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Azure AI Projects client library for Python - version 2.0.0b4

The AI Projects client library (in preview) is part of the Microsoft Foundry SDK, and provides easy access to resources in your Microsoft Foundry Project. Use it to:

  • Create and run Agents using methods on the .agents client property.
  • Enhance Agents with specialized tools:
    • Agent-to-Agent (A2A) (Preview)
    • Azure AI Search
    • Azure Functions
    • Bing Custom Search (Preview)
    • Bing Grounding
    • Browser Automation (Preview)
    • Code Interpreter
    • Computer Use (Preview)
    • File Search
    • Function Tool
    • Image Generation
    • Memory Search (Preview)
    • Microsoft Fabric (Preview)
    • Microsoft SharePoint (Preview)
    • Model Context Protocol (MCP)
    • OpenAPI
    • Web Search
    • Web Search (Preview)
  • Get an OpenAI client using .get_openai_client() method to run Responses, Conversations, Evaluations and Fine-Tuning operations with your Agent.
  • Manage memory stores (preview) for Agent conversations, using the .beta.memory_stores operations.
  • Explore additional evaluation tools (some in preview) to assess the performance of your generative AI application, using the .evaluation_rules, .beta.evaluation_taxonomies, .beta.evaluators, .beta.insights, and .beta.schedules operations.
  • Run Red Team scans (preview) to identify risks associated with your generative AI application, using the .beta.red_teams operations.
  • Fine tune AI Models on your data.
  • Enumerate AI Models deployed to your Foundry Project using the .deployments operations.
  • Enumerate connected Azure resources in your Foundry project using the .connections operations.
  • Upload documents and create Datasets to reference them using the .datasets operations.
  • Create and enumerate Search Indexes using methods the .indexes operations.

The client library uses version v1 of the AI Foundry data plane REST APIs.

Product documentation | Samples | API reference | Package (PyPI) | SDK source code | Release history

Reporting issues

To report an issue with the client library, or request additional features, please open a GitHub issue here. Mention the package name "azure-ai-projects" in the title or content.

Getting started

Prerequisite

  • Python 3.9 or later.
  • An Azure subscription.
  • A project in Microsoft Foundry.
  • A Foundry project endpoint URL of the form https://your-ai-services-account-name.services.ai.azure.com/api/projects/your-project-name. It can be found in your Microsoft Foundry Project overview page. Below we will assume the environment variable AZURE_AI_PROJECT_ENDPOINT was defined to hold this value.
  • An Entra ID token for authentication. Your application needs an object that implements the TokenCredential interface. Code samples here use DefaultAzureCredential. To get that working, you will need:

Install the package

pip install --pre azure-ai-projects

Key concepts

Create and authenticate the client with Entra ID

Entra ID is the only authentication method supported at the moment by the client.

To construct a synchronous client as a context manager:

import os
from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential

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

To construct an asynchronous client, install the additional package aiohttp:

pip install aiohttp

and run:

import os
import asyncio
from azure.ai.projects.aio import AIProjectClient
from azure.identity.aio import DefaultAzureCredential

async with (
    DefaultAzureCredential() as credential,
    AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as project_client,
):

Examples

Performing Responses operations using OpenAI client

Your Microsoft Foundry project may have one or more AI models deployed. These could be OpenAI models, Microsoft models, or models from other providers. Use the code below to get an authenticated OpenAI client from the openai package, and execute an example multi-turn "Responses" calls.

The code below assumes the environment variable AZURE_AI_MODEL_DEPLOYMENT_NAME is defined. It's the deployment name of an AI model in your Foundry Project. See "Build" menu, under "Models" (First column of the "Deployments" table).

with project_client.get_openai_client() as openai_client:
    response = openai_client.responses.create(
        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
        input="What is the size of France in square miles?",
    )
    print(f"Response output: {response.output_text}")

    response = openai_client.responses.create(
        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
        input="And what is the capital city?",
        previous_response_id=response.id,
    )
    print(f"Response output: {response.output_text}")

See the "responses" folder in the package samples for additional samples, including streaming responses.

Performing Agent operations

The .agents property on the AIProjectClient gives you access to all Agent operations. Agents use an extension of the OpenAI Responses protocol, so you will need to get an OpenAI client to do Agent operations, as shown in the example below.

The code below assumes environment variable AZURE_AI_MODEL_DEPLOYMENT_NAME is defined. It's the deployment name of an AI model in your Foundry Project. See "Build" menu, under "Models" (First column of the "Deployments" table).

See the "agents" folder in the package samples for an extensive set of samples, including streaming, tool usage and memory store usage.

with project_client.get_openai_client() as openai_client:
    agent = project_client.agents.create_version(
        agent_name="MyAgent",
        definition=PromptAgentDefinition(
            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
            instructions="You are a helpful assistant that answers general questions",
        ),
    )
    print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")

    conversation = openai_client.conversations.create(
        items=[{"type": "message", "role": "user", "content": "What is the size of France in square miles?"}],
    )
    print(f"Created conversation with initial user message (id: {conversation.id})")

    response = openai_client.responses.create(
        conversation=conversation.id,
        extra_body={"agent_reference": {"name": agent.name, "type": "agent_reference"}},
    )
    print(f"Response output: {response.output_text}")

    openai_client.conversations.items.create(
        conversation_id=conversation.id,
        items=[{"type": "message", "role": "user", "content": "And what is the capital city?"}],
    )
    print(f"Added a second user message to the conversation")

    response = openai_client.responses.create(
        conversation=conversation.id,
        extra_body={"agent_reference": {"name": agent.name, "type": "agent_reference"}},
    )
    print(f"Response output: {response.output_text}")

    openai_client.conversations.delete(conversation_id=conversation.id)
    print("Conversation deleted")

project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
print("Agent deleted")

Using Agent tools

Agents can be enhanced with specialized tools for various capabilities. For complete working examples of all tools, see the \agents\tools folder under the Samples folder.

