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Microsoft Foundry quickstart

In this quickstart you'll get started using models and agents in Foundry.

You will:

  • Generate a response from a model
  • Create an agent with a defined prompt
  • Have a multi-turn conversation with the agent

Prerequisites

Set environment variables and get the code

Store your project endpoint as an environment variable. Also set these values for use in your scripts.

- Python and JavaScript

PROJECT_ENDPOINT=<endpoint copied from welcome screen>
AGENT_NAME="MyAgent"

- C# and Java

ProjectEndpoint = <endpoint copied from welcome screen>
AgentName = "MyAgent"

Follow along below or get the code:

Get the code

Sign in using the CLI az login command to authenticate before running your Python scripts.

Install and authenticate

Make sure you install the correct version of the packages as shown here.

  1. Install the current version of azure-ai-projects. This version uses the Foundry projects (new) API .

    pip install azure-ai-projects>=2.0.0

  2. Sign in using the CLI az login command to authenticate before running your Python scripts.

Tip

Code uses Azure AI Projects 2.x and is incompatible with Azure AI Projects 1.x. See the Foundry (classic) documentation for the Azure AI Projects 1.x version.

Chat with a model

Interacting with a model is the basic building block of AI applications. Send an input and receive a response from the model:

from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient

# Format: "https://resource_name.ai.azure.com/api/projects/project_name"
PROJECT_ENDPOINT = "your_project_endpoint"

# Create project and openai clients to call Foundry API
project = AIProjectClient(
    endpoint=PROJECT_ENDPOINT,
    credential=DefaultAzureCredential(),
)
openai = project.get_openai_client()

# Run a responses API call
response = openai.responses.create(
    model="gpt-5-mini",  # supports all Foundry direct models
    input="What is the size of France in square miles?",
)
print(f"Response output: {response.output_text}")

After running the code, you see a model-generated response in the console (for example, a short poem or answer to your prompt). This confirms your project endpoint, authentication, and model deployment are working correctly.

Tip

Code uses Azure AI Projects 2.x and is incompatible with Azure AI Projects 1.x. See the Foundry (classic) documentation for the Azure AI Projects 1.x version.

Create an agent

Create an agent using your deployed model.

An agent defines core behavior. Once created, it ensures consistent responses in user interactions without repeating instructions each time. You can update or delete agents anytime.

from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import PromptAgentDefinition

# Format: "https://resource_name.ai.azure.com/api/projects/project_name"
PROJECT_ENDPOINT = "your_project_endpoint"
AGENT_NAME = "your_agent_name"

# Create project client to call Foundry API
project = AIProjectClient(
    endpoint=PROJECT_ENDPOINT,
    credential=DefaultAzureCredential(),
)

# Create an agent with a model and instructions
agent = project.agents.create_version(
    agent_name=AGENT_NAME,
    definition=PromptAgentDefinition(
        model="gpt-5-mini",  # supports all Foundry direct models"
        instructions="You are a helpful assistant that answers general questions",
    ),
)
print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")

The output confirms the agent was created. For SDK tabs, you see the agent name and ID printed to the console.

Tip

Code uses Azure AI Projects 2.x and is incompatible with Azure AI Projects 1.x. See the Foundry (classic) documentation for the Azure AI Projects 1.x version.

Chat with an agent

Use the previously created agent named "MyAgent" to interact by asking a question and a related follow-up. The conversation maintains history across these interactions.

from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient

# Format: "https://resource_name.ai.azure.com/api/projects/project_name"
PROJECT_ENDPOINT = "your_project_endpoint"
AGENT_NAME = "your_agent_name"

# Create project and openai clients to call Foundry API
project = AIProjectClient(
    endpoint=FOUNDRY_PROJECT_ENDPOINT,
    credential=DefaultAzureCredential(),
)
openai = project.get_openai_client()

# Create a conversation for multi-turn chat
conversation = openai.conversations.create()

# Chat with the agent to answer questions
response = openai.responses.create(
    conversation=conversation.id,
    extra_body={"agent_reference": {"name": FOUNDRY_AGENT_NAME, "type": "agent_reference"}},
    input="What is the size of France in square miles?",
)
print(response.output_text)

# Ask a follow-up question in the same conversation
response = openai.responses.create(
    conversation=conversation.id,
    extra_body={"agent_reference": {"name": FOUNDRY_AGENT_NAME, "type": "agent_reference"}},
    input="And what is the capital city?",
)
print(response.output_text)

You see the agent's responses to both prompts. The follow-up response demonstrates that the agent maintains conversation history across turns.

Tip

Code uses Azure AI Projects 2.x and is incompatible with Azure AI Projects 1.x. See the Foundry (classic) documentation for the Azure AI Projects 1.x version.

Clean up resources

If you no longer need any of the resources you created, delete the resource group associated with your project.

  • In the Azure portal, select the resource group, and then select Delete. Confirm that you want to delete the resource group.

Next step

Idea to prototype - Build and evaluate an enterprise agent

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