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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

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

🔑 Auth & lifecycle: This distro is configured with a connection string by design, but for AAD-authenticated ingestion (where supported) prefer DefaultAzureCredential via the credential= parameter — see the Azure AD Authentication section. Any Azure SDK clients you create alongside the exporter should be wrapped in with/async with blocks (and async credentials from azure.identity.aio likewise).

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 Configuration

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

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

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. 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()

configure_azure_monitor(
    credential=credential
)

Auto-Instrumentations Included

Library	Telemetry Type
Flask	Traces
Django	Traces
FastAPI	Traces
Requests	Traces
urllib3	Traces
httpx	Traces
aiohttp	Traces
psycopg2	Traces
pymysql	Traces
pymongo	Traces
redis	Traces

Configuration Options

Parameter	Description	Default
connection_string	Application Insights connection string	From env var
credential	Azure credential for AAD auth	None
sampling_ratio	Sampling rate (0.0 to 1.0)	1.0
resource	OpenTelemetry Resource	Auto-detected
instrumentations	List of instrumentations to enable	All
enable_live_metrics	Enable Live Metrics stream	False

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. Flush and shut down providers at process exit. Call the shutdown/flush APIs (e.g. tracer_provider.shutdown(), meter_provider.shutdown(), logger_provider.shutdown()) at process exit to flush telemetry before the process terminates.
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