Azure AI Projects client library for Python - version 2.2.0

The AI Projects client library 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. This includes Hosted Agents (Preview), which let you run your own containerized agent runtime while using Microsoft Foundry for managed hosting and scaling.
• 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)
  • Fabric IQ (Preview)
  • File Search
  • Function Tool
  • Image Generation
  • Memory Search (Preview)
  • Microsoft Fabric (Preview)
  • Microsoft SharePoint (Preview)
  • Model Context Protocol (MCP)
  • OpenAPI
  • Toolbox Search (Preview)
  • Web Search
  • Web Search (Preview)
  • Work IQ (Preview)
• Get an OpenAI client using .get_openai_client() method to run Responses, Conversations, Evaluations and Fine-Tuning operations with your Agent.
• Fine-tune AI Models on your data.
• Enumerate AI Models deployed to your Foundry Project using .deployments operations.
• Enumerate connected Azure resources in your Foundry project using .connections operations.
• Upload documents and create Datasets to reference them using .datasets operations.
• Create and enumerate Search Indexes using .indexes operations.
• Explore additional evaluation tools (some in preview) to assess the performance of your generative AI application, using .evaluation_rules, .beta.evaluation_taxonomies, .beta.evaluators, .beta.insights, and .beta.schedules operations.
• Manage memory stores (preview) for Agent conversations, using .beta.memory_stores operations.
• Register and manage local AI model weights (preview) using .beta.models operations.
• Run Red Team scans (preview) to identify risks associated with your generative AI application, using .beta.red_teams operations.
• Create and manage routines (preview) that execute triggered actions based on configured conditions, using .beta.routines operations.
• Manage skills (preview) as reusable capabilities that can be included in toolboxes and agents, using .beta.skills operations.
• Create and version toolboxes (preview) that bundle collections of tools and skills for your agents, using .beta.toolboxes operations.

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

Microsoft Foundry | 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 home page. Below we will assume the environment variable FOUNDRY_PROJECT_ENDPOINT was defined to hold this value.
• Client authentication is done using Entra ID. To authenticate, your application needs an object that implements the TokenCredential interface. Code samples here use DefaultAzureCredential. To get that working, you will need:
  • An appropriate role assignment. See Role-based access control in Microsoft Foundry portal. Role assignment can be done via the "Access Control (IAM)" tab of your Azure AI Project resource in the Azure portal.
  • Azure CLI installed.
  • You are logged into your Azure account by running az login.

Install the package

pip install azure-ai-projects

Verify that you have version 2.2.0 or above installed by running:

pip show azure-ai-projects

Key concepts

Create and authenticate the client with Entra ID

Entra ID is the only authentication method currently supported by the client.

To construct a synchronous client using a context manager:

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

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

Performing Responses operations using OpenAI client

Use the .get_openai_client() method to obtain an authenticated OpenAI client and run Responses, Conversations, Evaluations, Files, and Fine-Tuning operations. See the responses, agents, evaluations, files, and finetuning folders in the samples for complete working examples.

The code below assumes the environment variable FOUNDRY_MODEL_NAME is defined. It's the deployment name of an AI model in your Foundry Project. See first column titled "Name" in the "Deployed models" table in your Foundry Project.

with project_client.get_openai_client() as openai_client:
    response = openai_client.responses.create(
        model=os.environ["FOUNDRY_MODEL_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["FOUNDRY_MODEL_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 samples for additional samples including streaming responses.

Agents

See Foundry documentation:

• Microsoft Foundry Agents overview — concepts, setup, and quick-starts.
• Runtime components — deep-dive into agent architecture.
• Tool catalog — all available tools and agent capabilities.
• SDK samples folder — fully runnable Python code for synchronous and asynchronous clients covering all operations below.

Examples

The table below lists the operation groups supported by the client library, with links to Foundry documentation and relevant samples sub-folder. Additional documentation and samples may have been added after this package was released to cover the empty cells below.

Client-side tracing

See Add client-side tracing to Foundry agents (preview).

Important: GenAI tracing instrumentation is an experimental preview feature. Spans, attributes, and events may be modified in future versions.

Samples can be found in the sub-folders agents/telemetry and telemetry in the Samples folder.

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.projects' 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["FOUNDRY_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.