In the description below, tools are organized by their Foundry connection requirements: "Built-in Tools" (which do not require a Foundry connection) and "Connection-based Tools" (which require a Foundry connection).

Built-in Tools

These tools work immediately without requiring external connections.

Code Interpreter

Write and run Python code in a sandboxed environment, process files and work with diverse data formats. OpenAI Documentation

Basic tool declaration (no input files):

tool = CodeInterpreterTool()

See the basic sample in file \agents\tools\sample_agent_code_interpreter.py in the Samples folder.

After calling responses.create(), you can extract the code behind the scene from the code_interpreter_call output item:

code = next((output.code for output in response.output if output.type == "code_interpreter_call"), "")
print(f"Code Interpreter code:")
print(code)

If you want to upload an input file and download generated output files:

# Load the CSV file to be processed
asset_file_path = os.path.abspath(
    os.path.join(os.path.dirname(__file__), "../assets/synthetic_500_quarterly_results.csv")
)

# Upload the CSV file for the code interpreter
file = openai_client.files.create(purpose="assistants", file=open(asset_file_path, "rb"))
tool = CodeInterpreterTool(container=CodeInterpreterContainerAuto(file_ids=[file.id]))

After calling responses.create(), check for generated files in response annotations (type container_file_citation) and download them using openai_client.containers.files.content.retrieve().

See full sample file \agents\tools\sample_agent_code_interpreter_with_files.py in the Samples folder.

File Search

Built-in RAG (Retrieval-Augmented Generation) tool to process and search through documents using vector stores for knowledge retrieval. OpenAI Documentation

# Create vector store for file search
vector_store = openai_client.vector_stores.create(name="ProductInfoStore")
print(f"Vector store created (id: {vector_store.id})")

# Load the file to be indexed for search
asset_file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../assets/product_info.md"))

# Upload file to vector store
file = openai_client.vector_stores.files.upload_and_poll(
    vector_store_id=vector_store.id, file=open(asset_file_path, "rb")
)
print(f"File uploaded to vector store (id: {file.id})")

tool = FileSearchTool(vector_store_ids=[vector_store.id])

See the full sample in file \agents\tools\sample_agent_file_search.py in the Samples folder.

Image Generation

Generate images based on text prompts with customizable resolution, quality, and style settings:

tool = ImageGenTool(
    model=image_generation_model,  # Model such as "gpt-image-1"
    quality="low",
    size="1024x1024",
)

After calling responses.create(), you can download file using the returned response:

image_data = [output.result for output in response.output if output.type == "image_generation_call"]
if image_data and image_data[0]:
    print("Downloading generated image...")
    filename = "microsoft.png"
    file_path = os.path.join(tempfile.gettempdir(), filename)

    with open(file_path, "wb") as f:
        f.write(base64.b64decode(image_data[0]))

See the full sample in file \agents\tools\sample_agent_image_generation.py in the Samples folder.

Web Search / Web Search (Preview)

Discover up-to-date web content with the GA Web Search tool or try the Web Search Preview tool for the latest enhancements. Guidance on when to use each option is in the documentation: https://learn.microsoft.com/azure/ai-foundry/agents/how-to/tools/web-overview?view=foundry#determine-the-best-tool-for-your-use-cases.

Warning: Web Search tool uses Grounding with Bing, which has additional costs and terms: terms of use and privacy statement. Customer data will flow outside the Azure compliance boundary. Learn more here.

tool = WebSearchTool(user_location=WebSearchApproximateLocation(country="GB", city="London", region="London"))

See the full sample in file \agents\tools\sample_agent_web_search.py in the Samples folder.

tool = WebSearchPreviewTool(user_location=ApproximateLocation(country="GB", city="London", region="London"))

See the full sample in file \agents\tools\sample_agent_web_search_preview.py in the Samples folder.

Use the GA Web Search tool with a Bing Custom Search connection to scope results to your custom search instance:

tool = WebSearchTool(
    custom_search_configuration=WebSearchConfiguration(
        project_connection_id=os.environ["BING_CUSTOM_SEARCH_PROJECT_CONNECTION_ID"],
        instance_name=os.environ["BING_CUSTOM_SEARCH_INSTANCE_NAME"],
    )
)

See the full sample in file \agents\tools\sample_agent_web_search_with_custom_search.py in the Samples folder.

Computer Use (Preview)

Enable agents to interact directly with computer systems for task automation and system operations:

tool = ComputerUsePreviewTool(display_width=1026, display_height=769, environment="windows")

After calling responses.create(), process the response in an interaction loop. Handle computer_call output items and provide screenshots as computer_call_output with computer_screenshot type to continue the interaction.

