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Migrate samples from agentsv2-preview repo (#43798)
* Migrate samples from agentsv2-preview repo * run black * update * fix * comment out some samples
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‎sdk/ai/azure-ai-projects/samples/agents/sample_agent_memory.py‎

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# # ------------------------------------
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# # Copyright (c) Microsoft Corporation.
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# # Licensed under the MIT License.
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# # ------------------------------------
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# """
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# DESCRIPTION:
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# This sample demonstrates how to integrate memory into a prompt agent.
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# USAGE:
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# python sample_agent_memory.py
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# Before running the sample:
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# pip install python-dotenv azure-identity azure-ai-projects>=2.0.0b1
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# Deploy a chat model (e.g. gpt-4.1) and an embedding model (e.g. text-embedding-3-small).
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# Once you have deployed models, set the deployment name in the variables below.
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# Set these environment variables with your own values:
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# 1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
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# page of your Azure AI Foundry portal.
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# 2) AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME - The deployment name of the chat model for the agent, as found under the "Name" column in
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# the "Models + endpoints" tab in your Azure AI Foundry project.
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# 3) AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME - The deployment name of the chat model for memory, as found under the "Name" column in
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# the "Models + endpoints" tab in your Azure AI Foundry project.
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# 4) AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME - The deployment name of the embedding model for memory, as found under the
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# "Name" column in the "Models + endpoints" tab in your Azure AI Foundry project.
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# """
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# import os
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# from dotenv import load_dotenv
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# from azure.identity import DefaultAzureCredential
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# from azure.ai.projects import AIProjectClient
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# from azure.ai.projects.models import (
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# MemoryStoreDefaultDefinition,
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# MemoryStoreDefaultOptions,
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# MemorySearchOptions,
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# ResponsesUserMessageItemParam,
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# MemorySearchTool,
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# PromptAgentDefinition,
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# )
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# load_dotenv()
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# project_client = AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=DefaultAzureCredential())
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# with project_client:
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# openai_client = project_client.get_openai_client()
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# # Create a memory store
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# definition = MemoryStoreDefaultDefinition(
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# chat_model=os.environ["AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME"],
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# embedding_model=os.environ["AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME"],
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# )
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# memory_store = project_client.memory_stores.create(
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# name="my_memory_store",
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# description="Example memory store for conversations",
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# definition=definition,
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# )
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# print(f"Created memory store: {memory_store.name} ({memory_store.id}): {memory_store.description}")
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# # Create a prompt agent with memory search tool
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# agent = project_client.agents.create_version(
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# agent_name="MyAgent",
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# definition=PromptAgentDefinition(
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# model=os.environ["AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME"],
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# instructions="You are a helpful assistant that answers general questions",
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# ),
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# tools=[
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# MemorySearchTool(
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# memory_store_name=memory_store.name,
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# scope="{{$userId}}",
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# update_delay=10, # Wait 5 seconds of inactivity before updating memories
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# # In a real application, set this to a higher value like 300 (5 minutes, default)
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# )
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# ],
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# )
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# print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")
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# # Create a conversation with the agent with memory tool enabled
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# conversation = openai_client.conversations.create()
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# print(f"Created conversation (id: {conversation.id})")
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# # Create an agent response to initial user message
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# response = openai_client.responses.create(
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# conversation=conversation.id,
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# extra_body={"agent": AgentReference(name=agent.name).as_dict()},
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# input=[ResponsesUserMessageItemParam(content="I prefer dark roast coffee")],
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# )
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# print(f"Response output: {response.output_text}")
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# # After an inactivity in the conversation, memories will be extracted from the conversation and stored
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# sleep(60)
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# # Create a new conversation
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# new_conversation = openai_client.conversations.create()
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# print(f"Created new conversation (id: {new_conversation.id})")
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# # Create an agent response with stored memories
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# new_response = openai_client.responses.create(
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# conversation=new_conversation.id,
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# extra_body={"agent": AgentReference(name=agent.name).as_dict()},
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# input=[ResponsesUserMessageItemParam(content="Please order my usual coffee")],
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# )
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# print(f"Response output: {new_response.output_text}")
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# # Clean up
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# openai_client.conversations.delete(conversation.id)
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# openai_client.conversations.delete(new_conversation.id)
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# print("Conversations deleted")
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# project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
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# print("Agent deleted")
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# project_client.memory_stores.delete(memory_store.name)
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# print("Memory store deleted")

