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Azure OpenAI provides OpenAI’s GPT models through Microsoft’s cloud infrastructure, with enhanced security, compliance certifications (SOC 2, HIPAA, FedRAMP), private networking, and enterprise SLAs. This is the right pick for regulated industries and large-org deployments.

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Installation

pip install -U swarms

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Environment Setup

Azure OpenAI requires three environment variables: 
export AZURE_API_KEY="your-azure-openai-key"
export AZURE_API_BASE="https://your-resource-name.openai.azure.com/"
export AZURE_API_VERSION="2024-08-01-preview"
These come from your Azure OpenAI resource in the Azure Portal.
The model name you pass to Swarms must match your deployment name in Azure, not the underlying model name. Deployments are created in the Azure AI Foundry portal.

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Quick Start

Azure deployments use the azure/ prefix followed by your deployment name: 
from swarms import Agent

agent = Agent(
    agent_name="Azure-Agent",
    model_name="azure/gpt-4.1",         # your Azure deployment name
    max_loops=1,
)

print(agent.run("Summarize the case for private cloud AI in three paragraphs."))

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Common Deployment Patterns

Underlying modelTypical Azure model_name
GPT-5.4"azure/gpt-5.4"
GPT-4.1"azure/gpt-4.1"
GPT-4o"azure/gpt-4o"
GPT-4o Mini"azure/gpt-4o-mini"
o3"azure/o3"
The exact name depends on what you named your deployment in Azure.

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Tool Use

Tools work the same as on OpenAI directly: 
from swarms import Agent

def get_weather(city: str) -> str:
    """Return the current weather for a city."""
    return f"{city}: 21°C, partly cloudy"

agent = Agent(
    agent_name="Azure-Assistant",
    model_name="azure/gpt-4.1",
    tools=[get_weather],
    max_loops=3,
)

print(agent.run("What's the weather in Tokyo right now?"))

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Streaming

from swarms import Agent

agent = Agent(
    agent_name="Streaming-Azure",
    model_name="azure/gpt-4.1",
    streaming_on=True,
    max_loops=1,
)

agent.run("Walk me through how Azure Active Directory federates with Okta.")

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Multiple Deployments

Many organizations create separate deployments for different workloads (e.g., one with a 50K TPM quota for production, one with 5K TPM for dev). Swarms can target any of them by deployment name: 
from swarms import Agent

prod_agent = Agent(
    agent_name="Prod",
    model_name="azure/gpt-4-prod-50k",
    max_loops=1,
)

dev_agent = Agent(
    agent_name="Dev",
    model_name="azure/gpt-4-dev-5k",
    max_loops=1,
)

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Private Networking

If your Azure OpenAI resource is behind a private endpoint, set AZURE_API_BASE to the internal URL and ensure your runtime has network access: 
export AZURE_API_BASE="https://private-endpoint.privatelink.openai.azure.com/"
No code changes needed.

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Multi-Agent Pipeline

The same composition patterns work on Azure: 
from swarms import Agent, SequentialWorkflow

triage = Agent(
    agent_name="Triage",
    model_name="azure/gpt-4o-mini",         # cheap deployment
    system_prompt="Classify and route the request.",
    max_loops=1,
)

analyst = Agent(
    agent_name="Analyst",
    model_name="azure/gpt-4.1",             # workhorse deployment
    system_prompt="Produce a detailed analysis.",
    max_loops=1,
)

pipeline = SequentialWorkflow(agents=[triage, analyst], max_loops=1)
print(pipeline.run("Evaluate whether we should migrate our auth provider from Okta to Entra ID."))

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Production Defaults

from swarms import Agent

agent = Agent(
    agent_name="Production-Azure",
    model_name="azure/gpt-4.1",
    max_loops=1,
    persistent_memory=True,
    context_compression=True,
    context_length=128_000,
    autosave=True,
    retry_attempts=3,
    print_on=False,
)

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Troubleshooting

InvalidRequestError: Resource not found — your model_name doesn’t match a deployment in your Azure resource. Check the Deployments tab in Azure AI Foundry. AuthenticationError — your AZURE_API_KEY, AZURE_API_BASE, or AZURE_API_VERSION is missing or wrong. All three are required. Rate-limit errors — Azure deployments have hard TPM/RPM quotas set in the portal. Request a quota increase or split traffic across multiple deployments.

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Next Steps