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".
Install / Use
npx skills add microsoft/skills --skill azure-monitor-opentelemetry-exporter-pyInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
OperationsSupported Platforms
Our assessment of azure-monitor-opentelemetry-exporter-py
azure-monitor-opentelemetry-exporter-py scores 93/100 on our quality scale, 136th of 503 Operations skills we index (top 28%).
Its SKILL.md is 8.9 KB long, well organised into 36 sections with 11 code examples: a thorough specification that gives an agent plenty to work with.
With 3,051 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 5 days ago, so azure-monitor-opentelemetry-exporter-py is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.
Automated pattern scan on 2026-09-29. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
azure-monitor-opentelemetry-exporter-py compared with similar skills
All 4 of these similar skills score higher than azure-monitor-opentelemetry-exporter-py; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| azure-monitor-opentelemetry-exporter-py (this skill)by microsoft | 93 | 3.1k | 5d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 86.1k | 13d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 74.1k | today | CLAUDE.md |
| crawl4aiby unclecode | 100 | 84.4k | 4d ago | MCP Server |
| Scraplingby D4Vinci | 100 | 84.4k | today | MCP Server |
Frequently asked questions
- How do I install azure-monitor-opentelemetry-exporter-py?
- Run
npx skills add microsoft/skills --skill azure-monitor-opentelemetry-exporter-py. The install tabs above show the steps for each supported agent. - Which AI agents does azure-monitor-opentelemetry-exporter-py work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is azure-monitor-opentelemetry-exporter-py safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is azure-monitor-opentelemetry-exporter-py still maintained?
- The repository was last updated 5 days ago, so azure-monitor-opentelemetry-exporter-py is actively maintained.
Skill content
View source on GitHubname: azure-monitor-opentelemetry-exporter-py description: | Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights. Triggers: "azure-monitor-opentelemetry-exporter", "AzureMonitorTraceExporter", "AzureMonitorMetricExporter", "AzureMonitorLogExporter". license: MIT metadata: author: Microsoft version: "1.0.0" package: azure-monitor-opentelemetry-exporter
Azure Monitor OpenTelemetry Exporter for Python
Low-level exporter for sending OpenTelemetry traces, metrics, and logs to Application Insights.
Installation
pip install azure-monitor-opentelemetry-exporter
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:
- Prefer
DefaultAzureCredentialfor ingestion auth when supported.APPLICATIONINSIGHTS_CONNECTION_STRINGidentifies the target Application Insights resource, andcredential=DefaultAzureCredential(...)provides Microsoft Entra authentication.
- Local dev:
DefaultAzureCredentialworks as-is.- Production: set
AZURE_TOKEN_CREDENTIALS=prod(orAZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.- 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.
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
from azure.identity import DefaultAzureCredential
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env to identify the resource;
# DefaultAzureCredential authenticates ingestion via Microsoft Entra ID.
exporter = AzureMonitorTraceExporter(
credential=DefaultAzureCredential(),
)
# 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
from azure.identity import DefaultAzureCredential
from opentelemetry import metrics
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter
# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env; AAD-authenticated ingestion via DefaultAzureCredential.
exporter = AzureMonitorMetricExporter(
credential=DefaultAzureCredential(),
)
# 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
import logging
from azure.identity import DefaultAzureCredential
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
# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env; AAD-authenticated ingestion via DefaultAzureCredential.
exporter = AzureMonitorLogExporter(
credential=DefaultAzureCredential(),
)
# 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:
from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
# Connection string from environment; AAD-authenticated ingestion via DefaultAzureCredential.
exporter = AzureMonitorTraceExporter(
credential=DefaultAzureCredential(),
)
Azure AD Authentication
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
# 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()
exporter = AzureMonitorTraceExporter(
credential=credential
)
Sampling
Use ApplicationInsightsSampler for consistent sampling:
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:
from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
exporter = AzureMonitorTraceExporter(
credential=DefaultAzureCredential(),
storage_directory="/path/to/storage", # Custom storage path
disable_offline_storage=False # Enable retry (default)
)
Disable Offline Storage
exporter = AzureMonitorTraceExporter(
credential=DefaultAzureCredential(),
disable_offline_storage=True # No retry on failure
)
Sovereign Clouds
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
- Pick sync OR async and stay consistent. Do not mix
azure.xxxsync clients withazure.xxx.aioasync clients in the same call path. Choose one mode per module. - Call
provider.shutdown()/force_flush()at process exit to flush telemetry — providers are not context managers. - Use BatchSpanProcessor for production (not SimpleSpanProcessor)
- Use ApplicationInsightsSampler for consistent sampling across services
- Enable offline storage for reliability in production
- Use Microsoft Entra authentication instead of instrumentation keys
- Set export intervals appropriate for your workload
- Use the distro (
azure-monitor-opentelemetry) unless you need custom pipelines
Reference Files
| File | Contents | |------|----------| | references/capabilities.md | Additional non-hero capabilities, operation-group coverage, and production checklists. | | references/non-hero-scenarios.md | Dedicated non-hero examples for secondary/advanced scenarios. |
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
