azure-containerregistry-py

Azure Container Registry SDK for Python. Use for managing container images, artifacts, and repositories. Triggers: "azure-containerregistry", "ContainerRegistryClient", "container images", "docker registry", "ACR".

npx skills add https://github.com/microsoft/skills --skill azure-containerregistry-py

Azure Container Registry SDK for Python

Manage container images, artifacts, and repositories in Azure Container Registry.

Installation

pip install azure-containerregistry

Environment Variables

AZURE_CONTAINERREGISTRY_ENDPOINT=https://<registry-name>.azurecr.io  # 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. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
    • 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. Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically:
    • Sync: with <Client>(...) as client:
    • Async: async with <Client>(...) as client: and async with DefaultAzureCredential() as credential: (from azure.identity.aio)

Snippets may abbreviate this setup, but production code should always follow both rules.

Entra ID (Recommended)

import os
from azure.containerregistry import ContainerRegistryClient
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()

with ContainerRegistryClient(
    endpoint=os.environ["AZURE_CONTAINERREGISTRY_ENDPOINT"],
    credential=credential
) as client:
    # Use client here (see following sections for operations)
    ...

Anonymous Access (Public Registry)

from azure.containerregistry import ContainerRegistryClient

with ContainerRegistryClient(
    endpoint="https://mcr.microsoft.com",
    credential=None,
    audience="https://mcr.microsoft.com"
) as client:
    # Use client here (see following sections for operations)
    ...

List Repositories

with ContainerRegistryClient(endpoint, DefaultAzureCredential()) as client:
    for repository in client.list_repository_names():
        print(repository)

Repository Operations

Get Repository Properties

properties = client.get_repository_properties("my-image")
print(f"Created: {properties.created_on}")
print(f"Modified: {properties.last_updated_on}")
print(f"Manifests: {properties.manifest_count}")
print(f"Tags: {properties.tag_count}")

Update Repository Properties

from azure.containerregistry import RepositoryProperties

client.update_repository_properties(
    "my-image",
    properties=RepositoryProperties(
        can_delete=False,
        can_write=False
    )
)

Delete Repository

client.delete_repository("my-image")

List Tags

for tag in client.list_tag_properties("my-image"):
    print(f"{tag.name}: {tag.created_on}")

Filter by Order

from azure.containerregistry import ArtifactTagOrder

# Most recent first
for tag in client.list_tag_properties(
    "my-image",
    order_by=ArtifactTagOrder.LAST_UPDATED_ON_DESCENDING
):
    print(f"{tag.name}: {tag.last_updated_on}")

Manifest Operations

List Manifests

from azure.containerregistry import ArtifactManifestOrder

for manifest in client.list_manifest_properties(
    "my-image",
    order_by=ArtifactManifestOrder.LAST_UPDATED_ON_DESCENDING
):
    print(f"Digest: {manifest.digest}")
    print(f"Tags: {manifest.tags}")
    print(f"Size: {manifest.size_in_bytes}")

Get Manifest Properties

manifest = client.get_manifest_properties("my-image", "latest")
print(f"Digest: {manifest.digest}")
print(f"Architecture: {manifest.architecture}")
print(f"OS: {manifest.operating_system}")

Update Manifest Properties

from azure.containerregistry import ArtifactManifestProperties

client.update_manifest_properties(
    "my-image",
    "latest",
    properties=ArtifactManifestProperties(
        can_delete=False,
        can_write=False
    )
)

Delete Manifest

# Delete by digest
client.delete_manifest("my-image", "sha256:abc123...")

# Delete by tag
manifest = client.get_manifest_properties("my-image", "old-tag")
client.delete_manifest("my-image", manifest.digest)

Tag Operations

Get Tag Properties

tag = client.get_tag_properties("my-image", "latest")
print(f"Digest: {tag.digest}")
print(f"Created: {tag.created_on}")

Delete Tag

client.delete_tag("my-image", "old-tag")

Upload and Download Artifacts

from azure.containerregistry import ContainerRegistryClient

with ContainerRegistryClient(endpoint, DefaultAzureCredential()) as client:
    # Download manifest
    manifest = client.download_manifest("my-image", "latest")
    print(f"Media type: {manifest.media_type}")
    print(f"Digest: {manifest.digest}")

    # Download blob
    blob = client.download_blob("my-image", "sha256:abc123...")
    with open("layer.tar.gz", "wb") as f:
        for chunk in blob:
            f.write(chunk)

Async Client

from azure.containerregistry.aio import ContainerRegistryClient
from azure.identity.aio import DefaultAzureCredential

async def list_repos():
    async with DefaultAzureCredential() as credential:
        async with ContainerRegistryClient(endpoint, credential) as client:
            async for repo in client.list_repository_names():
                print(repo)

