azure-identity-rust

Azure Identity library for Rust. Microsoft Entra ID authentication for all Azure SDK clients. Triggers: "azure identity rust", "DeveloperToolsCredential", "authentication rust", "managed identity rust", "credential rust", "Entra ID rust".

npx skills add https://github.com/microsoft/skills --skill azure-identity-rust

Azure Identity library for Rust

Microsoft Entra ID authentication for Azure SDK clients.

Use this skill when:

  • An app needs to authenticate to Azure services from Rust
  • You need DeveloperToolsCredential for local development
  • You need ManagedIdentityCredential for Azure-hosted workloads
  • You need service principal auth with secret or certificate

IMPORTANT: Only use official azure_* crates published by the azure-sdk crates.io user. Do NOT use the deprecated azure_sdk_* crates (MindFlavor/AzureSDKForRust) or community crates. Official crates use underscores in names and none have version 0.21.0.

Note: The Rust SDK does not have DefaultAzureCredential. Use DeveloperToolsCredential for local development and ManagedIdentityCredential for production.

// Incorrect in Rust: this type does not exist in azure_identity
use azure_identity::DefaultAzureCredential;

Installation

cargo add azure_identity azure_core tokio

If your code uses azure_core types directly, add azure_core to Cargo.toml. If you only use service-crate re-exports, direct azure_core dependency is optional.

Environment Variables

AZURE_TENANT_ID=<your-tenant-id>         # Required for service principal auth
AZURE_CLIENT_ID=<your-client-id>         # Required for service principal or user-assigned managed identity
AZURE_CLIENT_SECRET=<your-client-secret> # Required for ClientSecretCredential

Authentication

DeveloperToolsCredential (Local Development)

Tries Azure CLI then Azure Developer CLI:

use azure_identity::DeveloperToolsCredential;
use azure_security_keyvault_secrets::SecretClient;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Local dev: DeveloperToolsCredential. Production: use ManagedIdentityCredential.
    let credential = DeveloperToolsCredential::new(None)?;
    let client = SecretClient::new(
        "https://<vault-name>.vault.azure.net/",
        credential.clone(),
        None,
    )?;

    let secret = client.get_secret("secret-name", None).await?.into_model()?;
    println!("Secret: {:?}", secret.value);
    Ok(())
}

Ensure you are logged in:

az login        # Azure CLI
azd auth login  # or Azure Developer CLI
OrderCredentialLogin Command
1AzureCliCredentialaz login
2AzureDeveloperCliCredentialazd auth login

ManagedIdentityCredential (Production)

For Azure-hosted resources (VMs, App Service, Functions, AKS):

use azure_identity::ManagedIdentityCredential;

// System-assigned managed identity
let credential = ManagedIdentityCredential::new(None)?;

ClientSecretCredential (Service Principal)

For CI/CD pipelines and service accounts:

use azure_identity::ClientSecretCredential;

let credential = ClientSecretCredential::new(
    "<tenant-id>",
    "<client-id>",
    "<client-secret>",
    None,
)?;

Credential Types

CredentialUse Case
DeveloperToolsCredentialLocal development — tries CLI tools
ManagedIdentityCredentialAzure VMs, App Service, Functions, AKS
WorkloadIdentityCredentialKubernetes workload identity
ClientSecretCredentialService principal with secret
ClientCertificateCredentialService principal with certificate
AzureCliCredentialDirect Azure CLI auth
AzureDeveloperCliCredentialDirect azd CLI auth
AzurePipelinesCredentialAzure Pipelines service connection
ClientAssertionCredentialCustom assertions (federated identity)

Best Practices

  1. Use cargo add to manage dependencies, never edit Cargo.toml directly. Add and remove Rust SDK dependencies with cargo commands instead of manual manifest edits.
  2. Add azure_core only when importing azure_core types directly. If your code imports azure_core::http::Url, azure_core::http::RequestContent, or azure_core::error::ErrorKind, include azure_core; otherwise a direct dependency is optional.
  3. Use DeveloperToolsCredential for local dev, ManagedIdentityCredential for production — Rust does not provide a single DefaultAzureCredential type
  4. Never hardcode credentials — use environment variables for service principals
  5. Clone credentials — pass credential.clone() when constructing multiple clients; credentials are Arc-wrapped
  6. Reuse clients — clients are thread-safe; create once, share across tasks
  7. Assign RBAC roles — ensure the identity has appropriate roles for the target service (e.g., "Key Vault Secrets User" for secret reads)
  8. Run cargo clippy -- -D warnings when the prompt, eval, or CI expects lint-clean output; Rust trajectory graders can fail on style lints even after compiler errors are fixed
  9. Future-proof #[non_exhaustive] SDK models — when constructing SDK model/options structs, end the initializer with ..Default::default() (add #[allow(clippy::needless_update)]) and use a _ wildcard arm when matching SDK enums, so new service-added fields/variants don't break your build

Reference Links

ResourceLink
API Referencehttps://docs.rs/azure_identity/latest/azure_identity
crates.iohttps://crates.io/crates/azure_identity
Source Codehttps://github.com/Azure/azure-sdk-for-rust/tree/main/sdk/identity/azure_identity

Mehr Skills von microsoft

oss-growth
microsoft
OSS-Wachstums-Hacker-Persona
agent-framework-azure-ai-py
microsoft
Erstellen Sie Azure AI Foundry-Agents mit dem Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Verwenden Sie dies beim Erstellen persistenter Agents mit AzureAIAgentsProvider, bei der Nutzung gehosteter Tools (Code-Interpreter, Dateisuche, Websuche), bei der Integration von MCP-Servern, bei der Verwaltung von Konversationsthreads oder bei der Implementierung von Streaming-Antworten. Umfasst Funktionstools, strukturierte Ausgaben und Multi-Tool-Agents.
development
airunway-aks-setup
microsoft
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.
devops
applicationinsights-web-ts
microsoft
Instrumentieren Sie Browser-/Web-Apps mit dem Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Verwenden Sie es für Real User Monitoring (RUM) – Seitenaufrufe, Klicks, AJAX/Fetch-Abhängigkeiten, Ausnahmen, benutzerdefinierte Ereignisse und browser-seitige GenAI-Agent-Traces, die mit Backend-OpenTelemetry-Traces korreliert werden. Umfasst SDK-Loader-Skript und npm-Setup, Framework-Erweiterungen (React, React Native, Angular), Click Analytics, Telemetrie-Initialisierer und OTel-GenAI-Semantik-Konventionen für Agent-/Tool-/Modell-Spans, die vom Browser ausgegeben werden.
devops
azure-ai-anomalydetector-java
microsoft
Erstellen Sie Anomalieerkennungsanwendungen mit dem Azure AI Anomaly Detector SDK für Java. Verwenden Sie dies bei der Implementierung von univariater/multivariater Anomalieerkennung, Zeitreihenanalyse oder KI-gestützter Überwachung.
development
azure-ai-language-conversations-py
microsoft
Implementieren Sie Conversational Language Understanding (CLU) mit dem azure-ai-language-conversations Python SDK. Verwenden Sie dies, wenn Sie mit ConversationAnalysisClient arbeiten, um Gesprächsabsichten und Entitäten zu analysieren, NLP-Funktionen zu erstellen oder Sprachverständnis in Anwendungen zu integrieren.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 für Python. Verwenden für ML-Workspaces, Jobs, Modelle, Datensätze, Compute und Pipelines. Auslöser: „azure-ai-ml“, „MLClient“, „Workspace“, „Modell-Registry“, „Trainings-Jobs“, „Datensätze“.
development