Microsoft Semantic Kernel integration

AI Gateway

Route your LLM calls through the AI Gateway with a single base URL change. Zero vendor lock-in: always run on the best model at the lowest cost for your use case.

Observability

Send Semantic Kernel traces to Orq.ai with OpenTelemetry.

AI Gateway

Overview

Microsoft Semantic Kernel is an SDK that integrates Large Language Models (LLMs) with conventional programming languages. By connecting Semantic Kernel to Orq.ai’s AI Gateway, you transform experimental AI agents into production-ready systems with enterprise-grade capabilities.

Key Benefits

Orq.ai’s AI Gateway enhances your Semantic Kernel applications with:

Complete Observability

Track every agent step, tool use, and interaction with detailed traces and analytics

Built-in Reliability

Automatic fallbacks, retries, and load balancing for production resilience

Cost Optimization

Real-time cost tracking and spend management across all your AI operations

Multi-Provider Access

Access 500+ LLMs and 30+ providers through a single, unified integration

Prerequisites

Before integrating Semantic Kernel with Orq.ai, ensure you have:

An Orq.ai account and API Key
Python 3.10 or higher
Semantic Kernel SDK installed

To setup your API key, see API keys & Endpoints.

Installation

Install Semantic Kernel and the OpenAI SDK:

pip install semantic-kernel openai

Configuration

Configure Semantic Kernel to use Orq.ai’s AI Gateway by creating an OpenAI client with a custom base URL:

from openai import AsyncOpenAI
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
import os

# Configure OpenAI client with Orq.ai AI Gateway
client = AsyncOpenAI(
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://my.orq.ai/v3/router"
)

# Create kernel
kernel = Kernel()

# Add chat completion service
chat_service = OpenAIChatCompletion(
    ai_model_id="openai/gpt-5.6-sol",
    async_client=client
)

kernel.add_service(chat_service)

base_url: https://my.orq.ai/v3/router

Basic Example

Here’s a complete example of using Semantic Kernel with Orq.ai:

from openai import AsyncOpenAI
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import (
    OpenAIChatCompletion,
    OpenAIChatPromptExecutionSettings
)
from semantic_kernel.contents import ChatHistory
import asyncio
import os

async def main():
    # Configure client with Orq.ai
    client = AsyncOpenAI(
        api_key=os.getenv("ORQ_API_KEY"),
        base_url="https://my.orq.ai/v3/router"
    )

    # Create kernel
    kernel = Kernel()

    # Add chat completion service
    chat_service = OpenAIChatCompletion(
        ai_model_id="openai/gpt-5.6-sol",
        async_client=client
    )
    kernel.add_service(chat_service)

    # Create execution settings
    settings = OpenAIChatPromptExecutionSettings(
        max_tokens=2000,
        temperature=0.7
    )

    # Create chat history
    history = ChatHistory()
    history.add_user_message("What is quantum computing?")

    # Get response
    response = await chat_service.get_chat_message_content(
        chat_history=history,
        settings=settings,
        kernel=kernel
    )

    print(response.content)

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

Using Plugins (Functions)

Semantic Kernel’s power comes from combining LLMs with plugins. Here’s how to use them with Orq.ai:

from openai import AsyncOpenAI
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
from semantic_kernel.functions import kernel_function
from semantic_kernel.contents import ChatHistory
import asyncio
import os

# Define a plugin
class WeatherPlugin:
    @kernel_function(
        name="get_weather",
        description="Get the weather for a location"
    )
    def get_weather(self, location: str) -> str:
        """Get weather for a location."""
        return f"The weather in {location} is sunny and 72°F"

async def main():
    # Configure client
    client = AsyncOpenAI(
        api_key=os.getenv("ORQ_API_KEY"),
        base_url="https://my.orq.ai/v3/router"
    )

    # Create kernel
    kernel = Kernel()

    # Add chat completion service
    chat_service = OpenAIChatCompletion(
        ai_model_id="openai/gpt-5.6-sol",
        async_client=client
    )
    kernel.add_service(chat_service)

    # Add plugin
    kernel.add_plugin(
        WeatherPlugin(),
        plugin_name="WeatherPlugin"
    )

    # Create chat history
    history = ChatHistory()
    history.add_user_message("What's the weather in San Francisco?")

    # Enable automatic function calling
    from semantic_kernel.connectors.ai.open_ai import OpenAIChatPromptExecutionSettings
    from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior

    execution_settings = OpenAIChatPromptExecutionSettings(
        function_choice_behavior=FunctionChoiceBehavior.Auto()
    )

    # Get response with function calling
    response = await chat_service.get_chat_message_content(
        chat_history=history,
        settings=execution_settings,
        kernel=kernel
    )

    print(response.content)

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

Model Selection

With Orq.ai, you can use any supported model from 30+ providers:

from openai import AsyncOpenAI
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
import os

# Configure client
client = AsyncOpenAI(
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://my.orq.ai/v3/router"
)

kernel = Kernel()

# Use Claude
claude_service = OpenAIChatCompletion(
    ai_model_id="anthropic/claude-sonnet-5",
    async_client=client,
    service_id="claude"
)
kernel.add_service(claude_service)

