Fix docstring/load_dotenv ordering in foundry samples and add try/finally cleanup

Agent-Logs-Url: https://github.com/microsoft/agent-framework/sessions/382df85c-cf3c-4802-97e0-a6df35fc9876

Co-authored-by: chetantoshniwal <255221507+chetantoshniwal@users.noreply.github.com>

python/samples/02-agents/providers/anthropic/anthropic_advanced.py

@@ -20,7 +20,7 @@ This sample demonstrates using Anthropic with:
 
 async def main() -> None:
     """Example of streaming response (get results as they are generated)."""
-    client = AnthropicClient[AnthropicChatOptions]()
+    client = AnthropicClient[AnthropicChatOptions](model_id="claude-sonnet-4-5-20250929")
 
     # Create MCP tool configuration using instance method
     mcp_tool = client.get_mcp_tool(

python/samples/02-agents/providers/azure/openai_client_with_session.py

@@ -76,19 +76,19 @@ async def example_with_session_persistence_in_memory() -> None:
     # First conversation
     query1 = "What's the weather like in Tokyo?"
     print(f"User: {query1}")
-    result1 = await agent.run(query1, session=session, store=False)
+    result1 = await agent.run(query1, session=session, options={"store": False})
     print(f"Agent: {result1.text}")
 
     # Second conversation using the same session - maintains context
     query2 = "How about London?"
     print(f"\nUser: {query2}")
-    result2 = await agent.run(query2, session=session, store=False)
+    result2 = await agent.run(query2, session=session, options={"store": False})
     print(f"Agent: {result2.text}")
 
     # Third conversation - agent should remember both previous cities
     query3 = "Which of the cities I asked about has better weather?"
     print(f"\nUser: {query3}")
-    result3 = await agent.run(query3, session=session, store=False)
+    result3 = await agent.run(query3, session=session, options={"store": False})
     print(f"Agent: {result3.text}")
     print("Note: The agent remembers context from previous messages in the same session.\n")
 

python/samples/02-agents/providers/foundry/foundry_agent_basic.py

@@ -1,9 +1,11 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import asyncio
+import os
 
 from agent_framework.foundry import FoundryAgent
 from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
 
 """
 Foundry Agent — Connect to a pre-configured agent in Microsoft Foundry
@@ -15,15 +17,18 @@ tools are all configured on the service — you just connect and run.
 Environment variables:
     FOUNDRY_PROJECT_ENDPOINT — Azure AI Foundry project endpoint
     FOUNDRY_AGENT_NAME       — Name of the agent in Foundry
-    FOUNDRY_AGENT_VERSION    — Version of the agent (for PromptAgents)
+    FOUNDRY_AGENT_VERSION    — Version of the agent (optional, for PromptAgents)
 """
 
+# Load environment variables from .env file
+load_dotenv()
+
 
 async def main() -> None:
     agent = FoundryAgent(
-        project_endpoint="https://your-project.services.ai.azure.com",
-        agent_name="my-prompt-agent",
-        agent_version="1.0",
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        agent_name=os.environ["FOUNDRY_AGENT_NAME"],
+        agent_version=os.environ.get("FOUNDRY_AGENT_VERSION"),
         credential=AzureCliCredential(),
     )
 

python/samples/02-agents/providers/foundry/foundry_agent_custom_client.py

@@ -1,10 +1,12 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import asyncio
+import os
 
 from agent_framework import Agent
 from agent_framework.foundry import FoundryAgent, RawFoundryAgentChatClient
 from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
 
 """
 Foundry Agent — Custom client configuration
@@ -18,16 +20,19 @@ This sample demonstrates three ways to customize the FoundryAgent client layer:
 Environment variables:
     FOUNDRY_PROJECT_ENDPOINT — Azure AI Foundry project endpoint
     FOUNDRY_AGENT_NAME       — Name of the agent in Foundry
-    FOUNDRY_AGENT_VERSION    — Version of the agent
+    FOUNDRY_AGENT_VERSION    — Version of the agent (optional, for PromptAgents)
 """
 
