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>"  # Required for all auth methods
AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production

Authentication & Lifecycle

> 🔑 Two rules apply to every code sample below: > > 1. Prefer DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation. > - Local dev: DefaultAzureCredential works as-is. > - Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials. > 2. Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically: > - Sync: with <Client>(...) as client: > - Async: async with <Client>(...) as client: and async with DefaultAzureCredential() as credential: (from azure.identity.aio) > > Snippets may abbreviate this setup, but production code should always follow both rules.

python
import os
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.ai.projects import AIProjectClient

# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential()
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()
with AIProjectClient(
    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    credential=credential,
) as client:
    deployments = list(client.deployments.list())

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

with AIProjectClient(
    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
) as client:
    # 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.models 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

1. Pick sync OR async and stay consistent. Do not mix azure.ai.projects sync clients with azure.ai.projects.aio async clients in the same call path. Choose one mode per module.
2. Always use context managers for clients and async credentials. Wrap every client in with AIProjectClient(...) as client: (sync) or async with AIProjectClient(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.
3. Clean up agents when done: client.agents.delete_agent(agent.id)
4. Use create_and_process for simple runs, streaming for real-time UX
5. Use versioned agents for production deployments
6. 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