-Platform: Available for Python, .NET, and Java developers

Example Workflows

Application Type	What Semantic Kernel Helps You Build
Conversational Assistants	Chatbots with memory, specialized knowledge, and tool access
Customer Support Systems	Agents that handle different categories of support inquiries
Research & Analysis Tools	Multi-agent systems that collect, analyze, and synthesize data
Enterprise Workflows	Complex business processes with human-in-the-loop capabilities
Document Processing	Systems that understand, extract, and generate content from documents

Using Semantic Kernel with APIpie

Basic Chat Agent (Python)

import os
import asyncio
from semantic_kernel.agents import ChatCompletionAgent
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

async def main():
    # Configure the chat service with APIpie
    chat_service = OpenAIChatCompletion(
        ai_model_id="gpt-4o",  # Use any model available on APIpie
        api_key=os.environ.get("APIPIE_API_KEY", "your-apipie-key"),
        endpoint="https://apipie.ai/v1"
    )
    
    # Create a simple chat agent
    agent = ChatCompletionAgent(
        service=chat_service,
        name="APIpie-Assistant",
        instructions="You are a helpful assistant that provides concise and accurate information.",
    )
    
    # Get a response to a user message
    response = await agent.get_response(messages="What is the relationship between AI and machine learning?")
    print(response.content)

if __name__ == "__main__":
    asyncio.run(main())

Basic Chat Agent (.NET)

using System;
using System.Threading.Tasks;
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Agents;

class Program
{
    static async Task Main()
    {
        // Create a kernel builder
        var builder = Kernel.CreateBuilder();
        
        // Add APIpie as the chat completion service
        builder.AddOpenAIChatCompletion(
            modelId: "gpt-4o",  // Use any model available on APIpie
            apiKey: Environment.GetEnvironmentVariable("APIPIE_API_KEY") ?? "your-apipie-key",
            endpoint: new Uri("https://apipie.ai/v1")
        );
        
        // Build the kernel
        var kernel = builder.Build();
        
        // Create a chat agent
        var agent = new ChatCompletionAgent()
        {
            Name = "APIpie-Assistant",
            Instructions = "You are a helpful assistant that provides concise and accurate information.",
            Kernel = kernel,
        };
        
        // Get a response to a user message
        await foreach (var response in agent.InvokeAsync("What is the relationship between AI and machine learning?"))
        {
            Console.WriteLine(response.Message?.Content);
        }
    }
}

Agent with Custom Tools (Python)

import os
import asyncio
from typing import Annotated
from semantic_kernel.agents import ChatCompletionAgent
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
from semantic_kernel.functions import kernel_function, KernelArguments

# Define a custom plugin for weather information
class WeatherPlugin:
    @kernel_function(description="Get the current weather for a given location.")
    def get_weather(self, location: Annotated[str, "The city and country"]) -> Annotated[str, "The current weather information"]:
        # In a real implementation, this would call a weather API
        return f"The weather in {location} is currently sunny with a temperature of 25°C (77°F)."
    
    @kernel_function(description="Get the weather forecast for the next few days.")
    def get_forecast(self, 
                    location: Annotated[str, "The city and country"],
                    days: Annotated[int, "Number of days to forecast"] = 3) -> Annotated[str, "The weather forecast"]:
        # In a real implementation, this would call a weather API
        return f"The {days}-day forecast for {location} shows sunny conditions with temperatures ranging from 22°C to 28°C."

async def main():
    # Configure the chat service with APIpie
    chat_service = OpenAIChatCompletion(
        ai_model_id="gpt-4o",  # Use any model available on APIpie
        api_key=os.environ.get("APIPIE_API_KEY", "your-apipie-key"),
        endpoint="https://apipie.ai/v1"
    )
    
    # Create a chat agent with the weather plugin
    agent = ChatCompletionAgent(
        service=chat_service,
        name="Weather-Assistant",
        instructions="You are a helpful assistant that provides weather information when asked.",
        plugins=[WeatherPlugin()],
    )
    
    # Get a response to a user message
    response = await agent.get_response(messages="What's the weather like in Tokyo today?")
    print(response.content)

if __name__ == "__main__":
    asyncio.run(main())

