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Azure Monitor Opentelemetry Py

MicrosoftOfficialCreated by Microsoft

  • Skill
  • Tool integration
  • Cloud and DevOps
  • MIT
  • Low risk

From the author

Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation. Triggers: "azure-monitor-opentelemetry", "configure_azure_monitor", "Application Insights", "OpenTelemetry distro", "auto-instrumentation".
— the description by Microsoft

AutoGineer summary

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

The azure-monitor-opentelemetry-py package provides a simplified configuration for integrating Python applications with Azure Monitor Application Insights. It leverages OpenTelemetry to automatically capture traces, metrics, and logs from common libraries and frameworks. The tool supports both connection-string-based authentication and Microsoft Entra ID authentication via DefaultAzureCredential, allowing for flexible deployment across development and production environments.

What it does

  • Provides one-line setup for Application Insights integration.
  • Enables auto-instrumentation for common Python frameworks and libraries.
  • Supports custom telemetry collection for traces, metrics, and logs.
  • Allows configuration of sampling ratios and cloud role names.
  • Supports Azure AD authentication for secure ingestion.
  • Provides options to selectively enable or disable specific instrumentations.

When to use it

  • When you need to monitor Python applications using Azure Application Insights.
  • When you want to implement OpenTelemetry-based observability with minimal code changes.
  • When you need to capture telemetry from frameworks like Flask, Django, or FastAPI.
  • When you require secure, identity-based authentication for telemetry ingestion.

Advantages

  • Simplifies observability setup with a single configuration call.
  • Reduces manual instrumentation effort through automatic library support.
  • Integrates natively with Azure identity services for production security.
  • Offers granular control over telemetry sampling and instrumentation scope.

Usage examples

“How do I set up Application Insights for my Python application?”
You can use configure_azure_monitor() from the azure.monitor.opentelemetry package to initialize telemetry collection.
“How can I authenticate telemetry ingestion using Azure AD?”
Pass an instance of DefaultAzureCredential to the configure_azure_monitor function to enable Microsoft Entra ID authentication.
“How do I sample only 10% of my application requests?”
Set the sampling_ratio parameter to 0.1 within the configure_azure_monitor function call.
“How can I disable specific auto-instrumentations?”
Use the instrumentation_options parameter in configure_azure_monitor to set specific libraries to enabled: False.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/microsoft/azure-monitor-opentelemetry-py/download
  2. Unzip it into the cross-client skills folder of your project:

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

Documentation by Microsoft

Azure Monitor OpenTelemetry Distro for Python

One-line setup for Application Insights with OpenTelemetry auto-instrumentation.

Installation

pip install azure-monitor-opentelemetry

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:

  1. 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.

  2. 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.

Quick Start

from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry import configure_azure_monitor

# Connection string identifies the App Insights resource (read from APPLICATIONINSIGHTS_CONNECTION_STRING env var).
# DefaultAzureCredential authenticates ingestion via Microsoft Entra ID (preferred over instrumentation-key-only auth).
configure_azure_monitor(
    credential=DefaultAzureCredential(),
)

# Your application code...

Explicit Connection String

Pass the connection string explicitly by reading it from the environment variable. The value includes both InstrumentationKey and IngestionEndpoint.

import os
from azure.monitor.opentelemetry import configure_azure_monitor

# Read the full connection string from the environment.
# Format: "InstrumentationKey=<key>;IngestionEndpoint=https://<id>.in.applicationinsights.azure.com/"
connection_string = os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"]

try:
    configure_azure_monitor(
        connection_string=connection_string,
    )
    # Your application code...
except Exception as exc:
    raise RuntimeError(f"Azure Monitor configuration failed: {exc}") from exc

With Flask

from flask import Flask
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

app = Flask(__name__)

@app.route("/")
def hello():
    return "Hello, World!"

if __name__ == "__main__":
    app.run()

With Django

# settings.py
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

# Django settings...

With FastAPI

from fastapi import FastAPI
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

app = FastAPI()

@app.get("/")
async def root():
    return {"message": "Hello World"}

Custom Traces

from opentelemetry import trace
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

tracer = trace.get_tracer(__name__)

with tracer.start_as_current_span("my-operation") as span:
    span.set_attribute("custom.attribute", "value")
    # Do work...

Custom Metrics

from opentelemetry import metrics
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

meter = metrics.get_meter(__name__)
counter = meter.create_counter("my_counter")

counter.add(1, {"dimension": "value"})

Custom Logs

import logging
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)

logger.info("This will appear in Application Insights")
logger.error("Errors are captured too", exc_info=True)

Sampling

from azure.monitor.opentelemetry import configure_azure_monitor

# Sample 10% of requests
configure_azure_monitor(
    sampling_ratio=0.1
)

Cloud Role Name

Set cloud role name for Application Map:

from azure.monitor.opentelemetry import configure_azure_monitor
from opentelemetry.sdk.resources import Resource, SERVICE_NAME

configure_azure_monitor(
    resource=Resource.create({SERVICE_NAME: "my-service-name"})
)

Disable Specific Instrumentations

from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor(
    instrumentations=["flask", "requests"]  # Only enable these
)

Enable Live Metrics

from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor(
    enable_live_metrics=True
)

Azure AD Authentication

from azure.monitor.opentelemetry import configure_azure_monitor
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential

# Local dev: DefaultAzureCredential. In production, set AZURE_TOKEN_CREDENTIALS=prod or use a specific credential.
credential = DefaultAzureCredential()
# 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()

configure_azure_monitor(
    credential=credential
)

Auto-Instrumentations Included

LibraryTelemetry Type
FlaskTraces
DjangoTraces
FastAPITraces
RequestsTraces
urllib3Traces
httpxTraces
aiohttpTraces
psycopg2Traces
pymysqlTraces
pymongoTraces
redisTraces

Configuration Options

ParameterDescriptionDefault
connection_stringApplication Insights connection stringFrom env var
credentialAzure credential for AAD authNone
sampling_ratioSampling rate (0.0 to 1.0)1.0
resourceOpenTelemetry ResourceAuto-detected
instrumentationsList of instrumentations to enableAll
enable_live_metricsEnable Live Metrics streamFalse

Best Practices

  1. 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.

  2. Call provider.shutdown() / force_flush() at process exit to flush telemetry — providers are not context managers.

  3. Call configure_azure_monitor() early — Before importing instrumented libraries

  4. Use environment variables for connection string in production

  5. Set cloud role name for multi-service applications

  6. Enable sampling in high-traffic applications

  7. Use structured logging for better log analytics queries

  8. Add custom attributes to spans for better debugging

  9. Use Microsoft Entra authentication for production workloads

Reference Files

FileContents
references/capabilities.mdAdditional non-hero capabilities, operation-group coverage, and production checklists.
references/non-hero-scenarios.mdDedicated non-hero examples for secondary/advanced scenarios.

Files

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

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/skills · .github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-py · commit 0696600
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

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