See the full sample in file \agents\tools\sample_agent_computer_use.py in the Samples folder.

Model Context Protocol (MCP)

Integrate MCP servers to extend agent capabilities with standardized tools and resources. OpenAI Documentation

mcp_tool = MCPTool(
    server_label="api-specs",
    server_url="https://gitmcp.io/Azure/azure-rest-api-specs",
    require_approval="always",
)

After calling responses.create(), check for mcp_approval_request items in the response output. Send back McpApprovalResponse with your approval decision to allow the agent to continue its work.

See the full sample in file \agents\tools\sample_agent_mcp.py in the Samples folder.

OpenAPI

Call external APIs defined by OpenAPI specifications without additional client-side code. OpenAI Documentation

with open(weather_asset_file_path, "r") as f:
    openapi_weather = cast(dict[str, Any], jsonref.loads(f.read()))

tool = OpenApiTool(
    openapi=OpenApiFunctionDefinition(
        name="get_weather",
        spec=openapi_weather,
        description="Retrieve weather information for a location.",
        auth=OpenApiAnonymousAuthDetails(),
    )
)

See the full sample in file \agents\tools\sample_agent_openapi.py in the Samples folder.

Function Tool

Define custom functions that allow agents to interact with external APIs, databases, or application logic. OpenAI Documentation

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,
)

After calling responses.create(), process function_call items from response output, execute your function logic with the provided arguments, and send back FunctionCallOutput with the results.

See the full sample in file \agents\tools\sample_agent_function_tool.py in the Samples folder.

Azure Functions

Integrate Azure Functions with agents to extend capabilities via serverless compute. Functions are invoked through Azure Storage Queue triggers, allowing asynchronous execution of custom logic.

tool = AzureFunctionTool(
    azure_function=AzureFunctionDefinition(
        input_binding=AzureFunctionBinding(
            storage_queue=AzureFunctionStorageQueue(
                queue_name=os.environ["STORAGE_INPUT_QUEUE_NAME"],
                queue_service_endpoint=os.environ["STORAGE_QUEUE_SERVICE_ENDPOINT"],
            )
        ),
        output_binding=AzureFunctionBinding(
            storage_queue=AzureFunctionStorageQueue(
                queue_name=os.environ["STORAGE_OUTPUT_QUEUE_NAME"],
                queue_service_endpoint=os.environ["STORAGE_QUEUE_SERVICE_ENDPOINT"],
            )
        ),
        function=AzureFunctionDefinitionFunction(
            name="queue_trigger",
            description="Get weather for a given location",
            parameters={
                "type": "object",
                "properties": {"location": {"type": "string", "description": "location to determine weather for"}},
            },
        ),
    )
)

After calling responses.create(), the agent enqueues function arguments to the input queue. Your Azure Function processes the request and returns results via the output queue.

See the full sample in file \agents\tools\sample_agent_azure_function.py and the Azure Function implementation in \agents\tools\get_weather_func_app.py in the Samples folder.

Memory Search Tool (Preview)

The Memory Store Tool adds Memory to an Agent, allowing the Agent's AI model to search for past information related to the current user prompt.

# Set scope to associate the memories with
# You can also use "{{$userId}}" to take the oid of the request authentication header
scope = "user_123"

tool = MemorySearchPreviewTool(
    memory_store_name=memory_store.name,
    scope=scope,
    update_delay=1,  # Wait 1 second of inactivity before updating memories
    # In a real application, set this to a higher value like 300 (5 minutes, default)
)

See the full sample in file \agents\tools\sample_agent_memory_search.py in the Samples folder showing how to create an Agent with a memory store, and use it in multiple conversations.

See also other samples in the folder \memories under Samples folder, showing how to manage memory stores.

Connection-Based Tools

These tools require configuring connections in your AI Foundry project and use project_connection_id.

Azure AI Search

Integrate with Azure AI Search indexes for powerful knowledge retrieval and semantic search capabilities:

tool = AzureAISearchTool(
    azure_ai_search=AzureAISearchToolResource(
        indexes=[
            AISearchIndexResource(
                project_connection_id=os.environ["AI_SEARCH_PROJECT_CONNECTION_ID"],
                index_name=os.environ["AI_SEARCH_INDEX_NAME"],
                query_type=AzureAISearchQueryType.SIMPLE,
            ),
        ]
    )
)

See the full sample in file \agents\tools\sample_agent_ai_search.py in the Samples folder.

Bing Grounding

Warning: Grounding with Bing Search tool uses Grounding with Bing, which has additional costs and terms: terms of use and privacy statement. Customer data will flow outside the Azure compliance boundary. Learn more here.

Ground agent responses with real-time web search results from Bing to provide up-to-date information:

tool = BingGroundingTool(
    bing_grounding=BingGroundingSearchToolParameters(
        search_configurations=[
            BingGroundingSearchConfiguration(project_connection_id=os.environ["BING_PROJECT_CONNECTION_ID"])
        ]
    )
)

See the full sample in file \agents\tools\sample_agent_bing_grounding.py in the Samples folder.

Bing Custom Search (Preview)

Warning: Grounding with Bing Custom Search tool uses Grounding with Bing, which has additional costs and terms: terms of use and privacy statement. Customer data will flow outside the Azure compliance boundary. Learn more here.

