azure-monitor-opentelemetry-exporter-py
Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights. Triggers: "azure-monitor-opentelemetry-exporter", "AzureMonitorTraceExporter", "AzureMonitorMetricExporter", "AzureMonitorLogExporter".
Best use case
azure-monitor-opentelemetry-exporter-py is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights. Triggers: "azure-monitor-opentelemetry-exporter", "AzureMonitorTraceExporter", "AzureMonitorMetricExporter", "AzureMonitorLogExporter".
Teams using azure-monitor-opentelemetry-exporter-py should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/azure-monitor-opentelemetry-exporter-py/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How azure-monitor-opentelemetry-exporter-py Compares
| Feature / Agent | azure-monitor-opentelemetry-exporter-py | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights. Triggers: "azure-monitor-opentelemetry-exporter", "AzureMonitorTraceExporter", "AzureMonitorMetricExporter", "AzureMonitorLogExporter".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Azure Monitor OpenTelemetry Exporter for Python
Low-level exporter for sending OpenTelemetry traces, metrics, and logs to Application Insights.
## Installation
```bash
pip install azure-monitor-opentelemetry-exporter
```
## Environment Variables
```bash
APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/
```
## 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
```python
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
# Create exporter
exporter = AzureMonitorTraceExporter(
connection_string="InstrumentationKey=xxx;..."
)
# 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
```python
from opentelemetry import metrics
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter
# Create exporter
exporter = AzureMonitorMetricExporter(
connection_string="InstrumentationKey=xxx;..."
)
# 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
```python
import logging
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
# Create exporter
exporter = AzureMonitorLogExporter(
connection_string="InstrumentationKey=xxx;..."
)
# 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:
```python
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
# Connection string from environment
exporter = AzureMonitorTraceExporter()
```
## Azure AD Authentication
```python
from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
exporter = AzureMonitorTraceExporter(
credential=DefaultAzureCredential()
)
```
## Sampling
Use `ApplicationInsightsSampler` for consistent sampling:
```python
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:
```python
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
exporter = AzureMonitorTraceExporter(
connection_string="...",
storage_directory="/path/to/storage", # Custom storage path
disable_offline_storage=False # Enable retry (default)
)
```
## Disable Offline Storage
```python
exporter = AzureMonitorTraceExporter(
connection_string="...",
disable_offline_storage=True # No retry on failure
)
```
## Sovereign Clouds
```python
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
1. **Use BatchSpanProcessor** for production (not SimpleSpanProcessor)
2. **Use ApplicationInsightsSampler** for consistent sampling across services
3. **Enable offline storage** for reliability in production
4. **Use AAD authentication** instead of instrumentation keys
5. **Set export intervals** appropriate for your workload
6. **Use the distro** (`azure-monitor-opentelemetry`) unless you need custom pipelinesRelated Skills
web-vitals-monitor
Web Vitals Monitor - Auto-activating skill for Frontend Development. Triggers on: web vitals monitor, web vitals monitor Part of the Frontend Development skill category.
torchscript-exporter
Torchscript Exporter - Auto-activating skill for ML Deployment. Triggers on: torchscript exporter, torchscript exporter Part of the ML Deployment skill category.
setting-up-synthetic-monitoring
This skill automates the setup of synthetic monitoring for applications. It allows Claude to proactively track performance and availability by configuring uptime, transaction, and API monitoring. Use this skill when the user requests to "set up synthetic monitoring", "configure uptime monitoring", "track application performance", or needs help with "proactive performance tracking". The skill helps to identify critical endpoints and user journeys, design monitoring scenarios, and configure alerts and dashboards.
sla-monitor-setup
Sla Monitor Setup - Auto-activating skill for Enterprise Workflows. Triggers on: sla monitor setup, sla monitor setup Part of the Enterprise Workflows skill category.
implementing-real-user-monitoring
This skill assists in implementing Real User Monitoring (RUM) to capture and analyze actual user performance data. It helps set up tracking for key metrics like Core Web Vitals, page load times, and custom performance events. Use this skill when the user asks to "setup RUM", "implement real user monitoring", "track user experience", or needs assistance with "performance monitoring". It guides the user through choosing a RUM platform, designing an instrumentation strategy, and implementing the necessary tracking code.
prediction-monitor
Prediction Monitor - Auto-activating skill for ML Deployment. Triggers on: prediction monitor, prediction monitor Part of the ML Deployment skill category.
pipeline-monitoring-setup
Pipeline Monitoring Setup - Auto-activating skill for Data Pipelines. Triggers on: pipeline monitoring setup, pipeline monitoring setup Part of the Data Pipelines skill category.
monitoring-whale-activity
Track large cryptocurrency transactions and whale wallet movements in real-time. Use when tracking large holder movements, exchange flows, or wallet activity. Trigger with phrases like "track whales", "monitor large transfers", "check whale activity", "exchange inflows", or "watch wallet".
deploying-monitoring-stacks
This skill deploys monitoring stacks, including Prometheus, Grafana, and Datadog. It is used when the user needs to set up or configure monitoring infrastructure for applications or systems. The skill generates production-ready configurations, implements best practices, and supports multi-platform deployments. Use this when the user explicitly requests to deploy a monitoring stack, or mentions Prometheus, Grafana, or Datadog in the context of infrastructure setup.
monitoring-error-rates
Monitor and analyze application error rates to improve reliability. Use when tracking errors in applications including HTTP errors, exceptions, and database issues. Trigger with phrases like "monitor error rates", "track application errors", or "analyze error patterns".
monitoring-database-transactions
Monitor use when you need to work with monitoring and observability. This skill provides health monitoring and alerting with comprehensive guidance and automation. Trigger with phrases like "monitor system health", "set up alerts", or "track metrics".
monitoring-database-health
Monitor use when you need to work with monitoring and observability. This skill provides health monitoring and alerting with comprehensive guidance and automation. Trigger with phrases like "monitor system health", "set up alerts", or "track metrics".