Add client-side tracing to Foundry agents (preview)

Important

Items marked (preview) in this article are currently in public preview. This preview is provided without a service-level agreement, and we don't recommend it for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

Microsoft Foundry automatically captures server-side traces for agents running in the portal. Client-side tracing extends that visibility into your own application code. By instrumenting your agent application with OpenTelemetry, you can capture spans for model calls, tool invocations, and custom logic — then export them to Azure Monitor Application Insights, the console, or any observability backend that supports the OpenTelemetry protocol (OTLP), such as Datadog, Grafana Tempo, Jaeger, or Honeycomb.

In this article, you learn how to:

Install the required OpenTelemetry tracing packages.
Enable GenAI tracing instrumentation for agent applications.
Export traces to Azure Monitor, the console, or an OTLP-compatible backend.
Enable content recording to capture message contents.
Enable trace context propagation for distributed tracing (Python).
Trace custom functions.

Prerequisites

A Foundry project with an Application Insights resource connected.
An AI model deployed to the project. Note the deployment name.
Azure CLI installed. Sign in by running az login.
Contributor or higher role on the Foundry project. To view traces, you also need Log Analytics Reader on the connected Application Insights resource. For more information, see Role-based access control in Foundry.

The following environment variables set with your own values:

Variable	Description
`FOUNDRY_PROJECT_ENDPOINT`	Your Foundry project endpoint URL. Find it on the project Overview page in the Foundry portal.
`FOUNDRY_MODEL_NAME`	The deployment name of an AI model in your project. Find it under Models in the Foundry portal.

Python 3.9 or later.
The azure-ai-projects package version 2.0.0 or later.

Install tracing packages

Python
C#

Install the Microsoft Foundry SDK, OpenTelemetry, and the Azure Monitor exporter:

pip install azure-ai-projects azure-identity opentelemetry-sdk azure-core-tracing-opentelemetry azure-monitor-opentelemetry

For console-only or OTLP export (for example, Aspire Dashboard), install the OTLP exporter:

pip install opentelemetry-exporter-otlp

Add the required NuGet packages:

dotnet add package Azure.AI.Projects
dotnet add package Azure.AI.Projects.Agents
dotnet add package Azure.Identity
dotnet add package Azure.Monitor.OpenTelemetry.Exporter
dotnet add package OpenTelemetry.Exporter.Console

For ASP.NET Core applications, Azure.Monitor.OpenTelemetry.AspNetCore is the preferred package. The Azure.Monitor.OpenTelemetry.Exporter package shown here works for all .NET application types.

Enable GenAI tracing

GenAI tracing instrumentation is an experimental preview feature. Spans, attributes, and events might change in future versions. You must explicitly opt in before tracing is active.

Python
C#

Set the AZURE_EXPERIMENTAL_ENABLE_GENAI_TRACING environment variable to true before calling AIProjectInstrumentor().instrument():

import os

os.environ["AZURE_EXPERIMENTAL_ENABLE_GENAI_TRACING"] = "true"

from azure.ai.projects.telemetry import AIProjectInstrumentor

# Enable instrumentation
AIProjectInstrumentor().instrument()

If the variable isn't set or isn't true (case-insensitive), tracing instrumentation isn't enabled and a warning is logged.

Use an AppContext switch or the same environment variable:

// Enable GenAI tracing
AppContext.SetSwitch("Azure.Experimental.EnableGenAITracing", true);

When you enable Azure.Experimental.EnableGenAITracing, the SDK automatically enables the Azure.Experimental.EnableActivitySource flag, which is required for OpenTelemetry instrumentation to function.

If both the AppContext switch and the environment variable are set, the AppContext switch takes priority.

Note

In C#, all tracing-related environment variables accept true (case-insensitive) or 1 as equivalent enabling values.

Export traces to Azure Monitor

Send traces to Azure Application Insights so they appear in the Foundry portal's Traces view and in Azure Monitor.

Python
C#

import os

os.environ["AZURE_EXPERIMENTAL_ENABLE_GENAI_TRACING"] = "true"

from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import PromptAgentDefinition
from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry import configure_azure_monitor
from opentelemetry import trace

endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]

with (
    DefaultAzureCredential() as credential,
    AIProjectClient(endpoint=endpoint, credential=credential) as project,
):
    # Get the Application Insights connection string from the project
    connection_string = project.telemetry.get_application_insights_connection_string()
    configure_azure_monitor(connection_string=connection_string)

    tracer = trace.get_tracer(__name__)

    with tracer.start_as_current_span("agent-tracing-scenario"):
        with project.get_openai_client() as openai:
            # Create an agent
            agent = project.agents.create_version(
                agent_name="MyAgent",
                definition=PromptAgentDefinition(
                    model=os.environ["FOUNDRY_MODEL_NAME"],
                    instructions="You are a helpful assistant.",
                ),
            )
            print(f"Agent created (id: {agent.id}, name: {agent.name})")