Use custom-configured Bing search instances for domain-specific or filtered web search results:

tool = BingCustomSearchPreviewTool(
    bing_custom_search_preview=BingCustomSearchToolParameters(
        search_configurations=[
            BingCustomSearchConfiguration(
                project_connection_id=os.environ["BING_CUSTOM_SEARCH_PROJECT_CONNECTION_ID"],
                instance_name=os.environ["BING_CUSTOM_SEARCH_INSTANCE_NAME"],
            )
        ]
    )
)

See the full sample in file \agents\tools\sample_agent_bing_custom_search.py in the Samples folder.

Microsoft Fabric (Preview)

Connect to and query Microsoft Fabric:

tool = MicrosoftFabricPreviewTool(
    fabric_dataagent_preview=FabricDataAgentToolParameters(
        project_connections=[
            ToolProjectConnection(project_connection_id=os.environ["FABRIC_PROJECT_CONNECTION_ID"])
        ]
    )
)

See the full sample in file \agents\tools\sample_agent_fabric.py in the Samples folder.

Microsoft SharePoint (Preview)

Access and search SharePoint documents, lists, and sites for enterprise knowledge integration:

tool = SharepointPreviewTool(
    sharepoint_grounding_preview=SharepointGroundingToolParameters(
        project_connections=[
            ToolProjectConnection(project_connection_id=os.environ["SHAREPOINT_PROJECT_CONNECTION_ID"])
        ]
    )
)

See the full sample in file \agents\tools\sample_agent_sharepoint.py in the Samples folder.

Browser Automation (Preview)

Automate browser interactions for web scraping, testing, and interaction with web applications:

tool = BrowserAutomationPreviewTool(
    browser_automation_preview=BrowserAutomationToolParameters(
        connection=BrowserAutomationToolConnectionParameters(
            project_connection_id=os.environ["BROWSER_AUTOMATION_PROJECT_CONNECTION_ID"],
        )
    )
)

See the full sample in file \agents\tools\sample_agent_browser_automation.py in the Samples folder.

MCP with Project Connection

MCP integration using project-specific connections for accessing connected MCP servers:

tool = MCPTool(
    server_label="api-specs",
    server_url="https://api.githubcopilot.com/mcp",
    require_approval="always",
    project_connection_id=os.environ["MCP_PROJECT_CONNECTION_ID"],
)

See the full sample in file \agents\tools\sample_agent_mcp_with_project_connection.py in the Samples folder.

Agent-to-Agent (A2A) (Preview)

Enable multi-agent collaboration where agents can communicate and delegate tasks to other specialized agents:

tool = A2APreviewTool(
    project_connection_id=os.environ["A2A_PROJECT_CONNECTION_ID"],
)
# If the connection is missing target, we need to set the A2A endpoint URL.
if os.environ.get("A2A_ENDPOINT"):
    tool.base_url = os.environ["A2A_ENDPOINT"]

See the full sample in file \agents\tools\sample_agent_to_agent.py in the Samples folder.

OpenAPI with Project Connection

Call external APIs defined by OpenAPI specifications using project connection authentication:

with open(tripadvisor_asset_file_path, "r", encoding="utf-8") as f:
    openapi_tripadvisor = cast(dict[str, Any], jsonref.loads(f.read()))

tool = OpenApiTool(
    openapi=OpenApiFunctionDefinition(
        name="tripadvisor",
        spec=openapi_tripadvisor,
        description="Trip Advisor API to get travel information",
        auth=OpenApiProjectConnectionAuthDetails(
            security_scheme=OpenApiProjectConnectionSecurityScheme(
                project_connection_id=os.environ["OPENAPI_PROJECT_CONNECTION_ID"]
            )
        ),
    )
)

See the full sample in file \agents\tools\sample_agent_openapi_with_project_connection.py in the Samples folder.

Evaluation

Evaluation in Azure AI Project client library provides quantitative, AI-assisted quality and safety metrics to asses performance and Evaluate LLM Models, GenAI Application and Agents. Metrics are defined as evaluators. Built-in or custom evaluators can provide comprehensive evaluation insights.

The code below shows some evaluation operations. Full list of sample can be found under "evaluation" folder in the package samples

with (
    DefaultAzureCredential() as credential,
    AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
    project_client.get_openai_client() as openai_client,
):
    agent = project_client.agents.create_version(
        agent_name=os.environ["AZURE_AI_AGENT_NAME"],
        definition=PromptAgentDefinition(
            model=model_deployment_name,
            instructions="You are a helpful assistant that answers general questions",
        ),
    )
    print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")

    data_source_config = DataSourceConfigCustom(
        type="custom",
        item_schema={"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]},
        include_sample_schema=True,
    )
    # Notes: for data_mapping:
    # sample.output_text is the string output of the agent
    # sample.output_items is the structured JSON output of the agent, including tool calls information
    testing_criteria = [
        {
            "type": "azure_ai_evaluator",
            "name": "violence_detection",
            "evaluator_name": "builtin.violence",
            "data_mapping": {"query": "{{item.query}}", "response": "{{sample.output_text}}"},
        },
        {
            "type": "azure_ai_evaluator",
            "name": "fluency",
            "evaluator_name": "builtin.fluency",
            "initialization_parameters": {"deployment_name": f"{model_deployment_name}"},
            "data_mapping": {"query": "{{item.query}}", "response": "{{sample.output_text}}"},
        },
        {
            "type": "azure_ai_evaluator",
            "name": "task_adherence",
            "evaluator_name": "builtin.task_adherence",
            "initialization_parameters": {"deployment_name": f"{model_deployment_name}"},
            "data_mapping": {"query": "{{item.query}}", "response": "{{sample.output_items}}"},
        },
    ]
    eval_object = openai_client.evals.create(
        name="Agent Evaluation",
        data_source_config=data_source_config,
        testing_criteria=testing_criteria,  # type: ignore
    )
    print(f"Evaluation created (id: {eval_object.id}, name: {eval_object.name})")

