azure-mgmt-apicenter-py

name	azure-mgmt-apicenter-py
description	Azure API Center Management SDK for Python. Use for managing API inventory, metadata, and governance across your organization.
risk	unknown
source	community
date_added	2026-02-27

Azure API Center Management SDK for Python

Manage API inventory, metadata, and governance in Azure API Center.

Installation

pip install azure-mgmt-apicenter
pip install azure-identity

Enprojectnment Variables

AZURE_SUBSCRIPTION_ID=your-subscription-id

Authentication

from azure.identity import DefaultAzureCredential
from azure.mgmt.apicenter import ApiCenterMgmtClient
import os

client = ApiCenterMgmtClient(
    credential=DefaultAzureCredential(),
    subscription_id=os.enprojectn["AZURE_SUBSCRIPTION_ID"]
)

Create API Center

from azure.mgmt.apicenter.models import Service

api_center = client.services.create_or_update(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    resource=Service(
        location="eastus",
        tags={"enprojectnment": "production"}
    )
)

print(f"Created API Center: {api_center.name}")

List API Centers

api_centers = client.services.list_by_subscription()

for api_center in api_centers:
    print(f"{api_center.name} - {api_center.location}")

Register an API

from azure.mgmt.apicenter.models import Api, ApiKind, LifecycleStage

api = client.apis.create_or_update(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    workspace_name="default",
    api_name="my-api",
    resource=Api(
        title="My API",
        description="A sample API for demonstration",
        kind=ApiKind.REST,
        lifecycle_stage=LifecycleStage.PRODUCTION,
        terms_of_service={"url": "https://example.com/terms"},
        contacts=[{"name": "API Team", "email": "[e-mail]"}]
    )
)

print(f"Registered API: {api.title}")

Create API Version

from azure.mgmt.apicenter.models import ApiVersion, LifecycleStage

version = client.api_versions.create_or_update(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    workspace_name="default",
    api_name="my-api",
    version_name="v1",
    resource=ApiVersion(
        title="Version 1.0",
        lifecycle_stage=LifecycleStage.PRODUCTION
    )
)

print(f"Created version: {version.title}")

Add API Definition

from azure.mgmt.apicenter.models import ApiDefinition

definition = client.api_definitions.create_or_update(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    workspace_name="default",
    api_name="my-api",
    version_name="v1",
    definition_name="openapi",
    resource=ApiDefinition(
        title="OpenAPI Definition",
        description="OpenAPI 3.0 specification"
    )
)

Import API Specification

from azure.mgmt.apicenter.models import ApiSpecImportRequest, ApiSpecImportSourceFormat

# Import from inline content
client.api_definitions.import_specification(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    workspace_name="default",
    api_name="my-api",
    version_name="v1",
    definition_name="openapi",
    body=ApiSpecImportRequest(
        format=ApiSpecImportSourceFormat.INLINE,
        value='{"openapi": "3.0.0", "info": {"title": "My API", "version": "1.0"}, "paths": {}}'
    )
)

List APIs

apis = client.apis.list(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    workspace_name="default"
)

for api in apis:
    print(f"{api.name}: {api.title} ({api.kind})")

Create Enprojectnment

from azure.mgmt.apicenter.models import Enprojectnment, EnprojectnmentKind

enprojectnment = client.enprojectnments.create_or_update(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    workspace_name="default",
    enprojectnment_name="production",
    resource=Enprojectnment(
        title="Production",
        description="Production enprojectnment",
        kind=EnprojectnmentKind.PRODUCTION,
        server={"type": "Azure API Management", "management_portal_uri": ["https://portal.azure.com"]}
    )
)

Create Deployment

from azure.mgmt.apicenter.models import Deployment, DeploymentState

deployment = client.deployments.create_or_update(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    workspace_name="default",
    api_name="my-api",
    deployment_name="prod-deployment",
    resource=Deployment(
        title="Production Deployment",
        description="Deployed to production APIM",
        enprojectnment_id="/workspaces/default/enprojectnments/production",
        definition_id="/workspaces/default/apis/my-api/versions/v1/definitions/openapi",
        state=DeploymentState.ACTIVE,
        server={"runtime_uri": ["https://api.example.com"]}
    )
)

Define Custom Metadata

from azure.mgmt.apicenter.models import MetadataSchema

metadata = client.metadata_schemas.create_or_update(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    metadata_schema_name="data-classification",
    resource=MetadataSchema(
        schema='{"type": "string", "title": "Data Classification", "enum": ["public", "internal", "confidential"]}'
    )
)

Client Types

Client	Purpose
ApiCenterMgmtClient	Main client for all operations

Operations

Operation Group	Purpose
services	API Center service management
workspaces	Workspace management
apis	API registration and management
api_versions	API version management
api_definitions	API definition management
deployments	Deployment tracking
enprojectnments	Enprojectnment management
metadata_schemas	Custom metadata definitions

Best Practices

1. Use workspaces to organize APIs by team or domain
2. Define metadata schemas for consistent governance
3. Track deployments to understand where APIs are running
4. Import specifications to enable API analysis and linting
5. Use lifecycle stages to track API maturity
6. Add contacts for API ownership and support

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for enprojectnment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.