# azure-monitor-opentelemetry-py

This skill guides Python projects through Azure Monitor OpenTelemetry setup for Application Insights. Use it to configure traces, metrics, and logs.

- skill · `type=skill` · Claude Code, GitHub Copilot · 2 installs · ★ 3149
- Author: Microsoft
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-py/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/azure-monitor-opentelemetry-py

## What it does

The skill shows how to connect a Python app to Application Insights with `azure-monitor-opentelemetry` and `configure_azure_monitor`. Its examples cover Flask, Django, and FastAPI, along with custom traces, metrics, and logs. Automatic instrumentation includes libraries such as `requests`, `httpx`, `redis`, and `psycopg2`.

Set `APPLICATIONINSIGHTS_CONNECTION_STRING` and install the package with `pip install azure-monitor-opentelemetry`. For production use of `DefaultAzureCredential`, set `AZURE_TOKEN_CREDENTIALS=prod`. Telemetry providers need an explicit flush and shutdown when the process exits, or buffered data may not be exported. The skill also covers sampling and Live Metrics.

## Requirements
- Works with: Azure

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add microsoft/skills --skill azure-monitor-opentelemetry-py -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/azure-monitor-opentelemetry-py/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/azure-monitor-opentelemetry-py/

### Codex

**Manually: put the files in place**
- File: `.agents/skills/azure-monitor-opentelemetry-py/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

**Manually: put the files in place**
- File: `.cursor/skills/azure-monitor-opentelemetry-py/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install microsoft/skills azure-monitor-opentelemetry-py
```

2. **Manually: put the files in place**
- File: `.github/skills/azure-monitor-opentelemetry-py/SKILL.md`

### Gemini CLI

**Platform CLI**
```bash
gemini skills install https://github.com/microsoft/skills --path .github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-py
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/azure-monitor-opentelemetry-py/SKILL.md` — The legacy .windsurf/skills/ path still works

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-py/SKILL.md

## When to use it
- connect a Python app to Application Insights
- enable automatic instrumentation for Flask or FastAPI
- add custom traces and metrics
- send Python logs to Application Insights
- configure sampling and Live Metrics

## Example prompts
- Show me how to connect my Python app to Application Insights with `configure_azure_monitor`.
- Set up `azure-monitor-opentelemetry` for Flask and authenticate with `DefaultAzureCredential`.
- How can I add a custom trace and turn on Live Metrics for Application Insights?

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 7090d5e01255b9f1.

BEGIN THIRD-PARTY FILE 7090d5e01255b9f1
````text
---
name: azure-monitor-opentelemetry-py
description: |
  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".
license: MIT
metadata:
  author: Microsoft
  version: "1.0.0"
  package: azure-monitor-opentelemetry
---

# Azure Monitor OpenTelemetry Distro for Python

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

## Installation

```bash
pip install azure-monitor-opentelemetry
```

## Environment Variables

```bash
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

```python
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`.

```python
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

```python
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
````
END THIRD-PARTY FILE 7090d5e01255b9f1

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