    data_source = {
        "type": "azure_ai_target_completions",
        "source": {
            "type": "file_content",
            "content": [
                {"item": {"query": "What is the capital of France?"}},
                {"item": {"query": "How do I reverse a string in Python?"}},
            ],
        },
        "input_messages": {
            "type": "template",
            "template": [
                {"type": "message", "role": "user", "content": {"type": "input_text", "text": "{{item.query}}"}}
            ],
        },
        "target": {
            "type": "azure_ai_agent",
            "name": agent.name,
            "version": agent.version,  # Version is optional. Defaults to latest version if not specified
        },
    }

    agent_eval_run: Union[RunCreateResponse, RunRetrieveResponse] = openai_client.evals.runs.create(
        eval_id=eval_object.id, name=f"Evaluation Run for Agent {agent.name}", data_source=data_source  # type: ignore
    )
    print(f"Evaluation run created (id: {agent_eval_run.id})")

Deployments operations

The code below shows some Deployments operations, which allow you to enumerate the AI models deployed to your AI Foundry Projects. These models can be seen in the "Models + endpoints" tab in your AI Foundry Project. Full samples can be found under the "deployment" folder in the package samples.

print("List all deployments:")
for deployment in project_client.deployments.list():
    print(deployment)

print(f"List all deployments by the model publisher `{model_publisher}`:")
for deployment in project_client.deployments.list(model_publisher=model_publisher):
    print(deployment)

print(f"List all deployments of model `{model_name}`:")
for deployment in project_client.deployments.list(model_name=model_name):
    print(deployment)

print(f"Get a single deployment named `{model_deployment_name}`:")
deployment = project_client.deployments.get(model_deployment_name)
print(deployment)

# At the moment, the only deployment type supported is ModelDeployment
if isinstance(deployment, ModelDeployment):
    print(f"Type: {deployment.type}")
    print(f"Name: {deployment.name}")
    print(f"Model Name: {deployment.model_name}")
    print(f"Model Version: {deployment.model_version}")
    print(f"Model Publisher: {deployment.model_publisher}")
    print(f"Capabilities: {deployment.capabilities}")
    print(f"SKU: {deployment.sku}")
    print(f"Connection Name: {deployment.connection_name}")

Connections operations

The code below shows some Connection operations, which allow you to enumerate the Azure Resources connected to your AI Foundry Projects. These connections can be seen in the "Management Center", in the "Connected resources" tab in your AI Foundry Project. Full samples can be found under the "connections" folder in the package samples.

print("List all connections:")
for connection in project_client.connections.list():
    print(connection)

print("List all connections of a particular type:")
for connection in project_client.connections.list(
    connection_type=ConnectionType.AZURE_OPEN_AI,
):
    print(connection)

print("Get the default connection of a particular type, without its credentials:")
connection = project_client.connections.get_default(connection_type=ConnectionType.AZURE_OPEN_AI)
print(connection)

print("Get the default connection of a particular type, with its credentials:")
connection = project_client.connections.get_default(
    connection_type=ConnectionType.AZURE_OPEN_AI, include_credentials=True
)
print(connection)

print(f"Get the connection named `{connection_name}`, without its credentials:")
connection = project_client.connections.get(connection_name)
print(connection)

print(f"Get the connection named `{connection_name}`, with its credentials:")
connection = project_client.connections.get(connection_name, include_credentials=True)
print(connection)

Dataset operations

The code below shows some Dataset operations. Full samples can be found under the "datasets" folder in the package samples.

print(
    f"Upload a single file and create a new Dataset `{dataset_name}`, version `{dataset_version_1}`, to reference the file."
)
dataset: DatasetVersion = project_client.datasets.upload_file(
    name=dataset_name,
    version=dataset_version_1,
    file_path=data_file,
    connection_name=connection_name,
)
print(dataset)

print(
    f"Upload files in a folder (including sub-folders) and create a new version `{dataset_version_2}` in the same Dataset, to reference the files."
)
dataset = project_client.datasets.upload_folder(
    name=dataset_name,
    version=dataset_version_2,
    folder=data_folder,
    connection_name=connection_name,
    file_pattern=re.compile(r"\.(txt|csv|md)$", re.IGNORECASE),
)
print(dataset)

print(f"Get an existing Dataset version `{dataset_version_1}`:")
dataset = project_client.datasets.get(name=dataset_name, version=dataset_version_1)
print(dataset)

print(f"Get credentials of an existing Dataset version `{dataset_version_1}`:")
dataset_credential = project_client.datasets.get_credentials(name=dataset_name, version=dataset_version_1)
print(dataset_credential)

print("List latest versions of all Datasets:")
for dataset in project_client.datasets.list():
    print(dataset)

print(f"Listing all versions of the Dataset named `{dataset_name}`:")
for dataset in project_client.datasets.list_versions(name=dataset_name):
    print(dataset)

print("Delete all Dataset versions created above:")
project_client.datasets.delete(name=dataset_name, version=dataset_version_1)
project_client.datasets.delete(name=dataset_name, version=dataset_version_2)

