> ## Documentation Index
> Fetch the complete documentation index at: https://docs.swarms.world/llms.txt
> Use this file to discover all available pages before exploring further.

# Building Agents with Azure OpenAI

> Build Swarms agents on Azure OpenAI — enterprise-grade GPT models through Microsoft Azure.

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.

## Installation

```bash theme={null}
pip install -U swarms
```

## Environment Setup

Azure OpenAI requires three environment variables:

```bash theme={null}
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](https://portal.azure.com/).

<Tip>
  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.
</Tip>

## Quick Start

Azure deployments use the `azure/` prefix followed by your deployment name:

```python theme={null}
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."))
```

## Common Deployment Patterns

| Underlying model | Typical 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.

## Tool Use

Tools work the same as on OpenAI directly:

```python theme={null}
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?"))
```

## Streaming

```python theme={null}
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.")
```

## 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:

```python theme={null}
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,
)
```

## 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:

```bash theme={null}
export AZURE_API_BASE="https://private-endpoint.privatelink.openai.azure.com/"
```

No code changes needed.

## Multi-Agent Pipeline

The same composition patterns work on Azure:

```python theme={null}
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."))
```

## Production Defaults

```python theme={null}
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,
)
```

## 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.

## Next Steps

* [Building Agents with OpenAI](/examples/model-providers/openai) — direct OpenAI alternative
* [Model Providers Overview](/integrations/model-providers)
* [Production Best Practices](/deployment/production-best-practices)