azure-ai-foundry · gerbermarco · Oct 7, 2025
diff --git a/samples/microsoft/python/getting-started-agents/azure_ai_search.py b/samples/microsoft/python/getting-started-agents/azure_ai_search.py
@@ -36,7 +36,6 @@
 project_client = AIProjectClient(
     endpoint=project_endpoint,
     credential=DefaultAzureCredential(exclude_interactive_browser_credential=False),  # Use Azure Default Credential for authentication
-    api_version="latest",
 )
 
 with project_client:
@@ -81,7 +80,7 @@
 
     # Fetch and log all messages from the thread
     messages = project_client.agents.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
-    for message in messages.data:
+    for message in messages:
         print(f"Role: {message.role}, Content: {message.content}")
 
     # Delete the agent when done to clean up resources

diff --git a/samples/microsoft/python/getting-started-agents/azure_functions.py b/samples/microsoft/python/getting-started-agents/azure_functions.py
@@ -8,17 +8,17 @@
 DESCRIPTION:
     This sample demonstrates how to use azure function agent operations from
     the Azure Agents service using a synchronous client.
- 
+
 USAGE:
     python sample_agents_azure_functions.py
- 
+
     Before running the sample:
- 
+
     pip install azure-ai-agents azure-identity
- 
+
     Set these environment variables with your own values:
     1) PROJECT_ENDPOINT - the Azure AI Agents endpoint.
-    2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in 
+    2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
        the "Models + endpoints" tab in your Azure AI Foundry project.
     3) STORAGE_SERVICE_ENDPONT - the storage service queue endpoint, triggering Azure function.
        Please see Getting Started with Azure Functions page for more information on Azure Functions:
@@ -38,7 +38,6 @@
 project_client = AIProjectClient(
     endpoint=project_endpoint,
     credential=DefaultAzureCredential(),
-    api_version="latest",  # Use the latest API version
 )
 
 # Use the project client within a context manager to ensure proper resource cleanup

diff --git a/samples/microsoft/python/getting-started-agents/basic_agent.py b/samples/microsoft/python/getting-started-agents/basic_agent.py
@@ -37,7 +37,6 @@
 project_client = AIProjectClient(
     endpoint=project_endpoint,
     credential=DefaultAzureCredential(exclude_interactive_browser_credential=False),  # Use Azure Default Credential for authentication
-    api_version="latest",
 )
 
 with project_client:

diff --git a/samples/microsoft/python/getting-started-agents/bing_grounding.py b/samples/microsoft/python/getting-started-agents/bing_grounding.py
@@ -33,14 +33,13 @@
 project_endpoint = os.environ["PROJECT_ENDPOINT"]  # Ensure the PROJECT_ENDPOINT environment variable is set
 # Ensure the BING_CONNECTION_ID environment variable is set, following the format:
 #"/subscriptions/<sub-id>/resourceGroups/<your-rg-name>/providers/Microsoft.CognitiveServices/accounts/<your-ai-services-name>/projects/<your-project-name>/connections/<your-bing-connection-name>"
-conn_id = os.environ["BING_CONNECTION_ID"]  
+conn_id = os.environ["BING_CONNECTION_ID"]
 model_deployment_name = os.environ["MODEL_DEPLOYMENT_NAME"]  # Ensure the MODEL_DEPLOYMENT_NAME environment variable is set
 
 # Initialize the AIProjectClient with the endpoint and credentials
 project_client = AIProjectClient(
     endpoint=project_endpoint,
     credential=DefaultAzureCredential(),  # Use Azure Default Credential for authentication
-    api_version="latest",
 )
 
 with project_client:

diff --git a/samples/microsoft/python/getting-started-agents/enterprise_search.py b/samples/microsoft/python/getting-started-agents/enterprise_search.py
@@ -19,7 +19,7 @@
 
     Set these environment variables with your own values:
     1) PROJECT_ENDPOINT - The Azure AI Agents endpoint.
-    2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in 
+    2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
        the "Models + endpoints" tab in your Azure AI Foundry project.
 """
 
@@ -43,7 +43,6 @@
 project_client = AIProjectClient(
     endpoint=project_endpoint,
     credential=DefaultAzureCredential(exclude_interactive_browser_credential=False),  # Use Azure Default Credential for authentication
-    api_version="latest",
 )
 
 with project_client:
@@ -85,7 +84,7 @@
 
     # Fetch and log all messages from the thread
     messages = project_client.agents.messages.list(thread_id=thread.id)
-    for message in messages.data:
+    for message in messages:
         print(f"Role: {message.role}, Content: {message.content}")
 