            # Create a conversation and get a response
            conversation = openai.conversations.create()
            response = openai.responses.create(
                conversation=conversation.id,
                extra_body={"agent_reference": {"name": agent.name, "id": agent.id, "type": "agent_reference"}},
                input="What is the largest city in France?",
            )
            print(f"Response: {response.output_text}")

            # Clean up
            openai.conversations.delete(conversation_id=conversation.id)
            project.agents.delete_version(agent_name=agent.name, agent_version=agent.version)

Reference: AIProjectClient, DefaultAzureCredential, configure_azure_monitor

using Azure.AI.Projects;
using Azure.AI.Projects.Agents;
using Azure.Identity;
using Azure.Monitor.OpenTelemetry.Exporter;
using OpenTelemetry;
using OpenTelemetry.Resources;
using OpenTelemetry.Trace;

var projectEndpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT");
var modelName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL_NAME");

// Enable GenAI tracing
AppContext.SetSwitch("Azure.Experimental.EnableGenAITracing", true);

AIProjectClient projectClient = new(
    endpoint: new Uri(projectEndpoint),
    tokenProvider: new DefaultAzureCredential());

// Get the Application Insights connection string from the project
var connectionString = await projectClient.Telemetry
    .GetApplicationInsightsConnectionStringAsync();

// The Azure Monitor exporter reads this environment variable automatically
Environment.SetEnvironmentVariable(
    "APPLICATIONINSIGHTS_CONNECTION_STRING", connectionString);

// Configure OpenTelemetry with Azure Monitor exporter
var tracerProvider = Sdk.CreateTracerProviderBuilder()
    .AddSource("Azure.AI.Projects.*")
    .SetResourceBuilder(
        ResourceBuilder.CreateDefault().AddService("AgentTracingSample"))
    .AddAzureMonitorTraceExporter()
    .Build();

using (tracerProvider)
{
    // Create an agent
    DeclarativeAgentDefinition agentDefinition = new(model: modelName)
    {
        Instructions = "You are a helpful assistant."
    };
    AgentVersion agent = await projectClient.Agents.CreateAgentVersionAsync(
        agentName: "myAgent",
        options: new(agentDefinition));
    Console.WriteLine(
        $"Agent created (id: {agent.Id}, name: {agent.Name})");

    // Clean up
    projectClient.Agents.DeleteAgentVersion(
        agentName: agent.Name, agentVersion: agent.Version);
}

Reference: AIProjectClient, DefaultAzureCredential, Sdk.CreateTracerProviderBuilder

Note

To correlate traces with a specific agent in the Foundry portal, include the agent_reference with both name and id in your responses.create() call (as shown in the Python sample above). Traces typically appear within 2-5 minutes.

Export traces to the console

Console export is useful for local debugging. Traces print directly to standard output.

Python
C#

import os

os.environ["AZURE_EXPERIMENTAL_ENABLE_GENAI_TRACING"] = "true"

from azure.ai.projects.telemetry import AIProjectInstrumentor
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor

# Set up console tracing
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(
    SimpleSpanProcessor(ConsoleSpanExporter()))
trace.set_tracer_provider(tracer_provider)

# Enable instrumentation
AIProjectInstrumentor().instrument()

tracer = trace.get_tracer(__name__)

You can also use Aspire Dashboard as a local OTLP-compatible viewer. Install the OTLP exporter (pip install opentelemetry-exporter-otlp) and configure it as the exporter instead of ConsoleSpanExporter.

Reference: AIProjectInstrumentor, ConsoleSpanExporter

using Azure.AI.Projects;
using Azure.Identity;
using OpenTelemetry;
using OpenTelemetry.Resources;
using OpenTelemetry.Trace;

// Enable GenAI tracing
AppContext.SetSwitch("Azure.Experimental.EnableGenAITracing", true);

// Configure OpenTelemetry with console exporter
var tracerProvider = Sdk.CreateTracerProviderBuilder()
    .AddSource("Azure.AI.Projects.*")
    .SetResourceBuilder(
        ResourceBuilder.CreateDefault().AddService("AgentTracingSample"))
    .AddConsoleExporter()
    .Build();

using (tracerProvider)
{
    // Agent operations emit traces to the console
}

Enable content recording

Content recording captures message contents and tool call arguments in traces. This data might include sensitive user information.

Caution

Content recording captures user messages, tool call arguments, and model outputs. Only enable this setting in development environments. Don't enable content recording in production unless your compliance and privacy requirements allow it.

Python
C#

Set the environment variable before instrumenting:

import os

os.environ["OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT"] = "true"

Important

This variable controls recording only for built-in traces. When you use the @trace_function decorator on your own functions, all parameters and return values are always traced regardless of this setting.