Agent with Custom Tools (.NET)

using System;
using System.ComponentModel;
using System.Threading.Tasks;
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Agents;

class Program
{
    static async Task Main()
    {
        // Create a kernel builder
        var builder = Kernel.CreateBuilder();
        
        // Add APIpie as the chat completion service
        builder.AddOpenAIChatCompletion(
            modelId: "gpt-4o",  // Use any model available on APIpie
            apiKey: Environment.GetEnvironmentVariable("APIPIE_API_KEY") ?? "your-apipie-key",
            endpoint: new Uri("https://apipie.ai/v1")
        );
        
        // Build the kernel
        var kernel = builder.Build();
        
        // Add the weather plugin
        kernel.Plugins.Add(KernelPluginFactory.CreateFromType<WeatherPlugin>("WeatherPlugin"));
        
        // Create a chat agent with the weather plugin
        var agent = new ChatCompletionAgent()
        {
            Name = "Weather-Assistant",
            Instructions = "You are a helpful assistant that provides weather information when asked.",
            Kernel = kernel,
            Arguments = new KernelArguments({ FunctionChoiceBehavior = FunctionChoiceBehavior.Auto() })
        };
        
        // Get a response to a user message
        await foreach (var response in agent.InvokeAsync("What's the weather like in Tokyo today?"))
        {
            Console.WriteLine(response.Message?.Content);
        }
    }
}

// Define a custom plugin for weather information
public class WeatherPlugin
{
    [KernelFunction, Description("Get the current weather for a given location.")]
    public string GetWeather([Description("The city and country")] string location)
    {
        // In a real implementation, this would call a weather API
        return $"The weather in {location} is currently sunny with a temperature of 25°C (77°F).";
    }
    
    [KernelFunction, Description("Get the weather forecast for the next few days.")]
    public string GetForecast(
        [Description("The city and country")] string location,
        [Description("Number of days to forecast")] int days = 3)
    {
        // In a real implementation, this would call a weather API
        return $"The {days}-day forecast for {location} shows sunny conditions with temperatures ranging from 22°C to 28°C.";
    }
}

Multi-Agent System with Handoffs (Python)

import os
import asyncio
from semantic_kernel.agents import ChatCompletionAgent
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

async def main():
    # Configure the chat service with APIpie
    chat_service = OpenAIChatCompletion(
        ai_model_id="gpt-4o",  # Use any model available on APIpie
        api_key=os.environ.get("APIPIE_API_KEY", "your-apipie-key"),
        endpoint="https://apipie.ai/v1"
    )
    
    # Create specialized agents
    billing_agent = ChatCompletionAgent(
        service=chat_service,
        name="BillingAgent",
        instructions="You handle billing issues like charges, payment methods, cycles, fees, discrepancies, and payment failures.",
    )
    
    refund_agent = ChatCompletionAgent(
        service=chat_service,
        name="RefundAgent",
        instructions="You assist users with refund inquiries, including eligibility, policies, processing, and status updates.",
    )
    
    # Create a triage agent that can delegate to specialized agents
    triage_agent = ChatCompletionAgent(
        service=chat_service,
        name="TriageAgent",
        instructions="Evaluate user requests and forward them to BillingAgent or RefundAgent for targeted assistance. Provide the full answer to the user containing any information from the agents.",
        plugins=[billing_agent, refund_agent],
    )
    
    # Process a user request
    user_query = "I was charged twice for my subscription last month and need a refund."
    response = await triage_agent.get_response(messages=user_query)
    print(f"User: {user_query}\n\nAgent: {response.content}")

if __name__ == "__main__":
    asyncio.run(main())

Using Memory for Context-Aware Responses (Python)

import os
import asyncio
from semantic_kernel import Kernel
from semantic_kernel.agents import ChatCompletionAgent
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAITextEmbedding
from semantic_kernel.memory import VolatileMemoryStore

async def main():
    # Create a kernel
    kernel = Kernel()
    
    # Configure chat service with APIpie
    chat_service = OpenAIChatCompletion(
        ai_model_id="gpt-4o",  # Use any model available on APIpie
        api_key=os.environ.get("APIPIE_API_KEY", "your-apipie-key"),
        endpoint="https://apipie.ai/v1"
    )
    