# Use Gemini
gemini_service = OpenAIChatCompletion(
    ai_model_id="google-ai/gemini-2.5-flash",
    async_client=client,
    service_id="gemini"
)
kernel.add_service(gemini_service)

# Use any other model
groq_service = OpenAIChatCompletion(
    ai_model_id="groq/qwen/qwen3.8-27b",
    async_client=client,
    service_id="groq"
)
kernel.add_service(groq_service)

Streaming Responses

Semantic Kernel supports streaming with Orq.ai:

from openai import AsyncOpenAI
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import (
    OpenAIChatCompletion,
    OpenAIChatPromptExecutionSettings
)
from semantic_kernel.contents import ChatHistory
import asyncio
import os

async def main():
    client = AsyncOpenAI(
        api_key=os.getenv("ORQ_API_KEY"),
        base_url="https://my.orq.ai/v3/router"
    )

    kernel = Kernel()
    chat_service = OpenAIChatCompletion(
        ai_model_id="openai/gpt-5.6-sol",
        async_client=client
    )
    kernel.add_service(chat_service)

    settings = OpenAIChatPromptExecutionSettings(
        max_tokens=2000,
        temperature=0.7
    )

    history = ChatHistory()
    history.add_user_message("Write a short story about AI")

    # Stream response
    async for message in chat_service.get_streaming_chat_message_content(
        chat_history=history,
        settings=settings,
        kernel=kernel
    ):
        print(message.content, end="", flush=True)

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

Observability

The AI Gateway records model requests routed through it. To capture Semantic Kernel’s own spans, including kernel function calls, configure OpenTelemetry in the application.

Install dependencies

Bash

pip install semantic-kernel openai opentelemetry-sdk opentelemetry-exporter-otlp-proto-http

Enable model diagnostics

Set this variable before starting Python so Semantic Kernel also emits model spans:

Bash

export SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS=true

This experimental setting includes model names and token usage without enabling prompt or response capture. See Semantic Kernel’s telemetry documentation for the available diagnostics settings.

Send traces to Orq.ai

Set ORQ_API_KEY to an Orq.ai API key, then run this example. Initialize one tracer provider for the application and shut it down when the application exits.

Python

import asyncio
import os

from openai import AsyncOpenAI
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

tracer_provider = TracerProvider(
    resource=Resource.create({"service.name": "semantic-kernel-app"})
)
tracer_provider.add_span_processor(
    BatchSpanProcessor(
        OTLPSpanExporter(
            endpoint="https://my.orq.ai/v2/otel/v1/traces",
            headers={"Authorization": f"Bearer {os.environ['ORQ_API_KEY']}"},
        )
    )
)
trace.set_tracer_provider(tracer_provider)


async def main():
    async with AsyncOpenAI(
        api_key=os.environ["ORQ_API_KEY"],
        base_url="https://my.orq.ai/v3/router",
    ) as client:
        kernel = Kernel()
        kernel.add_service(
            OpenAIChatCompletion(
                ai_model_id="openai/gpt-5.6-sol",
                async_client=client,
            )
        )
        answer = await kernel.invoke_prompt("Explain quantum computing in one sentence.")
        print(answer)


if __name__ == "__main__":
    try:
        asyncio.run(main())
    finally:
        tracer_provider.force_flush()
        tracer_provider.shutdown()

Open Traces to inspect the exported spans from semantic-kernel-app.

Evaluations & Experiments

Once your agents are running, use Evaluatorq to score outputs across a dataset and Experiments to compare configurations side-by-side.

Run Evaluations with Evaluatorq

Run parallel evaluations across your agents and compare results.

Run Experiments via the API

Compare agent configurations and view results in the AI Studio.

Model Providers

Coding Agents

Chat Interfaces

Frameworks & SDKs

Automations

Microsoft Semantic Kernel integration

AI Gateway

Observability

AI Gateway

Overview

Key Benefits

Complete Observability

Built-in Reliability

Cost Optimization

Multi-Provider Access

Prerequisites

Installation

Configuration

Basic Example

Using Plugins (Functions)

Model Selection

Streaming Responses

Observability

Install dependencies

Enable model diagnostics

Send traces to Orq.ai

Evaluations & Experiments

Run Evaluations with Evaluatorq

Run Experiments via the API

AI Gateway

Observability

​AI Gateway

​Overview

​Key Benefits

Complete Observability

Built-in Reliability

Cost Optimization

Multi-Provider Access

​Prerequisites

​Installation

​Configuration

​Basic Example

​Using Plugins (Functions)

​Model Selection

​Streaming Responses

​Observability

​Install dependencies

​Enable model diagnostics

​Send traces to Orq.ai

​Evaluations & Experiments

Run Evaluations with Evaluatorq

Run Experiments via the API

AI Gateway

Overview

Key Benefits

Prerequisites

Installation

Configuration

Basic Example

Using Plugins (Functions)

Model Selection

Streaming Responses

Observability

Install dependencies

Enable model diagnostics

Send traces to Orq.ai

Evaluations & Experiments