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Azure AI Projects Py

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  • Tool integration
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Build AI applications using the Azure AI Projects Python SDK (azure-ai-projects). Use when working with Foundry project clients, creating versioned agents with PromptAgentDefinition, running evaluations, managing connections/deployments/datasets/indexes, or using OpenAI-compatible clients. This is the high-level Foundry SDK - for low-level agent operations, use azure-ai-agents-python skill.
— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The azure-ai-projects-py skill provides an interface for interacting with the Azure AI Projects Python SDK. It enables developers to manage the lifecycle of AI agents, deployments, datasets, and indexes within the Foundry environment. The SDK supports both synchronous and asynchronous operations, integrates with Azure identity management, and provides an OpenAI-compatible client for standard API interactions.

What it does

  • Create and manage versioned AI agents using PromptAgentDefinition
  • List and manage model deployments, datasets, and indexes
  • Execute and manage agent threads, messages, and runs
  • Access an OpenAI-compatible client for standard chat completions
  • Configure and run evaluations on AI models
  • Manage project connections to external services

When to use it

  • When building AI applications that require integration with Microsoft Foundry
  • When you need to manage agent versions and definitions programmatically
  • When performing batch evaluations or quality checks on AI outputs
  • When you need to interface with Azure-managed AI resources and deployments

Advantages

  • Provides a unified client for managing diverse AI project resources
  • Supports both sync and async programming patterns
  • Enables versioning for agent definitions
  • Integrates with standard Azure authentication via DefaultAzureCredential

Usage examples

“How do I initialize the AIProjectClient?”
Use the AIProjectClient class with your project endpoint and a DefaultAzureCredential instance, wrapped in a context manager.
“How can I create a versioned agent?”
Use the client.agents.create_version method, passing a PromptAgentDefinition object with your model, instructions, and version label.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/microsoft/azure-ai-projects-py/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip azure-ai-projects-py.zip -d .agents/skills

Documentation by Microsoft

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

OperationAccessPurpose
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

ToolClassUse Case
Code InterpreterCodeInterpreterToolExecute Python, generate files
File SearchFileSearchToolRAG over uploaded documents
Bing GroundingBingGroundingToolWeb search (requires connection)
Azure AI SearchAzureAISearchToolSearch your indexes
Function CallingFunctionToolCall your Python functions
OpenAPIOpenApiToolCall REST APIs
MCPMcpToolModel Context Protocol servers
Memory SearchMemorySearchToolSearch agent memory stores
SharePointSharepointGroundingToolSearch 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

  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

Featureazure-ai-projectsazure-ai-agents
LevelHigh-level (Foundry)Low-level (Agents)
ClientAIProjectClientAgentsClient
Versioningcreate_version()Not available
ConnectionsYesNo
DeploymentsYesNo
Datasets/IndexesYesNo
EvaluationVia OpenAI clientNo
When to useFull Foundry integrationStandalone agent apps

Reference Files

Files

references/tools.md

Agent Tools Reference

Tool Import Patterns

# From azure.ai.agents.models (low-level tools)
from azure.ai.agents.models import (
    CodeInterpreterTool,
    FileSearchTool,
    FunctionTool,
    BingGroundingTool,
    OpenApiTool,
    OpenApiAnonymousAuthDetails,
    FilePurpose,
    MessageAttachment,
    ToolSet,
    SharepointTool,
    FabricTool,
    ConnectedAgentTool,
    McpTool,
)

# From azure.ai.projects.models (project-level tools)
from azure.ai.projects.models import (
    AzureAISearchAgentTool,
    AzureAISearchToolResource,
    AISearchIndexResource,
    AzureAISearchQueryType,
    BingGroundingAgentTool,
    BingGroundingSearchToolParameters,
    BingGroundingSearchConfiguration,
    PromptAgentDefinition,
)

CodeInterpreterTool

Execute Python code in a sandboxed environment.

Basic Usage
from azure.ai.agents.models import CodeInterpreterTool

code_interpreter = CodeInterpreterTool()

agent = project_client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="code-agent",
    instructions="You can execute Python code. Use Code Interpreter for calculations and visualizations.",
    tools=code_interpreter.definitions,
    tool_resources=code_interpreter.resources,
)
With File Upload
from azure.ai.agents.models import CodeInterpreterTool, FilePurpose

# Upload file for code interpreter
file = project_client.agents.files.upload_and_poll(
    file_path="data.csv",
    purpose=FilePurpose.AGENTS,
)

code_interpreter = CodeInterpreterTool()

agent = project_client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="data-agent",
    instructions="Analyze the uploaded data file.",
    tools=code_interpreter.definitions,
    tool_resources={"code_interpreter": {"file_ids": [file.id]}},
)

FileSearchTool

RAG over uploaded documents using vector stores.