Use an AppContext switch or the environment variable:

// Enable content recording
AppContext.SetSwitch("Azure.Experimental.TraceGenAIMessageContent", true);

Or set the OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT environment variable to true. If both the AppContext switch and the environment variable are set, the AppContext switch takes priority.

Disable automatic instrumentation (Python)

The Python SDK automatically instruments OpenAI Responses and Conversations API calls. To disable this auto-instrumentation, set AZURE_TRACING_GEN_AI_INSTRUMENT_RESPONSES_API to false before calling AIProjectInstrumentor().instrument(). When disabled, only explicit custom spans are recorded.

import os

os.environ["AZURE_TRACING_GEN_AI_INSTRUMENT_RESPONSES_API"] = "false"

Trace binary data (Python)

When content recording is enabled, the SDK traces file IDs and filenames by default. To include full image URLs (including base64 data URIs) and file data in spans, set AZURE_TRACING_GEN_AI_INCLUDE_BINARY_DATA to true.

import os

os.environ["AZURE_TRACING_GEN_AI_INCLUDE_BINARY_DATA"] = "true"

Warning

Enabling AZURE_TRACING_GEN_AI_INCLUDE_BINARY_DATA can significantly increase trace payload size. Some tracing backends have limitations on the maximum size of span data. Verify that your observability backend supports the expected payload sizes before enabling this setting.

Enable trace context propagation (Python)

Trace context propagation allows client-side spans to correlate with server-side spans from Azure OpenAI and other Azure services. When enabled, the SDK automatically injects W3C Trace Context headers (traceparent and tracestate) into HTTP requests made by OpenAI clients obtained via get_openai_client().

Trace context propagation is enabled by default when tracing is enabled. To disable it, set the AZURE_TRACING_GEN_AI_ENABLE_TRACE_CONTEXT_PROPAGATION environment variable to false, or pass the parameter directly:

from azure.ai.projects.telemetry import AIProjectInstrumentor

# Disable trace context propagation
AIProjectInstrumentor().instrument(enable_trace_context_propagation=False)

Changes to this setting only affect OpenAI clients obtained via get_openai_client() after the change. Previously acquired clients aren't affected.

Control baggage propagation (Python)

By default, only traceparent and tracestate headers are propagated. To also include the baggage header, set AZURE_TRACING_GEN_AI_TRACE_CONTEXT_PROPAGATION_INCLUDE_BAGGAGE to true.

Important

The baggage header can contain arbitrary key-value pairs, including user identifiers, session information, or other potentially sensitive data. Before enabling baggage propagation:

Audit what data your application and third-party libraries add to OpenTelemetry baggage.
Understand that baggage is sent to Azure OpenAI and might be logged by Azure services.
Never add sensitive information to baggage when propagation is enabled.

Note

The C# SDK relies on standard .NET System.Diagnostics.Activity propagation. Explicit per-request trace context injection isn't exposed as a separate SDK feature.

Trace custom functions

Python — use the `@trace_function` decorator

The trace_function decorator creates an OpenTelemetry span for each call to your function. Parameters are recorded as code.function.parameter.<name> and the return value as code.function.return.value.

from azure.ai.projects.telemetry import trace_function

@trace_function
def fetch_weather(location: str) -> str:
    """Get the current weather for a location."""
    return f"Weather in {location}: sunny, 72°F"

To use a custom span name instead of the function name, pass it as a parameter:

@trace_function("get-current-weather")
def fetch_weather(location: str) -> str:
    """Get the current weather for a location."""
    return f"Weather in {location}: sunny, 72°F"

The decorator records:

Parameters as code.function.parameter.<name> span attributes.
Return values as code.function.return.value.
Supported types: str, int, float, bool, and collections (list, dict, tuple, set). Object types are omitted.

Note

The OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT environment variable doesn't affect custom function tracing. The @trace_function decorator always traces parameters and return values.

C# — use `ActivitySource` manually

The C# SDK doesn't include a tracing decorator. Use the standard .NET ActivitySource to instrument your own functions:

using System.Diagnostics;

// Define a custom activity source
private static readonly ActivitySource s_source = new("MyApp.CustomFunctions");

string FetchWeather(string location)
{
    using var activity = s_source.StartActivity("FetchWeather");
    activity?.SetTag("input.location", location);

    var result = $"Weather in {location}: sunny, 72°F";
    activity?.SetTag("output", result);
    return result;
}

var tracerProvider = Sdk.CreateTracerProviderBuilder()
    .AddSource("Azure.AI.Projects.*")
    .AddSource("MyApp.CustomFunctions")
    .SetResourceBuilder(
        ResourceBuilder.CreateDefault().AddService("MyApp"))
    .AddConsoleExporter()
    .Build();