‎sdk/ai/azure-ai-projects/samples/agents/sample_mcp_tool.py‎

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# ------------------------------------
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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# ------------------------------------
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"""
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DESCRIPTION:
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This sample demonstrates how to interact with the Foundry Project MCP tool.
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USAGE:
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python sample_mcp_tool.py
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Before running the sample:
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pip install python-dotenv azure-identity azure-ai-projects>=2.0.0b1 mcp
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Set these environment variables with your own values:
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1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
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page of your Azure AI Foundry portal.
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2) IMAGE_GEN_DEPLOYMENT_NAME - The deployment name of the image generation model, as found under the "Name" column in
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the "Models + endpoints" tab in your Azure AI Foundry project.
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"""
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import asyncio
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import os
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from dotenv import load_dotenv
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from azure.ai.projects.aio import AIProjectClient
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from azure.identity.aio import DefaultAzureCredential
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from mcp import ClientSession
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from mcp.client.streamable_http import streamablehttp_client
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load_dotenv()
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async def main():
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credential = DefaultAzureCredential()
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try:
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# Fetch the Entra ID token with audience as https://ai.azure.com
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access_token = await credential.get_token("https://ai.azure.com")
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endpoint = os.getenv("AZURE_AI_PROJECT_ENDPOINT", "").rstrip("/")
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async with streamablehttp_client(
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url=f"{endpoint}/mcp_tools?api-version=2025-05-15-preview",
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headers={"Authorization": f"Bearer {access_token.token}"},
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) as (read_stream, write_stream, _):
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# Create a session using the client streams
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async with ClientSession(read_stream, write_stream) as session:
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# Initialize the connection
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await session.initialize()
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# List available tools
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tools = await session.list_tools()
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print(f"Available tools: {[tool.name for tool in tools.tools]}")
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# For each tool, print its details
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for tool in tools.tools:
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print(f"\n\nTool Name: {tool.name}, Input Schema: {tool.inputSchema}")
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# Run the code interpreter tool
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code_interpreter_result = await session.call_tool(
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name="code_interpreter",
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arguments={"code": "print('Hello from Azure AI Foundry MCP Code Interpreter tool!')"},
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)
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print(f"\n\nCode Interpreter Output: {code_interpreter_result.content}")
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# Run the image_generation tool
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image_generation_result = await session.call_tool(
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name="image_generation",
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arguments={"prompt": "Draw a cute puppy riding a skateboard"},
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meta={"imagegen_model_deployment_name": os.getenv("IMAGE_GEN_DEPLOYMENT_NAME", "")},
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)
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print(f"\n\nImage Generation Output: {image_generation_result.content}")
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# Run the file_search tool
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# Create a project client
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project_client = AIProjectClient(
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endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
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credential=credential,
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api_version="2025-05-15-preview",
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)
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async with project_client:
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# Create a vector store
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openai_client = await project_client.get_openai_client()
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vector_store = await openai_client.vector_stores.create(
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name="sample_vector_store",
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)
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vector_store_file = await openai_client.vector_stores.files.upload_and_poll(
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vector_store_id=vector_store.id,
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file=open(
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os.path.abspath(os.path.join(os.path.dirname(__file__), "./assets/product_info.md")),
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"rb",
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),
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)
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print(f"\n\nUploaded file, file ID: {vector_store_file.id} to vector store ID: {vector_store.id}")
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# Call the file_search tool
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file_search_result = await session.call_tool(
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name="file_search",
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arguments={"queries": ["What feature does Smart Eyewear offer?"]},
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meta={"vector_store_ids": [vector_store.id]},
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)
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print(f"\n\nFile Search Output: {file_search_result.content}")
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finally:
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await credential.close()
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if __name__ == "__main__":
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asyncio.run(main())