Basic Usage
from azure.ai.agents.models import FileSearchTool, FilePurpose

# Upload and create vector store
file = project_client.agents.files.upload_and_poll(
    file_path="./data/product_info.md",
    purpose=FilePurpose.AGENTS,
)
vector_store = project_client.agents.vector_stores.create_and_poll(
    file_ids=[file.id],
    name="product-docs",
)

# Create file search tool
file_search = FileSearchTool(vector_store_ids=[vector_store.id])

agent = project_client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="search-agent",
    instructions="Search uploaded files to answer questions.",
    tools=file_search.definitions,
    tool_resources=file_search.resources,
)
With Message Attachment
from azure.ai.agents.models import MessageAttachment, FileSearchTool

attachment = MessageAttachment(
    file_id=file.id,
    tools=FileSearchTool().definitions,
)

message = project_client.agents.messages.create(
    thread_id=thread.id,
    role="user",
    content="What features are mentioned in this document?",
    attachments=[attachment],
)

FunctionTool

Define custom Python functions for agents to call.

Basic Usage
from azure.ai.agents.models import FunctionTool

def get_weather(location: str) -> str:
    """Get weather for a location."""
    return f"Weather in {location}: Sunny, 72F"

def get_stock_price(symbol: str) -> str:
    """Get current stock price."""
    return f"{symbol}: $150.00"

functions = FunctionTool(functions=[get_weather, get_stock_price])

agent = project_client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="function-agent",
    instructions="Help with weather and stock queries.",
    tools=functions.definitions,
)
With ToolSet and Auto-Execution
from azure.ai.agents.models import FunctionTool, ToolSet

def get_weather(location: str) -> str:
    """Get weather for a location."""
    return f"Weather in {location}: Sunny, 72F"

functions = FunctionTool(functions=[get_weather])
toolset = ToolSet()
toolset.add(functions)

# Enable auto function calls
project_client.agents.enable_auto_function_calls(toolset)

agent = project_client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="auto-function-agent",
    instructions="Help with weather queries.",
    toolset=toolset,
)

# Process run - functions auto-execute
run = project_client.agents.runs.create_and_process(
    thread_id=thread.id,
    agent_id=agent.id,
    toolset=toolset,
)
Explicit Function Definition
from azure.ai.projects.models import FunctionTool

tool = FunctionTool(
    name="get_horoscope",
    parameters={
        "type": "object",
        "properties": {
            "sign": {
                "type": "string",
                "description": "An astrological sign like Taurus or Aquarius",
            },
        },
        "required": ["sign"],
        "additionalProperties": False,
    },
    description="Get today's horoscope for an astrological sign.",
    strict=True,
)

BingGroundingTool

Real-time web search grounding.

Using Low-Level Tool
from azure.ai.agents.models import BingGroundingTool

conn_id = os.environ["BING_CONNECTION_NAME"]
bing = BingGroundingTool(connection_id=conn_id)

agent = project_client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="bing-agent",
    instructions="Use web search to find current information.",
    tools=bing.definitions,
)
Using Project-Level Tool
from azure.ai.projects.models import (
    PromptAgentDefinition,
    BingGroundingAgentTool,
    BingGroundingSearchToolParameters,
    BingGroundingSearchConfiguration,
)

bing_connection = project_client.connections.get(
    os.environ["BING_PROJECT_CONNECTION_NAME"]
)

agent = project_client.agents.create_version(
    agent_name="bing-search-agent",
    definition=PromptAgentDefinition(
        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
        instructions="You are a helpful assistant with web search capabilities.",
        tools=[
            BingGroundingAgentTool(
                bing_grounding=BingGroundingSearchToolParameters(
                    search_configurations=[
                        BingGroundingSearchConfiguration(
                            project_connection_id=bing_connection.id
                        )
                    ]
                )
            )
        ],
    ),
)

AzureAISearchAgentTool

Enterprise search over your Azure AI Search indexes.

from azure.ai.projects.models import (
    AzureAISearchAgentTool,
    AzureAISearchToolResource,
    AISearchIndexResource,
    AzureAISearchQueryType,
    PromptAgentDefinition,
)