Indexes operations

The code below shows some Indexes operations. Full samples can be found under the "indexes" folder in the package samples.

print(f"Create Index `{index_name}` with version `{index_version}`, referencing an existing AI Search resource:")
index = project_client.indexes.create_or_update(
    name=index_name,
    version=index_version,
    index=AzureAISearchIndex(connection_name=ai_search_connection_name, index_name=ai_search_index_name),
)
print(index)

print(f"Get Index `{index_name}` version `{index_version}`:")
index = project_client.indexes.get(name=index_name, version=index_version)
print(index)

print("List latest versions of all Indexes:")
for index in project_client.indexes.list():
    print(index)

print(f"Listing all versions of the Index named `{index_name}`:")
for index in project_client.indexes.list_versions(name=index_name):
    print(index)

print(f"Delete Index `{index_name}` version `{index_version}`:")
project_client.indexes.delete(name=index_name, version=index_version)

Files operations

The code below shows some Files operations using the OpenAI client, which allow you to upload, retrieve, list, and delete files. These operations are useful for working with files that can be used for fine-tuning and other AI model operations. Full samples can be found under the "files" folder in the package samples.

print("Uploading file")
with open(file_path, "rb") as f:
    uploaded_file = openai_client.files.create(file=f, purpose="fine-tune")
print(uploaded_file)

print("Waits for the given file to be processed, default timeout is 30 mins")
processed_file = openai_client.files.wait_for_processing(uploaded_file.id)
print(processed_file)

print(f"Retrieving file metadata with ID: {processed_file.id}")
retrieved_file = openai_client.files.retrieve(processed_file.id)
print(retrieved_file)

print(f"Retrieving file content with ID: {processed_file.id}")
file_content = openai_client.files.content(processed_file.id)
print(file_content.content)

print("Listing all files:")
for file in openai_client.files.list():
    print(file)

print(f"Deleting file with ID: {processed_file.id}")
deleted_file = openai_client.files.delete(processed_file.id)
print(f"Successfully deleted file: {deleted_file.id}")

Fine-tuning operations

The code below shows how to create fine-tuning jobs using the OpenAI client. These operations support various fine-tuning techniques like Supervised Fine-Tuning (SFT), Reinforcement Fine-Tuning (RFT), and Direct Performance Optimization (DPO). Full samples can be found under the "finetuning" folder in the package samples.

print("Uploading training file...")
with open(training_file_path, "rb") as f:
    train_file = openai_client.files.create(file=f, purpose="fine-tune")
print(f"Uploaded training file with ID: {train_file.id}")

print("Uploading validation file...")
with open(validation_file_path, "rb") as f:
    validation_file = openai_client.files.create(file=f, purpose="fine-tune")
print(f"Uploaded validation file with ID: {validation_file.id}")

print("Waits for the training and validation files to be processed...")
openai_client.files.wait_for_processing(train_file.id)
openai_client.files.wait_for_processing(validation_file.id)

print("Creating supervised fine-tuning job")
fine_tuning_job = openai_client.fine_tuning.jobs.create(
    training_file=train_file.id,
    validation_file=validation_file.id,
    model=model_name,
    method={
        "type": "supervised",
        "supervised": {"hyperparameters": {"n_epochs": 3, "batch_size": 1, "learning_rate_multiplier": 1.0}},
    },
    extra_body={
        "trainingType": "GlobalStandard"
    },  # Recommended approach to set trainingType. Omitting this field may lead to unsupported behavior.
    # Preferred trainingtype is GlobalStandard.  Note:  Global training offers cost savings , but copies data and weights outside the current resource region.
    # Learn more - https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/ and https://azure.microsoft.com/explore/global-infrastructure/data-residency/
)
print(fine_tuning_job)

Tracing

Experimental Feature Gate

Important: GenAI tracing instrumentation is an experimental preview feature. Spans, attributes, and events may be modified in future versions. To use it, you must explicitly opt in by setting the environment variable:

AZURE_EXPERIMENTAL_ENABLE_GENAI_TRACING=true

This environment variable must be set before calling AIProjectInstrumentor().instrument(). If the environment variable is not set or is set to any value other than true (case-insensitive), tracing instrumentation will not be enabled and a warning will be logged.

Only enable this feature after reviewing your requirements and understanding that the tracing behavior may change in future versions.

Getting Started with Tracing

You can add an Application Insights Azure resource to your Microsoft Foundry project. See the Tracing tab in your AI Foundry project. If one was enabled, you can get the Application Insights connection string, configure your AI Projects client, and observe traces in Azure Monitor. Typically, you might want to start tracing before you create a client or Agent.

Installation

Make sure to install OpenTelemetry and the Azure SDK tracing plugin via

pip install "azure-ai-projects>=2.0.0b4" opentelemetry-sdk azure-core-tracing-opentelemetry azure-monitor-opentelemetry

You will also need an exporter to send telemetry to your observability backend. You can print traces to the console or use a local viewer such as Aspire Dashboard.

