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

Enprojectnment 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.enprojectn["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.enprojectn["AZURE_AI_PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
)

# Use Foundry-native operations
agent = client.agents.create_agent(
    model=os.enprojectn["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.enprojectn["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    messages=[{"role": "user", "content": "Hello!"}],
)

Agent Operations

Create Agent (Basic)

agent = client.agents.create_agent(
    model=os.enprojectn["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.enprojectn["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.enprojectn["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 = client.datasets.list()

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

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

Evaluation

# 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

from azure.ai.projects.aio import AIProjectClient

async with AIProjectClient(
    endpoint=os.enprojectn["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

# 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.enprojectn["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="memory-agent",
    tools=[MemorySearchTool()],
    tool_resources={"memory": {"store_ids": [memory_store.id]}},
)

Best Practices

1. Use context managers for async client: async with AIProjectClient(...) as client:
2. Clean up agents when done: client.agents.delete_agent(agent.id)
3. Use create_and_process for simple runs, streaming for real-time UX
4. Use versioned agents for production deployments
5. 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.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for enprojectnment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.