Clean Up Old Images

from datetime import datetime, timedelta, timezone

cutoff = datetime.now(timezone.utc) - timedelta(days=30)

for manifest in client.list_manifest_properties("my-image"):
    if manifest.last_updated_on < cutoff and not manifest.tags:
        print(f"Deleting {manifest.digest}")
        client.delete_manifest("my-image", manifest.digest)

Client Operations

OperationDescription
list_repository_namesList all repositories
get_repository_propertiesGet repository metadata
delete_repositoryDelete repository and all images
list_tag_propertiesList tags in repository
get_tag_propertiesGet tag metadata
delete_tagDelete specific tag
list_manifest_propertiesList manifests in repository
get_manifest_propertiesGet manifest metadata
delete_manifestDelete manifest by digest
download_manifestDownload manifest content
download_blobDownload layer blob

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. Always use context managers for clients and async credentials. Wrap every client in with Client(...) as client: (sync) or async with Client(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.
  3. Use Microsoft Entra ID for authentication in production
  4. Delete by digest not tag to avoid orphaned images
  5. Lock production images with can_delete=False
  6. Clean up untagged manifests regularly
  7. Use async client for high-throughput operations
  8. Order by last_updated to find recent/old images
  9. Check manifest.tags before deleting to avoid removing tagged images

Reference Files

FileContents
references/capabilities.mdAdditional non-hero capabilities, operation-group coverage, and production checklists.
references/non-hero-scenarios.mdDedicated non-hero examples for secondary/advanced scenarios.

Mais skills de microsoft

oss-growth
microsoft
Persona de growth hacker OSS
agent-framework-azure-ai-py
microsoft
Crie agentes do Azure AI Foundry usando o SDK Python do Microsoft Agent Framework (agent-framework-azure-ai). Use ao criar agentes persistentes com AzureAIAgentsProvider, usando ferramentas hospedadas (interpretador de código, pesquisa de arquivos, pesquisa na web), integrando servidores MCP, gerenciando threads de conversa ou implementando respostas em streaming. Abrange ferramentas de função, saídas estruturadas e agentes com múltiplas ferramentas.
development
airunway-aks-setup
microsoft
Configure o AI Runway no AKS — do cluster vazio ao modelo em execução. Abrange verificação do cluster, instalação do controlador, avaliação de GPU, configuração do provedor e primeira implantação. QUANDO: "configurar AI Runway", "integrar cluster AKS", "instalar AI Runway", "configuração do airunway", "implantar modelo no AKS", "inferência GPU no AKS", "configuração KAITO no AKS", "executar LLM no AKS", "vLLM no AKS", "configurar serviço de modelo no AKS", "controlador AI Runway".
devops
appinsights-instrumentation
microsoft
Orientação para instrumentar aplicações web com Azure Application Insights. Fornece padrões de telemetria, configuração de SDK e referências de configuração. QUANDO: como instrumentar o app, SDK do App Insights, padrões de telemetria, o que é App Insights, orientação sobre Application Insights, exemplos de instrumentação, melhores práticas de APM.
devops
applicationinsights-web-ts
microsoft
Instrumente aplicativos de navegador/web com o SDK JavaScript do Application Insights (@microsoft/applicationinsights-web). Use para Real User Monitoring (RUM) — visualizações de página, cliques, dependências AJAX/fetch, exceções, eventos personalizados e rastreamentos de agentes GenAI no lado do navegador correlacionados a rastreamentos OpenTelemetry no backend. Abrange o Script de Carregamento do SDK e a configuração via npm, extensões de frameworks (React, React Native, Angular), Click Analytics, inicializadores de telemetria e convenções semânticas GenAI do OTel para spans de agente/ferramenta/modelo emitidos pelo navegador.
devops
azure-ai-anomalydetector-java
microsoft
Crie aplicativos de detecção de anomalias com o SDK do Azure AI Anomaly Detector para Java. Use ao implementar detecção de anomalias univariada/multivariada, análise de séries temporais ou monitoramento com IA.
development
azure-ai-language-conversations-py
microsoft
Implemente o reconhecimento de linguagem conversacional (CLU) usando o SDK Python azure-ai-language-conversations. Use ao trabalhar com ConversationAnalysisClient para analisar intenção e entidades de conversas, criar recursos de NLP ou integrar o reconhecimento de linguagem em aplicativos.
development
azure-ai-ml-py
microsoft
SDK v2 do Azure Machine Learning para Python. Use para workspaces de ML, jobs, modelos, conjuntos de dados, computação e pipelines. Gatilhos: "azure-ai-ml", "MLClient", "workspace", "registro de modelos", "jobs de treinamento", "conjuntos de dados".
development