‎sdk/ai/azure-ai-projects/samples/agents/sample_memory_advanced.py‎

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# # ------------------------------------
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# # Copyright (c) Microsoft Corporation.
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# # Licensed under the MIT License.
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# # ------------------------------------
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# """
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# DESCRIPTION:
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# This sample demonstrates how to interact with the memory store to add and retrieve memory.
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# USAGE:
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# python sample_memory_advanced.py
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# Before running the sample:
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# pip install python-dotenv azure-identity azure-ai-projects>=2.0.0b1
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# Deploy a chat model (e.g. gpt-4.1) and an embedding model (e.g. text-embedding-3-small).
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# Once you have deployed models, set the deployment name in the variables below.
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# Set these environment variables with your own values:
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# 1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
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# page of your Azure AI Foundry portal.
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# 2) AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME - The deployment name of the chat model, as found under the "Name" column in
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# the "Models + endpoints" tab in your Azure AI Foundry project.
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# 3) AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME - The deployment name of the embedding model, as found under the
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# "Name" column in the "Models + endpoints" tab in your Azure AI Foundry project.
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# """
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# import os
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# from dotenv import load_dotenv
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# from azure.identity import DefaultAzureCredential
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# from azure.ai.projects import AIProjectClient
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# from azure.ai.projects.models import (
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# MemoryStoreDefaultDefinition,
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# MemoryStoreDefaultOptions,
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# MemorySearchOptions,
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# ResponsesUserMessageItemParam,
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# ResponsesAssistantMessageItemParam,
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# MemorySearchTool,
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# PromptAgentDefinition,
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# )
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# load_dotenv()
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# project_client = AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=DefaultAzureCredential())
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# with project_client:
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# # Create memory store with advanced options
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# options = MemoryStoreDefaultOptions(
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# user_profile_enabled=True,
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# user_profile_details="Preferences and interests relevant to coffee expert agent",
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# chat_summary_enabled=True,
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# )
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# definition = MemoryStoreDefaultDefinition(
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# chat_model=os.environ["AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME"],
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# embedding_model=os.environ["AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME"],
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# options=options,
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# )
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# memory_store = project_client.memory_stores.create(
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# name="my_memory_store_3",
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# description="Example memory store for conversations",
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# definition=definition,
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# )
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# print(f"Created memory store: {memory_store.name} ({memory_store.id}): {memory_store.description}")
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# # Set scope to associate the memories with.
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# # You can also use "{{$userId}}"" to take the oid of the request authentication header.
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# scope = "user_123"
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# # Extract memories from messages and add them to the memory store
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# user_message = ResponsesUserMessageItemParam(
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# content="I prefer dark roast coffee and usually drink it in the morning"
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# )
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# update_poller = project_client.memory_stores.begin_update_memories(
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# name=memory_store.name,
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# scope=scope,
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# items=[user_message], # Pass conversation items that you want to add to memory
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# # update_delay=300 # Keep default inactivity delay before starting update
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# )
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# print(f"Scheduled memory update operation (Update ID: {update_poller.update_id}, Status: {update_poller.status()})")
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# # Extend the previous update with another update and more messages
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# new_message = ResponsesUserMessageItemParam(content="I also like cappuccinos in the afternoon")
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# new_update_poller = project_client.memory_stores.begin_update_memories(
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# name=memory_store.name,
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# scope=scope,
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# items=[new_message],
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# previous_update_id=update_poller.update_id, # Extend from previous update ID
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# update_delay=0, # Trigger update immediately without waiting for inactivity
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# )
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# print(
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# f"Scheduled memory update operation (Update ID: {new_update_poller.update_id}, Status: {new_update_poller.status()})"
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# )
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# # As first update has not started yet, the new update will cancel the first update and cover both sets of messages
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# print(
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# f"Superseded first memory update operation (Update ID: {update_poller.update_id}, Status: {update_poller.status()})"
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# )
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# new_update_result = new_update_poller.result()
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# print(
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# f"Second update {new_update_poller.update_id} completed with {len(new_update_result.memory_operations)} memory operations"
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# )
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# for operation in new_update_result.memory_operations:
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# print(
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# f" - Operation: {operation.kind}, Memory ID: {operation.memory_item.memory_id}, Content: {operation.memory_item.content}"
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# )
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# # Retrieve memories from the memory store
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# query_message = ResponsesUserMessageItemParam(content="What are my morning coffee preferences?")
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# search_response = project_client.memory_stores.search_memories(
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# name=memory_store.name, scope=scope, items=[query_message], options=MemorySearchOptions(max_memories=5)
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# )
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# print(f"Found {len(search_response.memories)} memories")
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# for memory in search_response.memories:
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# print(f" - Memory ID: {memory.memory_item.memory_id}, Content: {memory.memory_item.content}")
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# # Perform another search using the previous search as context
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# agent_message = ResponsesAssistantMessageItemParam(
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# content="You previously indicated a preference for dark roast coffee in the morning."
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# )
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# followup_query = ResponsesUserMessageItemParam(
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# content="What about afternoon?" # Follow-up assuming context from previous messages
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# )
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# followup_search_response = project_client.memory_stores.search_memories(
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# name=memory_store.name,
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# scope=scope,
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# items=[agent_message, followup_query],
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# previous_search_id=search_response.search_id,
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# options=MemorySearchOptions(max_memories=5),
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# )
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# print(f"Found {len(followup_search_response.memories)} memories")
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# for memory in followup_search_response.memories:
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# print(f" - Memory ID: {memory.memory_item.memory_id}, Content: {memory.memory_item.content}")
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# # Delete memories for the current scope
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# delete_scope_response = project_client.memory_stores.delete_scope(name=memory_store.name, scope=scope)
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# print(f"Deleted memories for scope '{scope}': {delete_scope_response.deleted}")
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# # Delete memory store
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# delete_response = project_client.memory_stores.delete(memory_store.name)
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# print(f"Deleted: {delete_response.deleted}")

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