    # Configure embedding service with APIpie
    embedding_service = OpenAITextEmbedding(
        ai_model_id="text-embedding-3-large",  # Use any embedding model available on APIpie
        api_key=os.environ.get("APIPIE_API_KEY", "your-apipie-key"),
        endpoint="https://apipie.ai/v1"
    )
    
    # Add services to the kernel
    kernel.add_service(chat_service)
    kernel.add_service(embedding_service)
    
    # Set up memory
    memory_store = VolatileMemoryStore()
    kernel.register_memory(memory_store)
    
    # Add some memories
    await kernel.memory.save_information_async(
        collection="user_data",
        id="user_preferences",
        text="The user prefers vegetarian food and enjoys hiking on weekends."
    )
    
    await kernel.memory.save_information_async(
        collection="user_data",
        id="user_location",
        text="The user lives in Seattle, Washington."
    )
    
    # Create a chat agent with access to memory
    agent = ChatCompletionAgent(
        service=chat_service,
        name="ContextAwareAssistant",
        instructions="""You are a helpful assistant that provides personalized responses based on what you know about the user.
        Before answering, check if you have relevant information about the user that could help personalize your response.""",
        kernel=kernel,
    )
    
    # Query the memory and include relevant context in the response
    user_query = "Can you recommend some activities for this weekend?"
    
    # Retrieve relevant memories
    memories = await kernel.memory.search_async(
        collection="user_data",
        query=user_query,
        limit=5
    )
    
    # Build context from memories
    context = "User information:\n"
    for memory in memories:
        context += f"- {memory.text}\n"
    
    # Create a message with the context and query
    message = f"{context}\n\nUser query: {user_query}"
    
    # Get a response from the agent
    response = await agent.get_response(messages=message)
    print(f"User: {user_query}\n\nAgent: {response.content}")

if __name__ == "__main__":
    asyncio.run(main())

Troubleshooting & FAQ

How do I handle authentication with APIpie?
Store your API key securely in environment variables and pass it to the Semantic Kernel configuration. Never hardcode API keys in your code.
Which models work best with Semantic Kernel?
While most models available on APIpie will work with Semantic Kernel, models with strong tool use/function calling capabilities (like GPT-4o or Claude 3 Opus) work best for agent-based applications.
How do I debug agent behavior?
Enable logging in Semantic Kernel to see detailed information about each step the agent takes. For Python, use the standard logging module; for .NET, use ILogger.
Can I mix and match different LLM providers?
Yes, Semantic Kernel allows you to configure different services for different purposes. For example, you might use APIpie for chat completions but another provider for embeddings.
How do I handle rate limits?
Implement retry logic in your application to handle rate limits. Semantic Kernel does not have built-in retry mechanisms, so you'll need to handle this in your code.
Does Semantic Kernel support streaming?
Yes, Semantic Kernel supports streaming responses. Use the streaming methods provided by the library to process responses as they're generated.

For more information, see the Semantic Kernel documentation or the GitHub repository.

Support

If you encounter any issues during the integration process, please reach out on APIpie Discord for assistance.

What is Microsoft Semantic Kernel?​

Integration Steps​

1. Create an APIpie Account​

2. Add Credit​

3. Generate an API Key​

4. Install Semantic Kernel​

Python​

.NET​

5. Configure Semantic Kernel for APIpie​

Python​

.NET​

Key Features​

Example Workflows​

Using Semantic Kernel with APIpie​

Basic Chat Agent (Python)​

Basic Chat Agent (.NET)​

Agent with Custom Tools (Python)​

Agent with Custom Tools (.NET)​

Multi-Agent System with Handoffs (Python)​

Using Memory for Context-Aware Responses (Python)​

Troubleshooting & FAQ​

Support​

What is Microsoft Semantic Kernel?

Integration Steps

1. Create an APIpie Account

2. Add Credit

3. Generate an API Key

4. Install Semantic Kernel

Python

.NET

5. Configure Semantic Kernel for APIpie

Python

.NET

Key Features

Example Workflows

Using Semantic Kernel with APIpie

Basic Chat Agent (Python)

Basic Chat Agent (.NET)

Agent with Custom Tools (Python)

Agent with Custom Tools (.NET)

Multi-Agent System with Handoffs (Python)

Using Memory for Context-Aware Responses (Python)

Troubleshooting & FAQ

Support