Azure Monitor Opentelemetry Exporter Py

MicrosoftOfficialCreated by Microsoft

Skill
Tool integration
Cloud and DevOps
MIT
Low risk

From the author

Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights. Triggers: "azure-monitor-opentelemetry-exporter", "AzureMonitorTraceExporter", "AzureMonitorMetricExporter", "AzureMonitorLogExporter".

— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The azure-monitor-opentelemetry-exporter-py provides a low-level exporter for Python applications using the OpenTelemetry standard. It enables developers to send custom telemetry data, including traces, metrics, and logs, directly to Azure Application Insights. This tool is designed for scenarios requiring fine-grained control over telemetry pipelines, supporting advanced features like Microsoft Entra ID authentication, custom sampling, and offline storage for reliable data delivery.

What it does

Exports OpenTelemetry traces, metrics, and logs to Azure Application Insights.
Supports Microsoft Entra ID authentication via DefaultAzureCredential.
Provides configurable offline storage for reliable telemetry retries.
Allows for custom sampling strategies using ApplicationInsightsSampler.
Supports sovereign cloud configurations for specialized environments.

When to use it

When you need to build a custom OpenTelemetry pipeline instead of using auto-instrumentation.
When your application requires fine-grained control over how telemetry is exported.
When you need to integrate telemetry data into Azure Monitor from a Python-based service.
When you require reliable telemetry delivery with offline retry capabilities.

Advantages

Native integration with Azure Monitor and Application Insights.
Supports secure authentication using Azure identity credentials.
Configurable retry logic via offline storage.
Flexible configuration for various telemetry types.

Usage examples

“How do I export traces to Application Insights using OpenTelemetry in Python?”: You can use the AzureMonitorTraceExporter class, configure it with your connection string and credentials, and add it to your TracerProvider using a BatchSpanProcessor.
“How can I enable offline storage for my telemetry exporter?”: Initialize the exporter with the storage_directory parameter and ensure disable_offline_storage is set to False to enable automatic retries on failure.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/microsoft/azure-monitor-opentelemetry-exporter-py/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip azure-monitor-opentelemetry-exporter-py.zip -d .agents/skills

Documentation by Microsoft

Azure Monitor OpenTelemetry Exporter for Python

Low-level exporter for sending OpenTelemetry traces, metrics, and logs to Application Insights.

Installation

pip install azure-monitor-opentelemetry-exporter

Environment Variables

APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production

Authentication & Lifecycle

🔑 Two rules apply to every code sample below:
Prefer DefaultAzureCredential for ingestion auth when supported. APPLICATIONINSIGHTS_CONNECTION_STRING identifies the target Application Insights resource, and credential=DefaultAzureCredential(...) provides Microsoft Entra authentication.
Local dev: DefaultAzureCredential works as-is.
Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.
Providers are not context managers. Flush and shut down telemetry providers explicitly at process exit so buffers are exported deterministically.
Snippets may abbreviate this setup, but production code should always follow both rules.

When to Use

Scenario	Use
Quick setup, auto-instrumentation	`azure-monitor-opentelemetry` (distro)
Custom OpenTelemetry pipeline	`azure-monitor-opentelemetry-exporter` (this)
Fine-grained control over telemetry	`azure-monitor-opentelemetry-exporter` (this)

Trace Exporter

from azure.identity import DefaultAzureCredential
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env to identify the resource;
# DefaultAzureCredential authenticates ingestion via Microsoft Entra ID.
exporter = AzureMonitorTraceExporter(
    credential=DefaultAzureCredential(),
)

# Configure tracer provider
trace.set_tracer_provider(TracerProvider())
trace.get_tracer_provider().add_span_processor(
    BatchSpanProcessor(exporter)
)

# Use tracer
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("my-span"):
    print("Hello, World!")

Metric Exporter

from azure.identity import DefaultAzureCredential
from opentelemetry import metrics
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter

# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env; AAD-authenticated ingestion via DefaultAzureCredential.
exporter = AzureMonitorMetricExporter(
    credential=DefaultAzureCredential(),
)

# Configure meter provider
reader = PeriodicExportingMetricReader(exporter, export_interval_millis=60000)
metrics.set_meter_provider(MeterProvider(metric_readers=[reader]))

# Use meter
meter = metrics.get_meter(__name__)
counter = meter.create_counter("requests_total")
counter.add(1, {"route": "/api/users"})

Log Exporter

import logging
from azure.identity import DefaultAzureCredential
from opentelemetry._logs import set_logger_provider
from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter

# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env; AAD-authenticated ingestion via DefaultAzureCredential.
exporter = AzureMonitorLogExporter(
    credential=DefaultAzureCredential(),
)

# Configure logger provider
logger_provider = LoggerProvider()
logger_provider.add_log_record_processor(BatchLogRecordProcessor(exporter))
set_logger_provider(logger_provider)

# Add handler to Python logging
handler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)
logging.getLogger().addHandler(handler)

