Microsoft Agent Framework

Splunk Agent Observability supports logging traces from Microsoft Agent Framework applications using OpenTelemetry. The framework has built-in OTel instrumentation, so no extra instrumentation package is needed.

Set up OpenTelemetry

To log Microsoft Agent Framework traces using Splunk Agent Observability, the first step is to set up OpenTelemetry.

Installation

Add the OpenTelemetry packages to your project:

pip install opentelemetry-api opentelemetry-sdk \
            opentelemetry-exporter-otlp

The opentelemetry-api and opentelemetry-sdk packages provide the core OpenTelemetry functionality. The opentelemetry-exporter-otlp package enables sending traces to Splunk Agent Observability’s OTLP endpoint.

Create environment variables for your Splunk Agent Observability settings

Set environment variables for your Splunk Agent Observability settings, for example in a .env file. These environment variables are consumed by the SplunkAOSpanProcessorto authenticate and route traces to the correct Splunk Agent Observability Project and Agent Stream:

# Provide your Splunk Agent Observability
# API key and URL if you are using an
# on-premises, standalone, or custom deployment
# SPLUNK_AO_API_KEY="your-splunk-ao-api-key"
# SPLUNK_AO_CONSOLE_URL="your-splunk-ao-url"

# Your Splunk Agent Observability project name
SPLUNK_AO_PROJECT="your-splunk-ao-project-name"

# The name of the Agent Stream you want to use for logging
SPLUNK_AO_AGENT_STREAM="your-splunk-ao-agent-stream"

Self hosted deployments: Set the OTel endpoint

Skip this step if you are using a hosted version.

The OTel endpoint is different from Splunk Agent Observability’s regular API endpoint and is specifically designed to receive telemetry data in the OTLP format.If you are using:

A cloud deployment, then you don’t need to provide a custom OTel endpoint. The default endpoint <your-splunk-ao-api-url>/otel/traces will be used automatically.
A self-hosted deployment, replace the <your-splunk-ao-api-url>/otel/traces endpoint with your deployment URL. The format of this URL is based on your console URL, appending /otel/traces.

The convention is to store this in the SPLUNK_AO_CONSOLE_URL environment variable. For example:

os.environ["SPLUNK_AO_CONSOLE_URL"] = "<your-splunk-ao-url>"

Initialize and create the Splunk Agent Observability span processor

The SplunkAOSpanProcessor automatically configures authentication and metadata using your environment variables. It also:

Auto-builds OTLP headers using your Splunk Agent Observability credentials
Configures the correct OTLP trace endpoint
Registers a batch span processor that exports traces to Splunk Agent Observability

from splunk_ao import otel  

# SplunkAOSpanProcessor (no manual OTLP config required) loads the env vars for 
# the Splunk Agent Observability API key, Project, and Agent Stream. Make sure to set them first. 
splunk_ao_span_processor = otel.SplunkAOSpanProcessor(
    # Optional parameters if not set, uses env var
    # project=os.environ["SPLUNK_AO_PROJECT"], 
    # agentstream=os.environ.get("SPLUNK_AO_AGENT_STREAM"),  
)

Log a Microsoft Agent Framework agent using OpenTelemetry

Once OpenTelemetry is configured, you can use the SplunkAOSpanProcessor to capture traces from your agent.

Create the tracer provider with the Splunk Agent Observability span processor

Set up a TracerProvider with the SplunkAOSpanProcessor and register it as the global tracer provider:

from splunk_ao.otel import SplunkAOSpanProcessor, add_splunk_ao_span_processor
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider

tracer_provider = TracerProvider()
splunk_ao_processor = SplunkAOSpanProcessor()
add_splunk_ao_span_processor(tracer_provider, splunk_ao_processor)
trace.set_tracer_provider(tracer_provider)

Enable the framework's instrumentation

Enable the Microsoft Agent Framework’s built-in OTel instrumentation. Set enable_sensitive_data=True to send LLM inputs and outputs to Splunk Agent Observability. If set to False, only span metadata (timing, token counts, etc.) will be sent.

from agent_framework.observability import enable_instrumentation

enable_instrumentation(enable_sensitive_data=True)

When you run your agent code, traces will be logged to Splunk Agent Observability.

Full example

Here is a full example based on the Microsoft Agent Framework’s tool calling sample. You can find this project in the Splunk Agent Observability SDK examples repo. To run this example, create a .env file with the following values set, or set them as environment variables:

.env

# OpenAI environment variables
OPENAI_API_KEY=your-openai-api-key

# Splunk Agent Observability environment variables
SPLUNK_AO_PROJECT=your-splunk-ao-project
SPLUNK_AO_AGENT_STREAM=your-agent-stream
# Only required for on-premises, standalone, or custom deployments
# SPLUNK_AO_API_KEY=your-splunk-ao-api-key

Remember to update these to match your Splunk Agent Observability API key, project name, and Agent Stream name.

from random import randint
from typing import Annotated

from agent_framework import openai, tool
from agent_framework.observability import enable_instrumentation
from splunk_ao.otel import SplunkAOSpanProcessor, add_splunk_ao_span_processor
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from pydantic import Field

# Set up the OTel tracer provider with the Splunk Agent Observability span processor
tracer_provider = TracerProvider()
splunk_ao_processor = SplunkAOSpanProcessor()
add_splunk_ao_span_processor(tracer_provider, splunk_ao_processor)
trace.set_tracer_provider(tracer_provider)

# Enable the Microsoft Agent Framework's built-in OTel instrumentation.
# Set enable_sensitive_data=True to send LLM inputs and outputs to Splunk Agent Observability.
# If set to False, only span metadata (timing, token counts, etc.) will be sent.
enable_instrumentation(enable_sensitive_data=True)


@tool(approval_mode="never_require")
def get_weather(
    location: Annotated[
        str, Field(description="The location to get the weather for.")
    ],
) -> str:
    """Get the weather for a given location."""
    conditions = ["sunny", "cloudy", "rainy", "stormy"]
    temp = randint(10, 30)
    condition = conditions[randint(0, 3)]
    return f"The weather in {location} is {condition}, {temp}C."


client = openai.OpenAIChatClient(model_id="gpt-4.1-mini")

agent = client.as_agent(
    name="WeatherAgent",
    instructions="You are a helpful weather agent. "
    "Use the get_weather tool to answer questions.",
    tools=[get_weather],
)


async def main():
    result = await agent.run("What's the weather like in Seattle?")
    print(result)


if __name__ == "__main__":
    import asyncio

    asyncio.run(main())

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References

Set up OpenTelemetry

Log a Microsoft Agent Framework agent using OpenTelemetry

Full example

​Set up OpenTelemetry

​Log a Microsoft Agent Framework agent using OpenTelemetry

​Full example

Set up OpenTelemetry

Log a Microsoft Agent Framework agent using OpenTelemetry

Full example