# Get search connection
search_connection = project_client.connections.get(
    os.environ["AI_SEARCH_PROJECT_CONNECTION_NAME"]
)

agent = project_client.agents.create_version(
    agent_name="enterprise-search-agent",
    definition=PromptAgentDefinition(
        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
        instructions="""You are a helpful assistant. Always provide citations 
        using format: [message_idx:search_idx source].""",
        tools=[
            AzureAISearchAgentTool(
                azure_ai_search=AzureAISearchToolResource(
                    indexes=[
                        AISearchIndexResource(
                            project_connection_id=search_connection.id,
                            index_name=os.environ["AI_SEARCH_INDEX_NAME"],
                            query_type=AzureAISearchQueryType.SIMPLE,
                        ),
                    ]
                )
            )
        ],
    ),
)
Query Types
from azure.ai.projects.models import AzureAISearchQueryType

# Available query types:
# - AzureAISearchQueryType.SIMPLE: Simple keyword search
# - AzureAISearchQueryType.SEMANTIC: Semantic ranking
# - AzureAISearchQueryType.VECTOR: Vector search
# - AzureAISearchQueryType.VECTOR_SIMPLE_HYBRID: Vector + keyword hybrid
# - AzureAISearchQueryType.VECTOR_SEMANTIC_HYBRID: Vector + semantic hybrid

OpenApiTool

Call external REST APIs defined by OpenAPI spec.

from azure.ai.agents.models import OpenApiTool, OpenApiAnonymousAuthDetails

openapi_spec = """
openapi: 3.0.0
info:
  title: Weather API
  version: 1.0.0
paths:
  /weather:
    get:
      summary: Get weather
      parameters:
        - name: location
          in: query
          required: true
          schema:
            type: string
      responses:
        '200':
          description: Weather data
"""

openapi_tool = OpenApiTool(
    name="weather_api",
    spec=openapi_spec,
    description="Get weather information",
    auth=OpenApiAnonymousAuthDetails(),
)

agent = project_client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="api-agent",
    instructions="Use the weather API to get weather data.",
    tools=openapi_tool.definitions,
)

McpTool

Model Context Protocol server integration.

from azure.ai.agents.models import McpTool

mcp_tool = McpTool(
    server_label="my-mcp-server",
    server_url="http://localhost:3000",
    allowed_tools=["search", "calculate"],
)

agent = project_client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="mcp-agent",
    instructions="Use MCP tools for specialized operations.",
    tools=mcp_tool.definitions,
)

SharepointTool

Search SharePoint content.

from azure.ai.agents.models import SharepointTool

sharepoint = SharepointTool(connection_id=os.environ["SHAREPOINT_CONNECTION_ID"])

agent = project_client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="sharepoint-agent",
    instructions="Search SharePoint for documents.",
    tools=sharepoint.definitions,
)

ConnectedAgentTool

Multi-agent orchestration.

from azure.ai.agents.models import ConnectedAgentTool

# Connect to another agent
connected_agent = ConnectedAgentTool(
    agent_id=other_agent.id,
    name="specialist-agent",
    description="A specialist agent for complex queries",
)

orchestrator = project_client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="orchestrator",
    instructions="Delegate complex tasks to the specialist agent.",
    tools=connected_agent.definitions,
)

ToolSet Pattern

Combine multiple tools:

from azure.ai.agents.models import ToolSet, FunctionTool, CodeInterpreterTool

def my_function(x: int) -> int:
    """Double a number."""
    return x * 2

toolset = ToolSet()
toolset.add(FunctionTool(functions=[my_function]))
toolset.add(CodeInterpreterTool())

# Enable auto function calls
project_client.agents.enable_auto_function_calls(toolset)

agent = project_client.agents.create_agent(
    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
    name="multi-tool-agent",
    instructions="You have multiple tools available.",
    toolset=toolset,
)

# Pass toolset to run for auto-execution
run = project_client.agents.runs.create_and_process(
    thread_id=thread.id,
    agent_id=agent.id,
    toolset=toolset,
)

Tools Quick Reference

ToolClassConnection RequiredUse Case
Code InterpreterCodeInterpreterToolNoExecute Python, generate files
File SearchFileSearchToolNoRAG over uploaded documents
FunctionFunctionToolNoCall custom Python functions
Bing GroundingBingGroundingToolYesWeb search
Azure AI SearchAzureAISearchAgentToolYesEnterprise search
OpenAPIOpenApiToolNoCall REST APIs
MCPMcpToolNoMCP server integration
SharePointSharepointToolYesSharePoint search
FabricFabricToolYesMicrosoft Fabric integration
Connected AgentConnectedAgentToolNoMulti-agent orchestration

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key, Needs Python

Security

What our scanner noticed· Low risk

  • Instructions that use the network
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/skills · .github/plugins/azure-sdk-python/skills/azure-ai-projects-py · commit 0696600
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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