     # Delete the agent after use

diff --git a/samples/microsoft/python/getting-started-agents/file_search.py b/samples/microsoft/python/getting-started-agents/file_search.py
@@ -40,7 +40,6 @@
 project_client = AIProjectClient(
     endpoint=project_endpoint,
     credential=DefaultAzureCredential(exclude_interactive_browser_credential=False),  # Use Azure Default Credential for authentication
-    api_version="latest",
 )
 
 with project_client:
@@ -103,5 +102,5 @@
 
     # Fetch and log all messages from the thread
     messages = project_client.agents.messages.list(thread_id=thread.id)
-    for message in messages.data:
+    for message in messages:
         print(f"Role: {message.role}, Content: {message.content}")
diff --git a/samples/microsoft/python/getting-started-agents/functions_calling.py b/samples/microsoft/python/getting-started-agents/functions_calling.py
@@ -17,7 +17,7 @@
 
     Set these environment variables with your own values:
     1) PROJECT_ENDPOINT - the Azure AI Agents endpoint.
-    2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in 
+    2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
        the "Models + endpoints" tab in your Azure AI Foundry project.
 """
 # Import necessary modules
@@ -33,7 +33,6 @@
 project_client = AIProjectClient(
     endpoint=project_endpoint,  # Azure AI Agents endpoint
     credential=DefaultAzureCredential(),  # Use Azure Default Credential for authentication
-    api_version="latest",  # Use the latest API version
 )
 
 # Initialize the FunctionTool with user-defined functions

diff --git a/samples/microsoft/python/getting-started-agents/logic_apps.py b/samples/microsoft/python/getting-started-agents/logic_apps.py
@@ -7,24 +7,24 @@
 """
 DESCRIPTION:
     This sample demonstrates how to use agents with Logic Apps to execute the task of sending an email.
- 
+
 PREREQUISITES:
     1) Create a Logic App within the same resource group as your Azure AI Project in Azure Portal
-    2) To configure your Logic App to send emails, you must include an HTTP request trigger that is 
+    2) To configure your Logic App to send emails, you must include an HTTP request trigger that is
     configured to accept JSON with 'to', 'subject', and 'body'. The guide to creating a Logic App Workflow
-    can be found here: 
+    can be found here:
     https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/agents-logic-apps#create-logic-apps-workflows-for-function-calling
-    
+
 USAGE:
     python sample_agents_logic_apps.py
- 
+
     Before running the sample:
- 
+
     pip install azure-ai-agents azure-identity
 
     Set this environment variables with your own values:
     1) PROJECT_ENDPOINT - the Azure AI Agents endpoint.
-    2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in 
+    2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
        the "Models + endpoints" tab in your Azure AI Foundry project.
 
     Replace the following values in the sample with your own values:
@@ -40,7 +40,7 @@
 from typing import Set
 from azure.ai.agents.models import ToolSet, FunctionTool
 from azure.identity import DefaultAzureCredential
-from azure.ai.projects import AIProjectClient  
+from azure.ai.projects import AIProjectClient
 
 # Import user-defined functions and tools
 from user_functions import fetch_current_datetime  # Example user function to fetch current datetime
@@ -52,7 +52,6 @@
 project_client = AIProjectClient(
     endpoint=os.environ["PROJECT_ENDPOINT"],  # Azure AI Agents endpoint from environment variables
     credential=DefaultAzureCredential(),  # Use Azure Default Credential for authentication
-    api_version="latest",  # Use the latest API version
 )
 
 # Extract subscription ID and resource group name from environment variables

diff --git a/samples/microsoft/python/getting-started-agents/quickstart.py b/samples/microsoft/python/getting-started-agents/quickstart.py
@@ -37,7 +37,6 @@
 project_client = AIProjectClient(
     endpoint=os.environ["PROJECT_ENDPOINT"],  # Ensure the PROJECT_ENDPOINT environment variable is set
     credential=DefaultAzureCredential(),  # Use Azure Default Credential for authentication
-    api_version="latest",
 )
 
 # Access the Azure OpenAI client for chat completions
@@ -85,7 +84,7 @@
 
 # Fetch and log all messages from the thread
 messages = project_client.agents.messages.list(thread_id=thread.id)
-for message in messages.data:
+for message in messages:
     print(f"Role: {message.role}, Content: {message.content}")
 
 # Delete the agent after use
@@ -133,7 +132,7 @@
 
 result = project.evaluation.create_agent_evaluation(
     thread=thread.id,
-    run=run.id, 
+    run=run.id,
     evaluators=[EvaluatorIds.AGENT_QUALITY_EVALUATOR])
 
 # wait for evaluation to complete