# Use logging
logger = logging.getLogger(__name__)
logger.info("This will be sent to Application Insights")

From Environment Variable

Exporters read APPLICATIONINSIGHTS_CONNECTION_STRING automatically:

from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Connection string from environment; AAD-authenticated ingestion via DefaultAzureCredential.
exporter = AzureMonitorTraceExporter(
    credential=DefaultAzureCredential(),
)

Azure AD Authentication

from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()

exporter = AzureMonitorTraceExporter(
    credential=credential
)

Sampling

Use ApplicationInsightsSampler for consistent sampling:

from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.sampling import ParentBasedTraceIdRatio
from azure.monitor.opentelemetry.exporter import ApplicationInsightsSampler

# Sample 10% of traces
sampler = ApplicationInsightsSampler(sampling_ratio=0.1)

trace.set_tracer_provider(TracerProvider(sampler=sampler))

Offline Storage

Configure offline storage for retry:

from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

exporter = AzureMonitorTraceExporter(
    credential=DefaultAzureCredential(),
    storage_directory="/path/to/storage",  # Custom storage path
    disable_offline_storage=False  # Enable retry (default)
)

Disable Offline Storage

exporter = AzureMonitorTraceExporter(
    credential=DefaultAzureCredential(),
    disable_offline_storage=True  # No retry on failure
)

Sovereign Clouds

from azure.identity import AzureAuthorityHosts, DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Azure Government
credential = DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_GOVERNMENT)
exporter = AzureMonitorTraceExporter(
    connection_string="InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.us/",
    credential=credential
)

Exporter Types

Exporter	Telemetry Type	Application Insights Table
`AzureMonitorTraceExporter`	Traces/Spans	requests, dependencies, exceptions
`AzureMonitorMetricExporter`	Metrics	customMetrics, performanceCounters
`AzureMonitorLogExporter`	Logs	traces, customEvents

Configuration Options

Parameter	Description	Default
`connection_string`	Application Insights connection string	From env var
`credential`	Azure credential for AAD auth	None
`disable_offline_storage`	Disable retry storage	False
`storage_directory`	Custom storage path	Temp directory

Best Practices

Pick sync OR async and stay consistent. Do not mix azure.xxx sync clients with azure.xxx.aio async clients in the same call path. Choose one mode per module.
Call provider.shutdown() / force_flush() at process exit to flush telemetry — providers are not context managers.
Use BatchSpanProcessor for production (not SimpleSpanProcessor)
Use ApplicationInsightsSampler for consistent sampling across services
Enable offline storage for reliability in production
Use Microsoft Entra authentication instead of instrumentation keys
Set export intervals appropriate for your workload
Use the distro (azure-monitor-opentelemetry) unless you need custom pipelines

Reference Files

File	Contents
references/capabilities.md	Additional non-hero capabilities, operation-group coverage, and production checklists.
references/non-hero-scenarios.md	Dedicated non-hero examples for secondary/advanced scenarios.

Files

ATTRIBUTION.mdAdded by AutoGineer1.0 KiB
LICENSELicence file added by AutoGineer1.1 KiB
SKILL.md8.9 KiB
references/capabilities.md2.1 KiB
references/non-hero-scenarios.md3.0 KiB

references/capabilities.md

azure-monitor-opentelemetry-exporter-py capability coverage

SDK/package: azure-monitor-opentelemetry-exporter

This index maps hero scenarios in SKILL.md and links non-hero scenarios documented in dedicated reference files.

Hero scenarios covered in SKILL.md

Trace Exporter
Metric Exporter
Log Exporter
From Environment Variable

Non-hero scenarios

Azure AD Authentication: Dedicated example and implementation notes.
See: non-hero-scenarios.md#azure-ad-authentication
Sampling: Use ApplicationInsightsSampler for consistent sampling:
See: non-hero-scenarios.md#sampling
Offline Storage: Configure offline storage for retry:
See: non-hero-scenarios.md#offline-storage
Disable Offline Storage: Dedicated example and implementation notes.
See: non-hero-scenarios.md#disable-offline-storage
Sovereign Clouds: Dedicated example and implementation notes.
See: non-hero-scenarios.md#sovereign-clouds
Exporter Types: | Exporter | Telemetry Type | Application Insights Table |
See: non-hero-scenarios.md#exporter-types
Configuration Options: | Parameter | Description | Default |
See: non-hero-scenarios.md#configuration-options

non-hero-scenarios.md: Dedicated non-hero examples and implementation notes.

API breadth checklist

Verify client/auth mode for the environment before coding.
Confirm operation-group/method names against current Microsoft Learn API reference.
For Python SDKs with both sync and async clients, document both forms without a blanket preference.
Include cleanup/delete paths for created resources in examples.
Prefer idempotent create/update operations where available.
Validate paging/LRO/error-handling patterns for production paths.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

What our scanner noticed· Low risk

Instructions that use the network

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Attribution and licence

Created by

Microsoft

Publisher

Microsoft (Official)

Source

microsoft/skills · .github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-exporter-py · commit 0696600

Licence

MIT License (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

AutoGineer is not affiliated with or endorsed by Microsoft.

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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download azure-monitor-opentelemetry-exporter-py.zip