Azure AI Projects Python SDK (Foundry SDK)

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

Installation

bash

pip install azure-ai-projects azure-identity

Environment Variables

bash

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

Authentication

python

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)

python

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

python

# 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)

python

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

python

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

python

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

python

# 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

python

# 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

python

# 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

python

# List datasets
datasets = client.datasets.list()

# List indexes
indexes = client.indexes.list()

See references/datasets-indexes.md for data operations.

Evaluation

python

# Using OpenAI client for evals
openai_client = client.get_openai_client()

# Create evaluation with built-in evaluators
eval_run = openai_client.evals.runs.create(
    eval_id="my-eval",
    name="quality-check",
    data_source={
        "type": "custom",
        "item_references": [{"item_id": "test-1"}],
    },
    testing_criteria=[
        {"type": "fluency"},
        {"type": "task_adherence"},
    ],
)

See references/evaluation.md for evaluation patterns.

Async Client

python

from azure.ai.projects.aio import AIProjectClient

async with AIProjectClient(
    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
) as client:
    agent = await client.agents.create_agent(...)
    # ... async operations

See references/async-patterns.md for async patterns.

Memory Stores

python

# Create memory store for agent
memory_store = client.agents.create_memory_store(
    name="conversation-memory",
)

# Attach to agent for persistent memory
agent = client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="memory-agent",
    tools=[MemorySearchTool()],
    tool_resources={"memory": {"store_ids": [memory_store.id]}},
)

Best Practices

•Use context managers for async client: async with AIProjectClient(...) as client:
•Clean up agents when done: client.agents.delete_agent(agent.id)
•Use create_and_process for simple runs, streaming for real-time UX
•Use versioned agents for production deployments
•Prefer connections for external service integration (AI Search, Bing, etc.)

SDK Comparison

Feature	`azure-ai-projects`	`azure-ai-agents`
Level	High-level (Foundry)	Low-level (Agents)
Client	`AIProjectClient`	`AgentsClient`
Versioning	`create_version()`	Not available
Connections	Yes	No
Deployments	Yes	No
Datasets/Indexes	Yes	No
Evaluation	Via OpenAI client	No
When to use	Full Foundry integration	Standalone agent apps

Reference Files

•references/agents.md: Agent operations with PromptAgentDefinition
•references/tools.md: All agent tools with examples
•references/evaluation.md: Evaluation operations overview
•references/built-in-evaluators.md: Complete built-in evaluator reference
•references/custom-evaluators.md: Code and prompt-based evaluator patterns
•references/connections.md: Connection operations
•references/deployments.md: Deployment enumeration
•references/datasets-indexes.md: Dataset and index operations
•references/async-patterns.md: Async client usage
•references/api-reference.md: Complete API reference for all 373 SDK exports (v2.0.0b4)
•scripts/run_batch_evaluation.py: CLI tool for batch evaluations

When to Use

This skill is applicable to execute the workflow or actions described in the overview.