Configure instrumentation programmatically (Python)

As an alternative to environment variables, pass configuration parameters directly to AIProjectInstrumentor().instrument():

from azure.ai.projects.telemetry import AIProjectInstrumentor

AIProjectInstrumentor().instrument(
    enable_content_recording=True,
    enable_trace_context_propagation=True,
    enable_baggage_propagation=False,
)

Parameter	Environment variable equivalent	Default
`enable_content_recording`	`OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT`	`False`
`enable_trace_context_propagation`	`AZURE_TRACING_GEN_AI_ENABLE_TRACE_CONTEXT_PROPAGATION`	`True`*
`enable_baggage_propagation`	`AZURE_TRACING_GEN_AI_TRACE_CONTEXT_PROPAGATION_INCLUDE_BAGGAGE`	`False`

* Default is True when tracing is enabled.

When both a parameter and its corresponding environment variable are set, the parameter value takes priority.

Add custom attributes to spans (Python)

Create a custom SpanProcessor to inject metadata like session IDs into every span:

from opentelemetry.sdk.trace import SpanProcessor, ReadableSpan
from opentelemetry.trace import Span

class CustomAttributeSpanProcessor(SpanProcessor):
    def on_start(self, span: Span, parent_context=None):
        span.set_attribute("session.id", "user-session-abc")

    def on_end(self, span: ReadableSpan):
        pass

from typing import cast
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider

provider = cast(TracerProvider, trace.get_tracer_provider())
provider.add_span_processor(CustomAttributeSpanProcessor())

Control tracing behavior with environment variables

The following table lists all environment variables you can use to configure tracing behavior:

Variable	Language	Default	Description
`AZURE_EXPERIMENTAL_ENABLE_GENAI_TRACING`	Python, C#	`false`	Enable GenAI tracing instrumentation.
`OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT`	Python, C#	`false`	Capture message contents and tool call parameters.
`AZURE_TRACING_GEN_AI_ENABLE_TRACE_CONTEXT_PROPAGATION`	Python	`true`*	Inject W3C Trace Context headers into requests.
`AZURE_TRACING_GEN_AI_TRACE_CONTEXT_PROPAGATION_INCLUDE_BAGGAGE`	Python	`false`	Include the `baggage` header in trace context propagation.
`AZURE_TRACING_GEN_AI_INSTRUMENT_RESPONSES_API`	Python	`true`	Auto-instrument Responses and Conversations APIs.
`AZURE_TRACING_GEN_AI_INCLUDE_BINARY_DATA`	Python	`false`	Include image and file data in spans (not just file IDs).

* Default is true when tracing is enabled.

For the full list of environment variables and their behavior, see Tracing in the Azure AI Projects SDK README.

Security and privacy

Client-side tracing can capture sensitive information. Follow these practices to reduce risk:

Content recording: Captures user inputs, model responses, and tool call arguments. Disable in production unless required.
Baggage propagation: Can expose PII and session data. Disabled by default.
Trace context propagation: Sends trace IDs to Azure services. If compliance requirements prohibit sharing trace identifiers, disable it.
Secrets: Don't store secrets, credentials, or tokens in prompts, tool arguments, or span attributes.
Access control: Treat trace data as production telemetry. Apply the same access controls and retention policies you use for logs and metrics.

Troubleshooting

Issue	Resolution
Tracing doesn't produce any spans	Verify `AZURE_EXPERIMENTAL_ENABLE_GENAI_TRACING` is set to `true` before calling `AIProjectInstrumentor().instrument()` (Python) or before creating the tracer provider (C#).
Message content doesn't appear in spans	Set `OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT` to `true`.
Traces don't appear in Azure Monitor	Verify the Application Insights connection string is correct and the resource is accessible. Check that your account has the Log Analytics Reader role.
Client-side and server-side spans aren't correlated	(Python) Verify trace context propagation is enabled and that OpenAI clients are obtained via `get_openai_client()` after instrumentation.
Traces appear with a delay	Traces typically take 2-5 minutes to appear in the Foundry portal and Azure Monitor. Wait and refresh.

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Last updated on 2026-04-10

Add client-side tracing to Foundry agents (preview)

Prerequisites

Language-specific prerequisites

Install tracing packages

Enable GenAI tracing

Export traces to Azure Monitor

Export traces to the console

Enable content recording

Disable automatic instrumentation (Python)

Trace binary data (Python)

Enable trace context propagation (Python)

Control baggage propagation (Python)

Trace custom functions

Python — use the @trace_function decorator

C# — use ActivitySource manually

Configure instrumentation programmatically (Python)

Add custom attributes to spans (Python)

Control tracing behavior with environment variables

Security and privacy

Troubleshooting

Related content

Feedback

Additional resources

Python — use the `@trace_function` decorator

C# — use `ActivitySource` manually