+# Load environment variables from .env file
+load_dotenv()
+
 
 async def main() -> None:
     # Option 1: Default — full middleware on both agent and client
     agent = FoundryAgent(
-        project_endpoint="https://your-project.services.ai.azure.com",
-        agent_name="my-agent",
-        agent_version="1.0",
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        agent_name=os.environ["FOUNDRY_AGENT_NAME"],
+        agent_version=os.environ.get("FOUNDRY_AGENT_VERSION"),
         credential=AzureCliCredential(),
     )
     result = await agent.run("Hello from the default setup!")
@@ -35,9 +40,9 @@ async def main() -> None:
 
     # Option 2: Raw client — no client-level middleware (agent middleware still active)
     agent_raw_client = FoundryAgent(
-        project_endpoint="https://your-project.services.ai.azure.com",
-        agent_name="my-agent",
-        agent_version="1.0",
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        agent_name=os.environ["FOUNDRY_AGENT_NAME"],
+        agent_version=os.environ.get("FOUNDRY_AGENT_VERSION"),
         credential=AzureCliCredential(),
         client_type=RawFoundryAgentChatClient,
     )
@@ -47,9 +52,9 @@ async def main() -> None:
     # Option 3: Composition — use Agent(client=...) directly
     # this will not run the checks that the `FoundryAgent` does on things like tools.
     client = RawFoundryAgentChatClient(
-        project_endpoint="https://your-project.services.ai.azure.com",
-        agent_name="my-agent",
-        agent_version="1.0",
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        agent_name=os.environ["FOUNDRY_AGENT_NAME"],
+        agent_version=os.environ.get("FOUNDRY_AGENT_VERSION"),
         credential=AzureCliCredential(),
     )
     agent_composed = Agent(client=client)

python/samples/02-agents/providers/foundry/foundry_agent_hosted.py

@@ -1,9 +1,11 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import asyncio
+import os
 
 from agent_framework.foundry import FoundryAgent
 from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
 
 """
 Foundry Agent — Connect to a HostedAgent (no version needed)
@@ -16,12 +18,15 @@ Environment variables:
     FOUNDRY_AGENT_NAME       — Name of the hosted agent
 """
 
+# Load environment variables from .env file
+load_dotenv()
+
 
 async def main() -> None:
     # HostedAgents don't need agent_version
     agent = FoundryAgent(
-        project_endpoint="https://your-project.services.ai.azure.com",
-        agent_name="my-hosted-agent",
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        agent_name=os.environ["FOUNDRY_AGENT_NAME"],
         credential=AzureCliCredential(),
     )
 

python/samples/02-agents/providers/foundry/foundry_agent_with_env_vars.py

@@ -5,6 +5,7 @@ import os
 
 from agent_framework.foundry import FoundryAgent
 from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
 
 """
 Foundry Agent with Environment Variables
@@ -18,6 +19,9 @@ Environment variables:
     FOUNDRY_AGENT_VERSION    — Version of the agent (optional, for PromptAgents)
 """
 
+# Load environment variables from .env file
+load_dotenv()
+
 
 async def main() -> None:
     agent = FoundryAgent(

python/samples/02-agents/providers/foundry/foundry_agent_with_function_tools.py

@@ -1,11 +1,13 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import asyncio
+import os
 from typing import Annotated
 
 from agent_framework import tool
 from agent_framework.foundry import FoundryAgent
 from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
 from pydantic import Field
 
 """
@@ -24,6 +26,9 @@ Environment variables:
     FOUNDRY_AGENT_VERSION    — Version of the agent
 """
 
+# Load environment variables from .env file
+load_dotenv()
+
 
 @tool(approval_mode="never_require")
 def get_weather(
@@ -35,9 +40,9 @@ def get_weather(
 
 async def main() -> None:
     agent = FoundryAgent(
-        project_endpoint="https://your-project.services.ai.azure.com",
-        agent_name="my-weather-agent",
-        agent_version="1.0",
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        agent_name=os.environ["FOUNDRY_AGENT_NAME"],
+        agent_version=os.environ["FOUNDRY_AGENT_VERSION"],
         credential=AzureCliCredential(),
         tools=[get_weather],
     )

python/samples/02-agents/providers/foundry/foundry_chat_client_code_interpreter_files.py