To connect to Aspire Dashboard or another OpenTelemetry compatible backend, install OTLP exporter:

pip install opentelemetry-exporter-otlp

How to enable tracing

Remember: Before enabling tracing, ensure you have set the AZURE_EXPERIMENTAL_ENABLE_GENAI_TRACING=true environment variable as described in the Experimental Feature Gate section.

Here is a code sample that shows how to enable Azure Monitor tracing:

# Enable Azure Monitor tracing
application_insights_connection_string = project_client.telemetry.get_application_insights_connection_string()
configure_azure_monitor(connection_string=application_insights_connection_string)

You may also want to create a span for your scenario:

tracer = trace.get_tracer(__name__)
scenario = os.path.basename(__file__)

with tracer.start_as_current_span(scenario):

See the full sample in file \agents\telemetry\sample_agent_basic_with_azure_monitor_tracing.py in the Samples folder.

Note: In order to view the traces in the Microsoft Foundry portal, the agent ID should be passed in as part of the response generation request.

In addition, you might find it helpful to see the tracing logs in the console. Remember to set AZURE_EXPERIMENTAL_ENABLE_GENAI_TRACING=true before running the following code:

# Setup tracing to console
# Requires opentelemetry-sdk
span_exporter = ConsoleSpanExporter()
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(SimpleSpanProcessor(span_exporter))
trace.set_tracer_provider(tracer_provider)
tracer = trace.get_tracer(__name__)

# Enable instrumentation with content tracing
AIProjectInstrumentor().instrument()

See the full sample in file \agents\telemetry\sample_agent_basic_with_console_tracing.py in the Samples folder.

Enabling trace context propagation

Trace context propagation allows client-side spans generated by the Projects SDK to be correlated with server-side spans from Azure OpenAI and other Azure services. When enabled, the SDK automatically injects W3C Trace Context headers (traceparent and tracestate) into HTTP requests made by OpenAI clients obtained via get_openai_client().

This feature ensures that all operations within a distributed trace share the same trace ID, providing end-to-end visibility across your application and Azure services in your observability backend (such as Azure Monitor).

To enable trace context propagation, set the AZURE_TRACING_GEN_AI_ENABLE_TRACE_CONTEXT_PROPAGATION environment variable to true:

If no value is provided for the enable_trace_context_propagation parameter with the AIProjectInstrumentor.instrument()call and the environment variable is not set, trace context propagation defaults tofalse` (opt-in).

Important Security and Privacy Considerations:

  • Trace IDs: When trace context propagation is enabled, trace IDs are sent to Azure OpenAI and other external services.
  • Request Correlation: Trace IDs allow Azure services to correlate requests from the same session or user across multiple API calls, which may have privacy implications depending on your use case.
  • Opt-in by Design: This feature is disabled by default to give you explicit control over when trace context is propagated to external services.

Only enable trace context propagation after carefully reviewing your observability, privacy and security requirements.

Controlling baggage propagation

When trace context propagation is enabled, you can separately control whether the baggage header is included. By default, only traceparent and tracestate headers are propagated. To also include the baggage header, set the AZURE_TRACING_GEN_AI_TRACE_CONTEXT_PROPAGATION_INCLUDE_BAGGAGE environment variable to true:

If no value is provided for the enable_baggage_propagation parameter with the AIProjectInstrumentor.instrument() call and the environment variable is not set, the value defaults to false and baggage is not included.

Why is baggage propagation separate?

The baggage header can contain arbitrary key-value pairs added anywhere in your application's trace context. Unlike trace IDs (which are randomly generated identifiers), baggage may contain:

  • User identifiers or session information
  • Authentication tokens or credentials
  • Business-specific data or metadata
  • Personally identifiable information (PII)

Baggage is automatically propagated through your entire application's call chain, meaning data added in one part of your application will be included in requests to Azure OpenAI unless explicitly controlled.

Important Security Considerations:

  • Review Baggage Contents: Before enabling baggage propagation, audit what data your application (and any third-party libraries) adds to OpenTelemetry baggage.
  • Sensitive Data Risk: Baggage is sent to Azure OpenAI and may be logged or processed by Microsoft services. Never add sensitive information to baggage when baggage propagation is enabled.
  • Opt-in by Design: Baggage propagation is disabled by default (even when trace context propagation is enabled) to prevent accidental exposure of sensitive data.
  • Minimal Propagation: traceparent and tracestate headers are generally sufficient for distributed tracing. Only enable baggage propagation if your specific observability requirements demand it.

Enabling content recording

Content recording controls whether message contents and tool call related details, such as parameters and return values, are captured with the traces. This data may include sensitive user information.

To enable content recording, set the OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT environment variable to true. If the environment variable is not set and no value is provided with the AIProjectInstrumentor().instrument() call for the content recording parameter, content recording defaults to false.

Important: The environment variable only controls content recording for built-in traces. When you use custom tracing decorators on your own functions, all parameters and return values are always traced.

Disabling automatic instrumentation

The AI Projects client library automatically instruments OpenAI responses and conversations operations through AiProjectInstrumentation. You can disable this instrumentation by setting the environment variable AZURE_TRACING_GEN_AI_INSTRUMENT_RESPONSES_API to false. If the environment variable is not set, the responses and conversations APIs will be instrumented by default.

