Azure AI Projects Py Agent Skill

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>"  # 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.

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)

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

# 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.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

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 = 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.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

# 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

• 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.
• 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.
• 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