@@ -8,22 +8,26 @@ from agent_framework import Agent
 from agent_framework.foundry import FoundryChatClient
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
-from openai import AsyncAzureOpenAI
-
-# Load environment variables from .env file
-load_dotenv()
+from openai import AsyncOpenAI
 
 """
 Foundry Chat Client with Code Interpreter and Files Example
 
 This sample demonstrates using get_code_interpreter_tool() with Responses on Foundry
 for Python code execution and data analysis with uploaded files.
+
+Environment variables:
+    FOUNDRY_PROJECT_ENDPOINT — Azure AI Foundry project endpoint
+    FOUNDRY_MODEL            — Model deployment name (e.g. "gpt-4o")
 """
 
+# Load environment variables from .env file
+load_dotenv()
+
 # Helper functions
 
 
-async def create_sample_file_and_upload(openai_client: AsyncAzureOpenAI) -> tuple[str, str]:
+async def create_sample_file_and_upload(openai_client: AsyncOpenAI) -> tuple[str, str]:
     """Create a sample CSV file and upload it for Foundry code interpreter use."""
     csv_data = """name,department,salary,years_experience
 Alice Johnson,Engineering,95000,5
@@ -51,7 +55,7 @@ Frank Wilson,Engineering,88000,6
     return temp_file_path, uploaded_file.id
 
 
-async def cleanup_files(openai_client: AsyncAzureOpenAI, temp_file_path: str, file_id: str) -> None:
+async def cleanup_files(openai_client: AsyncOpenAI, temp_file_path: str, file_id: str) -> None:
     """Clean up both local temporary file and uploaded file."""
     # Clean up: delete the uploaded file
     await openai_client.files.delete(file_id)
@@ -68,36 +72,33 @@ async def main() -> None:
     # Initialize the underlying OpenAI client for file operations
     credential = AzureCliCredential()
 
-    async def get_token():
-        token = credential.get_token("https://cognitiveservices.azure.com/.default")
-        return token.token
-
-    openai_client = AsyncAzureOpenAI(
-        azure_ad_token_provider=get_token,
-        api_version="2024-05-01-preview",
+    # Create FoundryChatClient first, then reuse its project client for file operations
+    client = FoundryChatClient(
+        credential=credential,
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        model=os.environ["FOUNDRY_MODEL"],
     )
+    openai_client = client.project_client.get_openai_client()
 
     temp_file_path, file_id = await create_sample_file_and_upload(openai_client)
 
-    # Create agent using FoundryChatClient
-    client = FoundryChatClient(credential=credential)
+    try:
+        # Create code interpreter tool with file access
+        code_interpreter_tool = client.get_code_interpreter_tool(file_ids=[file_id])
 
-    # Create code interpreter tool with file access
-    code_interpreter_tool = client.get_code_interpreter_tool(file_ids=[file_id])
+        agent = Agent(
+            client=client,
+            instructions="You are a helpful assistant that can analyze data files using Python code.",
+            tools=[code_interpreter_tool],
+        )
 
-    agent = Agent(
-        client=client,
-        instructions="You are a helpful assistant that can analyze data files using Python code.",
-        tools=[code_interpreter_tool],
-    )
-
-    # Test the code interpreter with the uploaded file
-    query = "Analyze the employee data in the uploaded CSV file. Calculate average salary by department."
-    print(f"User: {query}")
-    result = await agent.run(query)
-    print(f"Agent: {result.text}")
-
-    await cleanup_files(openai_client, temp_file_path, file_id)
+        # Test the code interpreter with the uploaded file
+        query = "Analyze the employee data in the uploaded CSV file. Calculate average salary by department."
+        print(f"User: {query}")
+        result = await agent.run(query)
+        print(f"Agent: {result.text}")
+    finally:
+        await cleanup_files(openai_client, temp_file_path, file_id)
 
 
 if __name__ == "__main__":