Azure OpenAI

Integrate Azure OpenAI with Agency Swarm to ensure secure data processing and enhanced privacy.

Many organizations prioritize data privacy and are cautious about sharing their data with any third-parties. By leveraging Azure OpenAI, you can ensure that your data is processed only within your own secure Azure environment, and not even shared with OpenAI itself.

Running OpenAI models on Azure is the same as deploying your own open source model on any other cloud provider.

Prerequisites

Before you begin, ensure you have the following:

1. Create an Azure Account with an active subscription. Create an account here.
2. Get approved access to the OpenAI Service on Azure.
3. Create an Azure OpenAI resource in one of the available regions and deploy a model to it.
4. Obtain the endpoint URL and API key for the OpenAI resource.

Setting Up Azure OpenAI with Agency Swarm

1. Configure the Azure OpenAI Client

   To use Azure OpenAI, you need to configure the client to connect to your Azure OpenAI resource as follows:

   import os
   from openai import AsyncAzureOpenAI
   
   # Create a custom AsyncOpenAI client for Azure
   azure_agent = AsyncAzureOpenAI(
       api_key=os.getenv("AZURE_OPENAI_KEY"),
       azure_endpoint=os.getenv("AZURE_ENDPOINT"),
       api_version=os.getenv("AZURE_API_VERSION"),
   )
   
2. Update Agent Model Parameters

   Set the model parameter inside each agent to use your custom client and model:

   from agency_swarm import Agent, OpenAIChatCompletionsModel
   
   # Define your agent and pass the custom client into the model
   azure_agent = Agent(
       name="AzureAgent",
       instructions="You are a helpful assistant",
       model=OpenAIChatCompletionsModel(
           model="gpt-6-luna",
           openai_client=azure_agent,
       ),
   )
   

   Model deployment name might be different from the standard OpenAI model names. It is set by you when you deploy a model to Azure.

   A client stored on a model outlives a single event loop: httpx2 binds pooled connections to the loop that created them, so a fixed client can crash on the next get_response_sync. Pass http_client=loop_scoped_http_client() (from agency_swarm) when building azure_agent so its requests run on the caller’s loop. See Third-party models.

3. Run Your Agency

   After configuring the client and updating the agents, you can run your agency as usual:

   import asyncio
   from agency_swarm import Agency
   
   agency=Agency(azure_agent)
   
   agency.tui()
   

   The first terminal run downloads the matching terminal app automatically.

For other model providers (Anthropic, Google, AWS Bedrock, etc.), see Third-Party Models.