Azure ai projects py

Azure AI Projects Python SDK (Foundry SDK)

Build AI applications on Microsoft Foundry using the azure-ai-projects SDK.

Installation

pip install azure-ai-projects azure-identity

Environment Variables

AZURE_AI_PROJECT_ENDPOINT="https://<resource>.services.ai.azure.com/api/projects/<project>"
AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"

Authentication

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

credential = DefaultAzureCredential()
client = AIProjectClient(
    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    credential=credential,
)

Client Operations Overview

Operation	Access	Purpose
client.agents	.agents.*	Agent CRUD, versions, threads, runs
client.connections	.connections.*	List/get project connections
client.deployments	.deployments.*	List model deployments
client.datasets	.datasets.*	Dataset management
client.indexes	.indexes.*	Index management
client.evaluations	.evaluations.*	Run evaluations
client.red_teams	.red_teams.*	Red team operations

Two Client Approaches

1. AIProjectClient (Native Foundry)

from azure.ai.projects import AIProjectClient

client = AIProjectClient(
    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
)

# Use Foundry-native operations
agent = client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are helpful.",
)

2. OpenAI-Compatible Client

# Get OpenAI-compatible client from project
openai_client = client.get_openai_client()

# Use standard OpenAI API
response = openai_client.chat.completions.create(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    messages=[{"role": "user", "content": "Hello!"}],
)

Agent Operations

Create Agent (Basic)

agent = client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are a helpful assistant.",
)

Create Agent with Tools

from azure.ai.agents import CodeInterpreterTool, FileSearchTool

agent = client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="tool-agent",
    instructions="You can execute code and search files.",
    tools=[CodeInterpreterTool(), FileSearchTool()],
)

Versioned Agents with PromptAgentDefinition

from azure.ai.projects.models import PromptAgentDefinition

# Create a versioned agent
agent_version = client.agents.create_version(
    agent_name="customer-support-agent",
    definition=PromptAgentDefinition(
        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
        instructions="You are a customer support specialist.",
        tools=[],  # Add tools as needed
    ),
    version_label="v1.0",
)

See references/agents.md for detailed agent patterns.

Tools Overview

Tool	Class	Use Case
Code Interpreter	CodeInterpreterTool	Execute Python, generate files
File Search	FileSearchTool	RAG over uploaded documents
Bing Grounding	BingGroundingTool	Web search (requires connection)
Azure AI Search	AzureAISearchTool	Search your indexes
Function Calling	FunctionTool	Call your Python functions
OpenAPI	OpenApiTool	Call REST APIs
MCP	McpTool	Model Context Protocol servers
Memory Search	MemorySearchTool	Search agent memory stores
SharePoint	SharepointGroundingTool	Search SharePoint content

See references/tools.md for all tool patterns.

Thread and Message Flow

# 1. Create thread
thread = client.agents.threads.create()

# 2. Add message
client.agents.messages.create(
    thread_id=thread.id,
    role="user",
    content="What's the weather like?",
)

# 3. Create and process run
run = client.agents.runs.create_and_process(
    thread_id=thread.id,
    agent_id=agent.id,
)

# 4. Get response
if run.status == "completed":
    messages = client.agents.messages.list(thread_id=thread.id)
    for msg in messages:
        if msg.role == "assistant":
            print(msg.content[0].text.value)

Connections

# List all connections
connections = client.connections.list()
for conn in connections:
    print(f"{conn.name}: {conn.connection_type}")

# Get specific connection
connection = client.connections.get(connection_name="my-search-connection")

See references/connections.md for connection patterns.

Deployments

# List available model deployments
deployments = client.deployments.list()
for deployment in deployments:
    print(f"{deployment.name}: {deployment.model}")

See references/deployments.md for deployment patterns.

Datasets and Indexes

# List datasets
datasets = clie