Tracing Binary Data

Binary data are images and files sent to the service as input messages. When you enable content recording (OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT set to true), by default you only trace file IDs and filenames. To enable full binary data tracing, set AZURE_TRACING_GEN_AI_INCLUDE_BINARY_DATA to true. In this case:

  • Images: Image URLs (including data URIs with base64-encoded content) are included
  • Files: File data is included if sent via the API

Important: Binary data can contain sensitive information and may significantly increase trace size. Some trace backends and tracing implementations may have limitations on the maximum size of trace data that can be sent to and/or supported by the backend. Ensure your observability backend and tracing implementation support the expected trace payload sizes when enabling binary data tracing.

How to trace your own functions

The decorator trace_function is provided for tracing your own function calls using OpenTelemetry. By default the function name is used as the name for the span. Alternatively you can provide the name for the span as a parameter to the decorator.

Note: The OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT environment variable does not affect custom function tracing. When you use the trace_function decorator, all parameters and return values are always traced by default.

This decorator handles various data types for function parameters and return values, and records them as attributes in the trace span. The supported data types include:

  • Basic data types: str, int, float, bool
  • Collections: list, dict, tuple, set
    • Special handling for collections:
      • If a collection (list, dict, tuple, set) contains nested collections, the entire collection is converted to a string before being recorded as an attribute.
      • Sets and dictionaries are always converted to strings to ensure compatibility with span attributes.

Object types are omitted, and the corresponding parameter is not traced.

The parameters are recorded in attributes code.function.parameter.<parameter_name> and the return value is recorder in attribute code.function.return.value

Adding custom attributes to spans

You can add custom attributes to spans by creating a custom span processor. Here's how to define one:

class CustomAttributeSpanProcessor(SpanProcessor):
    def __init__(self):
        pass

    def on_start(self, span: Span, parent_context=None):
        # Add this attribute to all spans
        span.set_attribute("trace_sample.sessionid", "123")

        # Add another attribute only to create_thread spans
        if span.name == "create_thread":
            span.set_attribute("trace_sample.create_thread.context", "abc")

    def on_end(self, span: ReadableSpan):
        # Clean-up logic can be added here if necessary
        pass

Then add the custom span processor to the global tracer provider:

provider = cast(TracerProvider, trace.get_tracer_provider())
provider.add_span_processor(CustomAttributeSpanProcessor())

See the full sample in file \agents\telemetry\sample_agent_basic_with_console_tracing_custom_attributes.py in the Samples folder.

Additional resources

For more information see:

Troubleshooting

Exceptions

Client methods that make service calls raise an HttpResponseError exception for a non-success HTTP status code response from the service. The exception's status_code will hold the HTTP response status code (with reason showing the friendly name). The exception's error.message contains a detailed message that may be helpful in diagnosing the issue:

from azure.core.exceptions import HttpResponseError

...

try:
    result = project_client.connections.list()
except HttpResponseError as e:
    print(f"Status code: {e.status_code} ({e.reason})")
    print(e.message)

For example, when you provide wrong credentials:

Status code: 401 (Unauthorized)
Operation returned an invalid status 'Unauthorized'

Logging

The client uses the standard Python logging library. The logs include HTTP request and response headers and body, which are often useful when troubleshooting or reporting an issue to Microsoft.

Default console logging

To turn on client console logging define the environment variable AZURE_AI_PROJECTS_CONSOLE_LOGGING=true before running your Python script. Authentication bearer tokens are automatically redacted from the log. Your log may contain other sensitive information, so be sure to remove it before sharing the log with others.

Customizing your log

Instead of using the above-mentioned environment variable, you can configure logging yourself and control the log level, format and destination. To log to stdout, add the following at the top of your Python script:

import sys
import logging

# Acquire the logger for this client library. Use 'azure' to affect both
# 'azure.core` and `azure.ai.inference' libraries.
logger = logging.getLogger("azure")

# Set the desired logging level. logging.INFO or logging.DEBUG are good options.
logger.setLevel(logging.DEBUG)

# Direct logging output to stdout:
handler = logging.StreamHandler(stream=sys.stdout)
# Or direct logging output to a file:
# handler = logging.FileHandler(filename="sample.log")
logger.addHandler(handler)

# Optional: change the default logging format. Here we add a timestamp.
#formatter = logging.Formatter("%(asctime)s:%(levelname)s:%(name)s:%(message)s")
#handler.setFormatter(formatter)

By default logs redact the values of URL query strings, the values of some HTTP request and response headers (including Authorization which holds the key or token), and the request and response payloads. To create logs without redaction, add logging_enable=True to the client constructor:

project_client = AIProjectClient(
    credential=DefaultAzureCredential(),
    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    logging_enable=True
)

Note that the log level must be set to logging.DEBUG (see above code). Logs will be redacted with any other log level.

Be sure to protect non redacted logs to avoid compromising security.

For more information, see Configure logging in the Azure libraries for Python

Reporting issues

To report an issue with the client library, or request additional features, please open a GitHub issue here. Mention the package name "azure-ai-projects" in the title or content.

Next steps

Have a look at the Samples folder, containing fully runnable Python code for synchronous and asynchronous clients.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information, see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

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