Which Python frameworks are supported for auto-instrumentation?

It automatically supports major libraries including Flask, Django, FastAPI, Requests, httpx, aiohttp, and several database drivers like psycopg2 and pymongo.

Does this support Azure Active Directory authentication?

Yes, you can pass a DefaultAzureCredential from the azure-identity package to the configuration function to use AAD instead of connection strings.

Can I control the amount of data sent to Azure to manage costs?

Yes, the skill provides patterns for setting a sampling_ratio (from 0.0 to 1.0) to control the volume of telemetry data ingested into Application Insights.

How do I start using this skill with my Python app?

Simply call the configure_azure_monitor() function at the beginning of your application entry point to enable auto-instrumentation for supported libraries.

Azure Monitor OpenTelemetry for Python

Name: Azure Monitor OpenTelemetry for Python
Author: microsoft

bymicrosoft

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1,777

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分析と監視

Simplifies Azure Application Insights integration with one-line OpenTelemetry auto-instrumentation for Python applications.

This skill provides comprehensive guidance and implementation patterns for integrating the Azure Monitor OpenTelemetry Distro into Python projects. It enables developers to quickly set up distributed tracing, metrics, and logging for popular frameworks like Flask, Django, and FastAPI with minimal configuration. By grounding Claude in best practices for observability, it helps automate the setup of cloud role names, sampling ratios, and Azure AD authentication to ensure production-ready monitoring in Azure environments.

主な機能

01One-line configuration for Application Insights via OpenTelemetry.

02Support for custom distributed tracing, metrics, and structured logging.

03Configurable sampling and cloud role names for complex microservices architectures.

04Integrated Azure Active Directory (AAD) authentication for secure telemetry ingestion.

051,777 GitHub stars

06Automatic instrumentation for popular frameworks like Flask, Django, and FastAPI.

ユースケース

01Adding observability to a new Python web service with a single line of code.

02Migrating existing Python apps from legacy SDKs to the OpenTelemetry-based Azure Monitor distro.

03Implementing secure, identity-based telemetry reporting for enterprise Azure environments.

主な機能

01One-line configuration for Application Insights via OpenTelemetry.

02Support for custom distributed tracing, metrics, and structured logging.

03Configurable sampling and cloud role names for complex microservices architectures.

04Integrated Azure Active Directory (AAD) authentication for secure telemetry ingestion.

051,777 GitHub stars

06Automatic instrumentation for popular frameworks like Flask, Django, and FastAPI.

ユースケース

01Adding observability to a new Python web service with a single line of code.

02Migrating existing Python apps from legacy SDKs to the OpenTelemetry-based Azure Monitor distro.

03Implementing secure, identity-based telemetry reporting for enterprise Azure environments.